pa ge 1 pa ge 41 american journal of applied statistics and economics (ajase) econometric modelling of macroeconomic interdependencies and the impact on nigeria’s economic growth amidst the covid-19 pandemic ajibode i. a.1, ogunnusi o. n.1* volume 2 issue 1, year 2023 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v2i1.1792 https://journals.e-palli.com/home/index.php/ajase article information abstract received: august 15, 2023 accepted: september 20, 2023 published: september 22, 2023 this paper dwelt on the investigation of the impact of covid-19 on economic growth of nigeria with reference to crude oil price, crude oil export and naira/dollar exchange rate as macroeconomic indicators. the pre and during covid-19 periods were represented by dummy variables (0, 1). six years of monthly data ranging between 2016 and 2021 were obtained from the cbn and nbs bulletin. the ardl model was calculated using the e-view programme. the findings revealed a long-term cointegration between the variables under consideration. there was a significant association established in the short term between the lagged dependent variable, exchange rate, and dummy variable at a 1% level of significance. however, the crude oil price had no significant impact on the model over the study period. according to these findings, the government should diversify its focus by investing more in non-traditional sectors, particularly the service and agricultural sectors. over-reliance on crude oil exports should be decreased, as the pandemic has highlighted the importance of being prepared for unexpected events and having a resilient economy. nigeria can reduce its vulnerability to external shocks and boost economic growth by researching and developing in other areas. the epidemic acts as a wake-up call to prioritise diversification and resilience in the face of future uncertainties. keywords gdp, covid-19, pandemic, ardl, crude oil 1 department of mathematics & statistics, federal polytechnic, ilaro, nigeria * corresponding author’s e-mail: ogunnusioluwatobi@gmail.com introduction an unprecedented global crisis brought about by the covid-19 epidemic has disrupted markets all across the world and made it difficult for nations to travel through unfamiliar terrain. one of the biggest economies in africa, nigeria, has not been exempt from the pandemic’s widespread effects. with major effects on numerous industries and macroeconomic indices, the virus has put a tremendous amount of strain on nigeria’s economy. nigeria’s economic growth trajectory has been negatively impacted by the covid-19 pandemic, which has reduced gdp and disrupted several important industries. nigeria’s real gdp decreased by 1.8% in 2020, according to the world bank, demonstrating the heavy toll the pandemic had on economic activity (world bank, 2021). lockdown measures, travel restrictions, and interruptions in global supply chains brought on by the pandemic have hindered manufacturing, decreased consumer demand, and decreased economic activity (sariakin et al., 2023). nigeria’s economy, which is heavily dependent on oil, has been particularly affected. nigeria, a country that exports oil, has had to contend with both falling oil prices and a decline in global demand. according to the international monetary fund (imf), the decrease in oil prices caused nigeria’s oil sector to experience a significant contraction, which exacerbated the country’s economic crisis (imf, 2021). government finances are under pressure due to the downturn in oil revenue, which has an effect on budgetary allocations and the execution of important development projects. in nigeria, the pandemic has also had an impact on jobs and standard of living. according to the international labour organisation (ilo), unemployment rates in nigeria increased significantly, with an alarmingly high number of people without jobs (ilo, 2021). the pandemic has severely affected the informal sector, which makes up a sizeable percentage of nigeria’s economy, resulting in widespread job losses and decreased incomes for millions of nigerians. the pandemic has also increased nigeria’s inflationary pressures. rising production costs, broken supply chains, and currency devaluation have all contributed to higher prices for products and services. inflation increased, above the target range of 6-9%, according to the central bank of nigeria (cbn) (cbn, 2021). high inflation reduces purchasing power and has a detrimental impact on living standards, especially for society’s most disadvantaged groups. the government implemented a number of measures to lessen the burden on the economy in response to the economic difficulties brought on by the pandemic. these consist of monetary policy adjustments, fiscal stimulus plans, and specialised aid for vulnerable groups. the impact of these actions in fostering economic recovery and reestablishing sustainable growth is still being researched. the covid-19 crisis has had a considerable influence on the nigerian economy, causing gdp to decrease, disruptions in numerous sectors, and challenges in macroeconomic statistics. several existing literature studies have analysed nigeria’s economic growth in the midst of the covid-19 pandemic and contributed significantly to our understanding of the subject. bala and owolabi (2021) use a vector autoregression (var) model to investigate the economic impact of pa ge 42 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 41-47, 2023 the pandemic in nigeria. their findings show that there are considerable negative effects on economic growth, emphasising the importance of effective policy responses to offset the consequences. they also emphasise the significance of tackling the issues that important sectors such as agriculture, industry, and services are facing in order to encourage recovery. jibrin and mubaraq (2021) also look into the link between the pandemic, oil price shocks, and macroeconomic indicators. they observe negative effects on economic growth, inflation, and currency rates using an autoregressive distributed lag (ardl) model. the study emphasises the significance of economic diversification in increasing resilience to external shocks. they also emphasise the importance of fiscal and monetary policy cooperation in order to stabilise the economy and alleviate the negative consequences of the crisis. in addition, nasiru and muazu (2021) use a vector error correction model (vecm) to assess the economic impact of the pandemic. their study emphasises the negative consequences on gdp growth, unemployment, and inflation, emphasising the importance of focused policy actions to foster recovery and stability. they also emphasise the importance of structural changes, vital sector investment, and social protection measures in promoting equitable and sustainable growth. similarly, mohammed and asongu (2021) uses a generalised method of moments (gmm) estimator to investigate how the pandemic and oil price shocks affect nigeria’s economic performance. their research uncovers important implications on gdp growth, inflation, and fiscal performance, emphasising the significance of long-term economic diversity for resilience. they further advocate for policies that foster economic diversification, improve governance, and promote technological advancement to mitigate the vulnerabilities exposed by the crisis. tella, oyewole, and adeyemi (2020) utilise an aggregated structural model to estimate the macroeconomic impact of the pandemic. their research demonstrates that the effects on economic growth, employment, fiscal policy, and monetary policy are all negative. the report emphasises the importance of focused policy initiatives and policies to support economic recovery and resilience. they also emphasise on the importance of investing in infrastructure, human capital, and digital technologies in order to boost nigeria’s competitiveness and assure longterm growth. despite scholars’ excellent efforts and contributions, a full assessment of the effects of crude oil price, export, and currency rate on gdp before and after the covid-19 regime is still lacking. while multiple studies have looked into various facets of nigeria’s economic growth in the midst of the epidemic, the specific influence of these factors on gdp dynamics has to be investigated further. as a result, the goal of this study. methodology this study employed a quantitative research design to effectively address its objectives. using time series data, the analysis covered a six-year period (january 2016 to october 2021). because of the mixed order of integration i(1) and i(0) seen in the unit root test for both endogenous and exogenous variables, the autoregressive distributed lag (ardl) model was adopted. data were gathered from the central bank of nigeria’s (cbn) 2021 statistics bulletin and the national bureau of statistics (nbs). the variables studied were gdp, crude oil production, crude oil exports, and the exchange rate. this is stated as follows: ∆at= c0+c1a(t-i)+∑p (i=1)ci∆a(t-i)+ki (1) the optimum lag was selected with the lowest sbic (schwartz bayesian information criterion). estimation technique equation 1 can be rewritten to describe an ardl equation for determining whether the variables have a long run relationship. the relationship can be expressed as: δln gdpt= γ0+ ∑p (i=1)γ1 δln (gdp)(t-i)+ ∑p (i=0) γ2 δln (cop) (t-i)+ ∑p (i=0) γ3 δln (crdexpt)(t-i)+∑p (i=0) γ4 δln(exrt) (t-i) +∑p (i=0) γ5 δln(dumy)(t-i)+ kt (2) where gdp = gross domestic product cop = crude oil price; crdexpt = crude oil export; exrt = exchange rate dumy = (0 for before covid 19, 1 for during covid 19) γ0,γ1,γ2,γ3,γ4 and γ5 are coefficient to be evaluated. kt random error term (kt~iid(0,σ2 )) to affirm the existence of a long-run relationship, we test the hypothesis: h0: ηim= . . .=ηim (for i=1, 2, 3, 4) the rejection of h0 indicates integration, ln gdpt= γ0+∑p (i=0)γ1iln(gdp)(t-i)+∑p (i=0)α1iln(cop)(t-i)+ ∑p (i=0) θ1i ln(crdexpt)(t-i) +∑p (i=0) θ1i ln(exrt)(t-i) +∑p (i=0) ϑ1i (dumy)(t-i)+ kt (3) ln gdpt=γ0+∑p (i=0) γ2i ln(gdp)(t-i)+∑p (i=0) α2i ln(cop)(t-i) +∑p (i=0) θ2i ln(crdexpt)(t-i) +∑p (i=0) θ2i ln(exrt)(t-i) +∑p (i=0) ϑ2i (dumy)(t-i) +μt (4) equation (3) represents the coefficients in the long run at ‘m’ optimal lag with gdp as a response variable. in addition, (4) represents the ardl short run specification, in which error correction model (ecm) can be derived from (rasheed, 2023). equation (5), on the other hand, indicates the ecm’s speed of recovery from deviation. ecm(t-1)= ln gdpt γ1-∑ p (i=0) γ1i ln (gdp)(t-i) -∑p (i=0) α1i δln (cop)(t-i) -∑p (i=0) θ1i δln(crdexpt)(t-i) -∑p (i=0) θ1i δln(exrt)(t-i) -∑ p (i=0) ϑ1i δ(dumy)(t-i) (5) coefficient of determination (r2) was utilised to confirm how good the fitted model is. for the sake of robustness, this describes how well the line describe the data. this can be expressed as: r2=1-( ssr)/sst (6) ssr represents square regression and sst represents sum of square total. pa ge 43 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 41-47, 2023 results table 1 examined the joint description of measurement variables. the test results show that the exchange rate and gdp have non-normal distribution patterns over time, with a significant p-value of 0.05. the minimum and maximum values of the predictors as well as their variance were also recorded throughout time. table 1: descriptive statistics cop crdexpt exrt gdp mean 56.65 1.41 268.39 9033461 median 59.10 1.48 199.8 6347146 maximum 79.59 2.00 396.00 14521335 minimum 14.28 0.97 169.68 5265989 std. dev. 13.72 0.20 81.56 3577591 skewness -0.58 0.18 0.34 0.21 kurtosis 3.01 3.20 1.40 1.24 jarque-bera 3.89 0.52 8.62 9.39 probability 0.14 0.76 0.01 0.009 source: researchers’ computations, 2022 figure 1: time plot of gdp figure 2: time plot of crdexrt figure 3: time plot of cop pa ge 44 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 41-47, 2023 table 2 shows the variables’ integration qualities, labelled as i(1) and i(0), signifying mixed orders of integration of 1 and 0, respectively. the adf test findings for cop indicate that the variable is integrated at the zero-order. this implies that the selected macroeconomic indicators are appropriate in modeling nigeria economic growth via the ardl methodology as there exist mix order of integration. confirmatory analysis of the pp test in table 3 also indicated that exrt is of i(0) as compared to the adf test. this confirms the robustness of the pp methodology in testing for stationarity of variables for model identification. the aic was used to propose the largest lag structure for endogenous and exogenous variables to lag order 1, as indicated in table 4. table 2: adf stationarity test results variables levels critical values @5% 1st diff. critical values @ 5% remark cop -3.0221 -2.9055 (0.0379)* i(0) crdexpt -2.8219 -2.9048 (0.0605) -9.6041 -2.9055 (0.000)** i(1) exrt -0.5555 -2.94848 (0.8728)* -9.4070 -2.9055 (0.000)** i(1) gdp -0.8443 -2.9048 (0.7997) -8.2507 -2.9055 (0.000)** i(1) note: values represented by *, ** signifies level of significance @ 5% and 1% respectively source: researchers’ computations, 2022 table 3: philips perron (pp) unit root results variables levels critical values @5% 1st diff. critical values @ 5% remark cop -3.1421 -2.7175 (0.0289)* i(0) crdexpt -2.9829 -2.3048 (0.0615) -8.2135 -2.9165 (0.000)** i(1) exrt -3.7143 -5.9218 (0.0234)* i(0) gdp -0.5555 -2.9446 (0.8385) -9.4482 -2.9385 (0.000)** i(1) note: values represented by *, ** signifies level of significance @ 5% and 1% respectively source: researchers’ computations, 2022 table 4: lag selection criteria lag logl lr fpe aic sc hq 0 75.8006 na 1.20e-06 -2.2793 -2.1433 -2.2258 1 262.47 343.72* 5.34e-09* -7.6976* -7.0172* -7.4300* * implies lag order chosen by several criterion source: researchers’ computations, 2022 table 5: result for dynamic regressors (ardl) variables coefficient std. error t-value pr(>|t|) ln (gdp)(t-1) 0.7734 0.0704 10.9768 0.000 ln(cop)t 0.0408 0.0242 1.68490 0.100 ln(crdexpt)t -0.0355 0.0460 -0.7713 0.444 ln(exrt)t 0.8907 0.0918 9.7015 0.000 ln(exrt)(t-1) -0.4912 0.1184 -4.1460 0.000 dummy (0,1) -0.0736 0.0263 -2.7920 0.007 constant 1.2693 0.6019 2.1086 0.039 r2 = 0.985; adj. r2 = 0.984; mse = 0.051; rmse = 0.225; aic = -3.0263; f-value = 697.510, (sig-value = 0.000) source: researchers’ computations, 2022 hence; δ l n ( g d p ) = 1 . 2 6 9 3 0 . 7 7 3 4 l n ( g d p ) ( t 1 ) + 0.0408ln(cop)0.0355ln(crdexpt) + 0.8907 ln(exrt)t0.4912 ln(exrt)(t-1)0.0736dumy (7) according to the r-squared value of 0.985, the combined effects of crude oil price, exchange rate, and export account for roughly 98.5% of the variation in economic development throughout the examined years. the updated r-squared value of 0.984 implies that new variables will continue to account for around 98.4% of the variation in economic growth. with a p-value less than 0.05, the f-statistic of 697.51 implies that model is adequate enough in predicting the dynamic impact of economic growth with reference to the selected macroeconomic indicators. pa ge 45 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 41-47, 2023 furthermore, the study finds that a 1% increase in gdp in the following year equates to a large 77.3% increase in present economic growth. furthermore, both the crude oil price (cop) and exrt contribute to a rise in economic growth of 4.1% and 89.1%, respectively. assuming all other variables remain constant, a 1% increase in exports (expt) and a one-period lag in the exchange rate result in a 3.6% and 49.1% drop in economic growth, respectively. the study also discovers that lagged variables have a major influence on nigeria’s economic growth over this time span. the research demonstrates that the variables crude oil price (cop) and export (expt) make insignificant contributions to the model, implying that they have a limited impact on economic growth. the study, however, emphasises the significance of lag 1 of the exchange rate (exrt) as a statistically significant variable, demonstrating its impact on economic growth the following year. furthermore, the analysis shows that the exchange rate at lag 0 contributes considerably to economic growth throughout the study period, as indicated by a p-value less than 0.05. a detailed analysis using dummy variables demonstrates a considerable negative influence on economic growth between the pre-covid-19 period (represented by 0) and the post-covid-19 period (represented by 1). this effect is significant, accounting for 7.4% of the observed impact, and the related p-value is less than the usually accepted statistical significance threshold of 0.05. these data highlight the considerable impact of covid-19 on the country’s economic collapse, which may be linked to a number of variables, including a combination of low crude oil prices and a high naira/dollar exchange rate, as well as lower export levels. according to the supplied critical bounds values, the estimated f-statistic (3.9687) for the cointegration limits test exceeds the upper bound critical values of 2.79, 3.67, 2.37, and 3.20 for significance levels of p = 0.01, 0.05, and 0.10. as a result, the null hypothesis of no cointegration is rejected, and it is conceivable to conclude that there is a long-term cointegrating link. according to the findings, nigeria’s non-oil export statistics demonstrate a long-term relationship between the identified exogenous variables and economic growth in both pre-covid-19 and covid-19 era. in selecting an ardl (1, 0, 0, 1) model, the akaike information criterion (aic) was utilised. table 6: error correction regression model response variable = δlngdp var. coef. se t-stat. prob. c 1.2693 0.6019 2.1086 0.039 ln(gdp(-1)) -0.2265 0.0704 -3.2144 0.002 ln(cop) 0.0408 0.0242 1.6849 0.097 ln(crdexpt) -0.0355 0.0460 -0.7713 0.443 ln(exrt(-1)) 0.3995 0.1036 3.8537 0.000 ln(exrt) 0.8907 0.0918 9.7015 0.000 dumy -0.0736 0.0263 -2.7920 0.007 ecm(t-1) -0.2265 0.0492 -4.5983 0.000 r2 = 0.618 ; adj. r2 = 0.606; dw stat = 1.9001 source: researchers’ computations, 2022 the ardl co-integration analysis, at a significance level of 5%, reveals a substantial long-term influence between current economic growth and economic growth in the previous year. however, impact of crude oil price on economic growth is statistically insignificant (p-value > 0.05), with a 4.1% incremental change in economic growth per percentage shift in crude oil price. in contrast, export shows a long run negative contribution to economic growth, insignificant at the 5% level (p-value 0.4435 > 0.05), while import contributes positively at 3.6%. exchange rate at lags 0 and 1 also exhibit longterm co-integration effects on economic growth, with incremental rates of 89.7% and 40%, respectively. this suggests that changes in exchange rate before and during the covid-19 period will result in approximately 90% increase in the unemployment rate in the current year and a 40% rate of increment of economic growth in the previous year. the coefficient of current domestic output displays elasticity in response to economic growth. furthermore, the long-term effect of the pre and post covid-19 era dummy variable indicates a negative multiplier effect of 7.4% on nigerian economic growth. the statistically significant (p-value 0.000 = 0.05 level of significance) -0.2265 ecm(t-1) coefficient demonstrates the presence of a long-term equilibrium among economic factors. the error correction model (ecm) system’s coefficients provide insight into the short-term adjustment process towards long-run equilibrium. with a significant value of -0.226509, the ecm shows that approximately 22.7% of the disparity in the determinants of economic growth from the previous era has been addressed in the present period to restore equilibrium. as a result, returning to the equilibrium condition would take around one calendar year. pa ge 46 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 41-47, 2023 table 7 summarises the results of the portmanteau tests performed on the ardl model residuals. these tests show that the residuals are free of heteroskedasticity and serial correlation. furthermore, the adjusted r-squared of the shortrun ardl model, which is around 61.8%, shows that the model effectively passes the autocorrelation and heteroscedasticity diagnostic tests. the ar inverse roots of the characteristic polynomial were examined to determine stability and stationarity. it indicated that the root modulus is smaller than one and lies within the unit circle. this verifies the ardl (1, 0, 0, 1) model stability and invertibility. the cusum test was used on the model residuals to ensure its stability. the test results showed that the cusum test statistic is still within the critical parameters of the 5% level. this implies that the model estimated parameters are consistent throughout the study period. table 7: screening test chi-squareheteroskedasticity(1) = 1.2352 [0.2969] chi-squareserial correlation(2) = 0.0485 [0.9527] p-values is quoted in [ ] source: researchers’ computations, 2022 figure 4: inverse root of the selected ardl figure 5: cumulative sum of recursive residuals plot for coefficients of ardl model conclusion from the findings of study’s predefined objectives, it has been discovered that the selected macroeconomic factors have significant links with nigeria’s economic growth. the exchange rate is a significant and influential component that plays an important impact in economic growth. it is useful in understanding differences in economic growth by using the current rate as a factor. furthermore, examining previous gdp growth rates is critical for gaining a better understanding relationship existing between exchange rate and nigeria economic growth. while crude oil prices had no substantial impact on economic development, they did show a positive trend throughout the study period, particularly during the difficult period of the covid-19 pandemic. this suggests that, despite the lack of statistical significance, the crude oil price had a favourable affect throughout the time period studied. in assessing the consequence of the pandemic on economic growth, the introduction of a dummy variable to distinguish between the pre-covid-19 and covid-19 eras generated statistically significant results. the dummy variable’s negative contribution suggests that the covid-19 epidemic has considerably led to a major fall in nigeria’s gdp, resulting in an economic downturn and sufferings for the populace. this can be linked to the country’s high reliance on imports as opposed to exports, as the exportation variable did not contribute much to economic growth during this time period. it is advised that the government dedicate additional resources to sectors such as services and agriculture, diversifying the economy beyond crude oil exportation, to reduce the negative effects and support economic recovery. furthermore, encouraging consumer expenditure on goods and services, particularly in areas such as home development, construction, and allied pa ge 47 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 41-47, 2023 industries, can help to gdp growth and, ultimately, strengthen the naira in the global market. by implementing these steps, nigeria will be able to gradually overcome the pandemic’s obstacles, drive economic growth, and strengthen its position in the international economic scene. references bala, y., & owolabi, f. (2021). impact of covid-19 pandemic on nigeria’s economy: a vector autoregression (var) approach. journal of economics and sustainable development, 12(6), 86-97. central bank of nigeria. (2021). annual statistical bulletin. retrieved retrieved from www.worldbank. org 19th november, 2022. international labour organization. (2021). impact of covid-19 on the nigerian labor market. retrieved from www.ilo.org 19th november, 2022. international monetary fund. (2021). nigeria: staff report for the 2021 article iv consultation. retrieved from www.ilo.org 19th november, 2022. jibrin, i., & mubaraq, i. (2021). covid-19 pandemic, oil price shocks, and macroeconomic indicators in nigeria: an autoregressive distributed lag (ardl) approach. international journal of economics, commerce, and management, 9(6), 86-97. mohammed, y., & asongu, s. a. (2021). covid-19, oil prices, and economic growth in nigeria: insights from generalized method of moments (gmm) estimator. african development review, 33(s1), s93-s106. nasiru, i., & muazu, i. (2021). the economic impact of covid-19 on nigeria: an empirical analysis. international journal of business, economics and management, 8(3), 100-109. rasheed, o. n. (2023). the effect of unemployment on economic growth in nigeria. american journal of applied statistics and economics.1(1) 11-14, 2023 sariakin, fitria, n., faiza, c., amiruddin, & usman, m. b. (2023). e-learning in the educational system post covid-19 pandemic: a review of the obstacles and opportunities to curriculum designers. american journal of multidisciplinary research and innovation, 2(5), 40–46. https://doi.org/10.54536/ajmri.v2i5.1497 tella, s. a., oyewole, s. o., & adeyemi, s. l. (2020). macroeconomic impact of covid-19 pandemic: evidence from nigeria. research in international business and finance, 54, 101419. world bank. (2021). nigeria: country overview. retrieved from www.worldbank.org 19th june, 2023. pa ge 1 pa ge 11 9 american journal of applied statistics and economics (ajase) research on the development of green energy ecological economy industry kailun gu1, jiaxing li1*, yulin qin2 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2864 https://journals.e-palli.com/home/index.php/ajase article information abstract received: may 06, 2024 accepted: june 04, 2024 published: june 08, 2024 since modern times, with the rapid development of the economy and the improvement of people’s living standards, people have gradually begun to pay attention to green environmental protection, attach importance to the development of green economy, and at the same time, the development of green economy has also put forward the adjustment and optimization of industrial structure. today’s green economy has become the trend of the times, and it is an economic development path to realize the harmony between man and nature. continuously strive to promote the green and low-carbon transformation of energy, it will provide important support and reference for the development of renewable energy in the republic. based on the analysis of the literature and the data of the national bureau of statistics, this paper puts forward the problems existing in today’s green ecological economy, and gives relevant reasonable suggestions for these problems, hoping to provide relevant references for energy economy enterprises, so as to promote complementary advantages and promote the development of national energy transformation. in order to promote the economic value and economic development significance of green economy in ecological and environmental protection, so as to realize the prospect of green economic development from the perspective of ecological and environmental protection, and the effective path of today is discussed and summarized, in order to help the development of china’s green economy. keywords green, economy renewable, green and low-carbon 1 belarusian state university business school, master candidate, minsk, belaru 2 sichuan hope automtie vocational college, ziyang city, sichuan province, china * corresponding author’s e-mail: yulinqin51@gmail.com introduction the development and research of green energy is the general trend of social development. with the global emphasis on environmental protection, the development and research of green energy gradually become the top priority of global development (gao teng, 2022). green energy is pollution-free, low-cost, and has no side effects, and as a new green and environmentally friendly sustainable energy form that can replace traditional fossil fuels, green energy will become a major component of the future energy structure. therefore, we should promote the development and use of green energy (wei hongxiang, 2022). in recent years, people have gradually realized that energy is an important factor that promotes and restricts the country’s economic development. the world is facing problems such as energy shortage, continuous deterioration of the ecological environment, and climate change, and the development of renewable energy has become an important direction to solve future global development problems (jin xiaoxuan, 2022). as a responsible major country, china attaches great importance to promoting its own and other countries’ joint efforts to achieve green and low-carbon energy transformation and development (zhang xuesheng, 2022). in particular, in 2021, president xi jinping made it clear in his important speech at the general debate of the 76th session of the united nations general assembly that “china will strive to peak carbon emissions before 2030 and achieve carbon neutrality before 2060” and “china will vigorously support the green and low-carbon development of energy in developing countries, and will not build new overseas coal-fired power projects” (huang qili, 2018). under the framework of the belt and road initiative and the paris agreement, china should not only optimize its energy structure and leap to the high-quality development of renewable energy, but also support and promote the green and low-carbon energy transformation and development of the countries in the belt and road initiative, and make unremitting efforts to achieve the goals of the paris agreement (lv jiangtao, 2022). global renewable energy development overall, on the investment side, renewable energy has become the most important area of energy investment, with china’s renewable energy investment ranking first in the world, and global fossil energy still dominates the development status and trend of renewable energy, but the growth momentum of renewable energy utilization is strong (long houyin, 2018). literature review the current situation of china’s green energy economic development renewable energy industry in recent years, with the discovery and research of renewable energy, its green environmental protection value has gradually become a hot spot of concern in countries around the world, and has begun to be valued in economic development and maintenance of the ecological environment, and with the increasing proportion of the energy industry in the social market, as shown in figure 1, in 2018, china’s renewable market energy reached 1.89 pa ge 12 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 119-123, 2024 billion us dollars, and the scale reached in 2019 2.09 billion us dollars, with a scale of 2.33 billion yuan in 2020, 2.74 billion yuan in 2021, and 3.37 billion yuan in 2022. china vigorously develops renewable energy, with energy conservation, pollution reduction, and environmental improvement as the core, and in the future development, china will pay more attention to the research and development and implementation of renewable energy technologies. in 2017, general secretary xi jinping put forward the strategic idea of “four energy revolutions and one international cooperation”, and renewable energy has become the core of the energy transition. renewable energy resources are abundant, environmentally friendly and clean, and the development of renewable energy is conducive to the protection of the natural environment and the sustainable development of social economy. china has abundant reserves of energy resources such as wind, hydro, solar, marine, and solar energy. as of the end of june 2023, the country’s installed hydropower capacity was 418 million kilowatts, biomass power generation capacity was 43 million kilowatts, solar power generation capacity was 471 million kilowatts, wind power installed capacity was 390 million kilowatts, and the total installed capacity of renewable energy power generation exceeded 1.3 billion kilowatts, reaching 1.322 billion kilowatts, a year-on-year increase of 18.2%, accounting for about 48.8% of china’s total installed capacity. from january to june, china’s new installed capacity of renewable energy power generation was 109 million kilowatts (accounting for 77% of the country’s new installed capacity). among them, 5.36 million kilowatts of hydropower, 22.99 million kilowatts of wind power, 78.42 million kilowatts of solar power generation, and 1.76 million kilowatts of biomass power generation were added. the country’s renewable energy generation capacity reached 1.34 trillion kilowatts. figure 1: energy analysis of china’s renewable market source: [https://www.stats.gov.cn/] figure 2: comparison of the renewable energy industry in china and the rest of the country source: [https://www.stats.gov.cn/] sustainable energy industry a sustainable energy industry is a development model that is based on meeting the energy needs of the present without compromising the energy needs of future generations. under the current situation of rapid growth of global energy demand and the gradual depletion of non-renewable energy reserves, the sustainable development of the energy industry has become an important direction to lead the development of the energy industry. in terms of the development of the sustainable energy industry, the first is the rapid development of renewable energy. renewable energy sources such as solar, wind, and hydro have gradually become mainstream forms of energy. in particular, the pa ge 12 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 119-123, 2024 solar energy industry has made great breakthroughs in recent years, and the scale of the photovoltaic power generation market continues to expand. at the same time, wind energy has also become an important form of renewable energy, and wind farms have been built in various places, so that wind power generation capacity has been continuously enhanced. second, significant progress has been made in energy storage technology. renewable energy is characterized by unstable power generation capacity, and the development of storage technology has become the key to solving this problem. at present, the continuous breakthrough of battery technology has continuously reduced the cost of energy storage, and the promotion of electric vehicles has also provided a huge market demand for the development of energy storage technology. the application of smart energy systems is also gradually becoming popular. the intelligent energy system uses artificial intelligence, big data and other technologies to achieve efficient management of energy and improve energy efficiency. the construction and application of smart grids continue to advance, with the help of the internet of things and cloud computing technology, real-time monitoring and regulation of energy have been realized. however, there are still some challenges to the sustainable energy industry. the first is the dilemma of technological innovation. although significant progress has been made in the development of renewable energy, there are still problems of inefficiency and high cost. there is a need to continue technology research and development to improve the efficiency of renewable energy use and reduce costs. although the sustainable energy industry faces some challenges, with the continuous breakthrough of renewable energy technology and the support of government policies, the sustainable energy industry is expected to achieve faster and healthier development in the future. at present, the world is facing great climate problems, such as the rise of the earth’s temperature, the warming of air temperatures, the melting of permafrost, the rise of sea levels, the destruction of the ozone layer, the imbalance of natural ecosystems, and the sharp decline of biological species. in this environment, clean recycling, waste utilization is particularly important, there have been many clean recycling energy industry, and the global market size continues to expand, clean recycling energy industry has become a hot spot for global energy investment, attracting a large number of talents and a large amount of capital, clean recycling energy gradually development and utilization, is conducive to further improving the ecological environment, improve the quality of life of all classes, social benefits gradually emerged. at present, there are 42 million biogas users in the country, and the annual output of biogas in the country is more than 15.8 billion cubic meters, replacing more than 25 million tons of coal, reducing carbon dioxide emissions by more than 60 million tons, and greatly improving the environmental situation and rural quality of life. figure 3: analysis of the use of biogas for sustainable development in chinasource: [https://www.stats.gov.cn/] materials and methods the development of china’s green energy economy issues related to government and policy china attaches great importance to and vigorously develops the green energy industry, the scale and influence of the clean energy industry are constantly expanding, the state has increased the supervision of environmental pollution and the elimination of enterprises with large energy consumption and serious environmental pollution, which can not only inhibit the development pa ge 12 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 119-123, 2024 of high energy consumption industries, but also increase efforts to support the development of green and clean energy industries with low energy consumption. with the attention of the national and local governments, china’s green energy industry has developed rapidly, and significant progress has been made in all aspects of clean energy legislation, industrial policy and government policy. however, there are still some deficiencies, because the green energy economy is a new issue raised in recent decades, and its understanding and research have certain limitations, so the direction and methods of supporting green energy need to be improved, and relevant policies and laws and regulations should also make new changes and improvements with the problem. when new problems arise, a lot of manpower, material and financial resources will be invested in studying and solving them. the initial research and development cost is high, which restricts the development of the green energy industry to a certain extent, so the state’s financial support is particularly important for the development of the new green energy industry. the government should strengthen its support for the development of the green energy industry and promote the sustainable development of green energy. the government has strengthened the supervision and implementation of policies to ensure that policies can be carried out normally and effectively. the issue of green energy and economic development of enterprises at present, green energy enterprises have been established not long ago, the social foundation is not solid, its ability to resist risks is not high, once it encounters some problems, it may fall into a great predicament, the enterprise economy is not stable enough, there is not enough economic support, some enterprises want to further study how to reduce production costs and other issues are more difficult. in the absence of research results, some banks are reluctant to help and do not give loans, which is likely to bankrupt the company. the second is that the quality of economic operation is not very high, and the investigation and research found that the profits of china’s green energy enterprises continue to grow. as shown in figure 4, xintian green energy companies will grow by 15.01% in 2022, but their profit growth still does not meet the expected standard. after the steady growth of profits from 2019 to 2020, in the context of the three-year epidemic, the load rate of china’s green energy enterprises continued to grow: in 2021, the epidemic led to an overall decline in profits, only 10.54%, and the epidemic will be completely controlled in 2023. the economy rose by 21.86%. at this stage, some banks in china have not set up lending institutions for new green energy enterprises, and have not improved the relevant loan policies, which leads to some green energy enterprises do not have sufficient financial support, no reasonable source of funds, which narrows the financing channels of enterprises. this situation may lead to the adoption of informal channels by a small number of smes, which may limit the development of enterprises. figure 4: economic profit of xintian green energy enterprises from 2019 to 2023 source: [https://www.stats.gov.cn/] pa ge 12 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 119-123, 2024 results and discussion suggestions on the development of china’s green energy economy policy-related recommendations at the government level the lack of policy support restricts the development of the sustainable energy industry. government departments should introduce more targeted and long-term policies to provide a better development environment and support for the sustainable development of the energy industry. further improve the policy system for green energy development, clarify the scope and methods of support and how to specifically support the development of enterprises. establish a unified market rules and regulations, introduce new policies to standardize the development behavior of enterprises, increase support for green energy, and give relevant subsidy policies, incentive policies, and incentive policies. formulate relevant tax reduction policies to reduce the development cost of green energy. the government should work with enterprises to actively promote the formulation and implementation of relevant international standards, further improve the international competitiveness of china’s green energy industry, and let china’s green energy enterprises have the confidence to go abroad and go to the world. suggestions on the development of products and economic development trends of enterprises determine the function of the enterprise, take the function as the center to develop the company’s product development plan, conduct social surveys, market research, and accurately understand the needs of users, the needs of the market, and then analyze the gap between the current market environment and the ideal market environment, what is the gap between the products provided by the enterprise and the ideal products in the minds of users, and obtain useful information such as which areas are still blank or which products are more different from the ideal products in the hearts of users. conduct a comprehensive analysis of each workshop of the enterprise, and weave a specific product development plan to deal with problems and customer service deficiencies. then try to reduce the cost as much as possible, should reduce the total cost of the product throughout the whole process of new product development, and coordinate the relationship between manufacturing cost and use cost, in the maximum degree of cost reduction, cost reduction is not to cut corners to reduce, but to use raw materials as much as possible to recycle, and then form a virtuous circle of new product development, as far as possible to reduce the generation of industrial waste. conclusion the market for green energy is vast in the future, but due to the lack of technology, the cost is high for the time being. the investment in the new energy industry is larger, but the time cycle of recovering the cost is relatively long, due to a series of factors such as high cost, limited technology and incomplete government policies, etc., the profitability of development enterprises has been in a low state, which has brought a great adverse impact on the development of green energy, and has a negative impact on the development of the industry. however, china’s territory is vast; green pollution-free energy is a huge wealth in china, so we should work hard to explore and develop. the state should vigorously support and encourage it. with the government as the leading and the enterprise as the main body, we will purposefully and selectively introduce advanced technology, technology and key equipment at home and abroad, increase cooperation with domestic and foreign scientific research institutes and well-known enterprises, and develop technology and equipment with international advanced level and independent intellectual property rights as soon as possible, so as to enhance industrial competitiveness. references hongxiang, w. (2022).industrial structure change and green economic development in ethnic areas. journal of nanning vocational and technical college, 23-25. houyin, l., lin, l., weidong, l. (2018). analysis of china’s primary energy development potential. journal of ningde normal university (natural science edition),11-13. jiangtao, l. (2022).renewable energy promotes china’s green development[j], china economic weekly,renewable energy promotes china’s green development. china economic weekly, 8-9. qili, h. (2018).enlightenment of china’s renewable energy development to the construction of global energy interconnection. global energy interconnection, 13-15. teng, g. (2022). analysis of the green economic development of yulin city from the perspective of marxist ecological concept, modern commerce and trade industry, 35-36 xiaoxuan, j. (2022). exploration of economic development issues from the perspective of green economy. green economy, 6-7. xiaoxuan, z. (2022).research on the impact of industrial agglomeration on green economic development from the perspective of technological innovation. journal of fujian normal university, 6-9. pa ge 1 pa ge 61 american journal of applied statistics and economics (ajase) mode innovation and discussion of enterprise financial management in the new economic background haojie wang1* volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2433 https://journals.e-palli.com/home/index.php/ajase article information abstract received: january 28, 2024 accepted: february 26, 2024 published: march 01, 2024 the development of the globalized economy has also made enterprise financial management work into a new stage of reform, the; transformation of the market economic system has also made the enterprise financial management work content and mode of change the traditional financial management model has failed to meet the needs of the current enterprise, and even hinder the development of enterprises. in the new economic background promoting financial management reform and innovation is not only the way of enterprise development. it is also in line with the trend of the times, where the article through the trend of economic development and in-depth analysis of the current financial management situation.by analyzing published literature and using the literature analysis method, we summarize the current problems in corporate financial management and propose corresponding solutions to the problems based on the key points of the problems, thereby providing reference and help for corporate development. keywords new economic context, business, financial management 1 belarusian state university, business school, master of financial management, minsk, belarus * corresponding author’s e-mail: 18884364612@163.com introduction the market economy and social situation in the context of the new era have undergone great changes, their core competitiveness, financial management plays a great role. it provides key data support for enterprises to designate scientific planning and save operating costs. under the background of the new economy, if enterprises want to be invincible in the fierce market competition, they must update their business concepts, innovate their business methods and adopt scientific management methods to promote the improvement of the management level of enterprises and the enhancement of the competitive advantages of enterprises at present, the financial management of some enterprises is still using the traditional management methods, which is difficult to promote the enterprises and at the same time, it is also, to a certain extent, restricting the healthy development of enterprises. at present, some enterprises are still using traditional management methods of financial management, it is difficult to promote enterprises, but also to a certain extent restricts the healthy development of enterprises. with the continuous progress of society and the deepening of enterprise reform, the financial management of enterprises will face unprecedented challenges and difficulties. therefore, enterprises must fully understand the importance of financial management innovation, and reform and innovation of the enterprise’s financial management work, prompting enterprises to improve the quality of financial management to enhance the competitive advantage. literature review connotation and characteristics of new economy enterprises connotations of new economy enterprises with the rapid development of the socialist market economy and science and technology, china’s economic development process is accelerating, gradually stepping into the era of the new economy. the continuous development of china’s economic innovation and diversification has made the connotation of “new economy” more and more rich, and it is generally believed that the new economy is characterized by new technologies, new industries and new modes of business (ren baoping and song xuechun, 2020), and in this context, a number of enterprises with “four new” characteristics, which are different from the traditional business model, have emerged. under this background, a group of enterprises with “four new features” different from the traditional enterprise business model have emerged, which are called new economy enterprises, and their development in the new environment is not only facing new opportunities but also facing many challenges. characteristics of new economy enterprises high human capital and high-tech capital input. innovation is the inexhaustible driving force for the development of new economy enterprises, which mainly relies on the input of production factors such as high human capital and high technology. new economy enterprises, such as those engaged in new technology-based products or new types of production and living services, need to employ a large number of technical and managerial talents, which requires more human capital investment, high-tech investment and a large amount of capital support for new economy enterprises (cui wei, 2020). flexible utilization of light assets. light assets are important resources for new economy enterprises to obtain profits, with intangible assets, including corporate culture, customer relations, management experience, governance system and other items, the enterprise capital consumption is low, but has the ability to create greater pa ge 62 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 61-66, 2024 value, which is one of the important characteristics of the new economy enterprises differentiated from traditional enterprises.the development path of the new enterprise economy is shown in figure 1. figure 1: development path of new economy for enterprises compliance with sustainable development strategies. traditional economic enterprises are easily affected by the economic environment, and their development is slowing down. however, the asset-light model of new economy enterprises enables them to have strong core competitiveness, and realize sustained and rapid growth with the support of national policies and changes in the economic market. in addition, new economy enterprises are in line with the market development, in the world are in urgent need of development of the industry, such as new energy industry, energy saving and environmental protection industry is more reflective of the contemporary theme of harmonious development of man and nature. characterization and importance of the new economic context web-based information technology applications are widespread at present, the sign of great changes in the social and economic environment is that network information technology is widely used in various fields, even people’s daily life travel and so on, can not be separated from the support of network information technology, such as cell phones and other communication tools, car navigation, etc., are supported by the technology to make its function perfect (chen xiaoming, 2019). network information technology brings convenience to people’s life, but also brings challenges to people’s work. therefore, it is necessary to face up to the development of science and technology and utilize it to better serve the social development concave. changing patterns of economic development the mode of economic development has changed dramatically, on the one hand, the addition of knowledgebased talent to create more possibilities for enterprise development, on the one hand, the development and application of science and technology for enterprise development has brought more opportunities. therefore, in order to seek development in today’s society, enterprises must learn how to use people, how to use modern science and technology products to promote the long-term development of enterprises. complex market environment under the background of the new economy, the economic market environment has become more and more complex, mainly due to the people’s horizons have become wider, with the development of modern science and technology, to create a more intense market environment. not only that, at this stage, enterprise development is facing a more severe market environment, the environment of market competition is becoming increasingly fierce, the risk of business operations is also greater, only the use of science and technology, and constantly seek management mode, business means of breakthroughs, in order to better protect the healthy and sustainable development of enterprises. the importance of innovation in financial management in the new economic era first of all, the development of the market will inevitably lead to the development of financial management, in the late 1980s, china’s financial system has begun its own development, china at that time appeared a number of software companies based on the service program is the financial management such as the golden disk, liu dan, etc. (liu dan, zheng xuefei, 2020), at that time, although compared with some foreign software has a more obvious gap, but at that time the development of china’s financial management was also much stronger than some countries in the asia-pacific region. in order to make the financial management of china’s enterprises in the current environment can be more rapid development, enterprise pa ge 63 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 61-66, 2024 management and financial accounting combined management is the most important. for example, we can project budget cost management personnel performance unified management planning, to better meet the current market development, enhance the management capacity of enterprises, and increase corporate profitability. secondly, the enterprise financial management innovation of the times is to carry out the development of information technology, especially in the group enterprise, a single traditional financial management, difficult to meet the development needs of enterprises, so they need a more systematic and advanced financial management, enterprise unified management planning. for example, in the group enterprise, group finance should form a line of income and expenditure, capital pool. in the group of enterprises should be set up within the financial system and direct contact with the bank, which are in the context of the current era, the development of financial management is an inevitable trend, but also the current needs of the times. this can improve the enterprise’s financial management efficiency, at the same time in terms of manpower to save manpower, reduce costs. the use of technical means to reduce or even avoid errors that may occur in financial calculations, greatly reducing the losses caused by miscalculation to the enterprise. materials and methods problems facing financial management in the new economic environment this chapter uses literature analysis to investigate the problems faced by financial management in the new economic environment, summarize and classify them, and discuss existing problems overly traditional financial management model with the continuous development of information technology many enterprises have adopted information technology management mode, both management process and management efficiency,which has the absolute advantage (zhang siwen, 2020). however, affected by the traditional form of bookkeeping, some enterprises are still paper-based bookkeeping. such financial management not only fails to guarantee the security of data, but also increases the difficulty of checking. it also increases the difficulty of checking. enterprises usually need to check all the data of previous years in order to find out an account. it the efficiency of financial management. in addition, which is very unfavorable to the stable development of enterprises. therefore, to create a good financial environment, enterprises must realize the financial information management, actively promote the construction of information technology, optimize the thinking and concept of the staff to ultimately improve the quality of financial management. poor professionalism of financial managers in order to keep the business running smoothly. many financial tasks can be carried out through the network . further simplify the reporting process. in addition, by the financial system, many enterprises in the recruitment of financial management personnel. candidates for the information management of the water asian requirements are not high, only the use of computers can be passed. part of the financial management personnel also exists in the work attitude, enthusiasm is not high, old-fashioned thinking and other issues. occasional errors in the work . it seriously affects the quality of financial management (wang huimin, 2020). at the same time . under the environment of economic globalization, the development environment of enterprises has changed. the financial risks faced, the scope of cost accounting is further expanded, and some financial managers lack solid knowledge of economic and financial theory. computing ability and knowledge updating ability is low. plus many financial personnel are copying the company’s financial program. lack of initiative and innovation in financial management. can not adapt to the development of enterprise informationization. thus, the quality of financial management is affected. more complex economic environment and greater exposure to risks compared with the planned economy, the market economy is more active and the economic elements are diversified. such an open economic environment increases the difficulty of financial management of enterprises. it also increases the risk of financial management, and the content of financial management is increasing. in addition, many enterprises nowadays are mostly adopting the shareholding system. in order to reduce the interests of differences, it is necessary to realize the fairness of resource distribution through financial management. in addition. enterprise financial talent allocation lag, financial management model, financial management concepts also need to be further improved in order to effectively respond to changes in the economic environment (liu jiaqi, 2021). therefore, to do a good job of financial management innovation is to enhance the unit’s financial risk control ability, to ensure the smooth progress of financial management work is an important way. reduced competitiveness of single firms with higher management costs under the influence of the international economic environment . china’s economic growth has slowed and economic development is under greater pressure. domestic currency issuance has increased, and the problem of supply exceeding demand in the market has become increasingly prominent. the reasons for the above problems are very numerous. in addition to the influence of the international economy, there is also a close connection with the development mechanism of our domestic economy. in order to solve the above problems, the state has applied scientific macro-control measures. however, it has not yet been solved fundamentally. in terms of the current domestic market, the development pa ge 64 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 61-66, 2024 of enterprises also exists greater pressure, the enterprise’s labor costs, material costs, material procurement costs and so on are constantly rising. financial management work is also facing greater pressure. in the new economic era, china has implemented a positive and prudent monetary policy and raised the reserve requirement ratio of bank deposits, the volume of money in the market has been lowered, and the financing problem of the enterprises has prevailed. and after entering the new economic development period, the cost of loans for enterprises has also been rising, which also has a certain impact on the development of enterprises. a large number of enterprises sales growth rate showed a downward trend, due to the competitiveness of enterprises greatly affected, the requirements of financial management work has also undergone new changes. results and discussion innovation of enterprise financial management models in the context of the new economic era proactive integration into new economic markets after entering the new economic era, enterprises need to reform and innovate the financial management mode according to the existing economic situation in order to achieve better development. only by adopting this way can we improve the level of enterprise management and comprehensive competitiveness (ai zhiping, 2020). the development of enterprises is closely related to the market, in the financial management model, but also based on its market demand to start the new economic era requires enterprises to his unified single model into a diversified development model, which should rely on big data, internet of things, artificial intelligence and other technologies to break the original ban, from the traditional business wei wei wei extended to a new field, requiring financial managers can use various information technology means to collect and summarize new data, to provide the best solution for the enterprise management level and comprehensive competitiveness (ai zhiping 2020). various types of internal and external data for enterprise development to provide decision-making data support and rationalization proposals. clarify the objectives of the enterprise’s time management work the enterprise financial management mode in the context of the new economic era should have a clear goal, not aimless extensive development. the formulation of financial management objectives needs to be consistent with the actual situation of the enterprise and the new economic era in line with the enterprise’s demand for tailor-made financial management objectives. objectives must be feasible. after the goal has been set, we should build a perfect actual ability of the theater, so that financial workers can suck out to the rules to follow, the enterprise up and down the personnel cohesion so that the enterprise financial management work will have a clear direction for the development of the enterprise to dedicate more strength, to create more benefits. in addition, but also within the enterprise to develop a sound financial management information platform, strengthen the link between the financial department and other departments, to lay a guarantee for the development of enterprises. building a sound internal control mechanism in order to realize the development of enterprises in the competitive atmosphere of the new economic era, it is necessary to build a perfect internal control mechanism within the enterprise to provide a stable foundation for financial management. the establishment of internal control mechanism should not only promote the stable and healthy development of financial management, but also provide reasonable suggestions for enterprise decision-making, and maximize the protection of comprehensive financial information, the internal control mechanism should also be consistent with the original rules and regulations of the enterprise, and ensure that every staff member has a clear understanding of the internal control mechanism, which will be used in the whole financial system (atmaja d s, zaroni a n, , 2023). 2023). and the scientific management of the enterprise’s monetary funds, assets, and the linkage of all aspects of the financial work, so that the file management, the transfer of current affairs can be closely integrated, strict control of the enterprise’s income and expenditure, for each sum of money, should be registered in a timely manner, through strict and perfect control methods, can make the enterprise to obtain more economic benefits. expanding the channels for corporate youth fundraising in the enterprise financial management work, financing channels is also a key, nowadays, the financial institutions for the enterprise’s payment has become more strict, need to comprehensively consider all aspects of the enterprise, in the analysis and decision-making, the enterprise lei more according to their own strengths and characteristics select suitable financing channels, do not need to be confined to a specific range, with the creative way to expand, form a virtual diversified investment channels which can also provide more funds for the enterprise’s development. this can also provide more funds for the development of the enterprise. when providing diversified communication channels between staff of various departments of the enterprise and solving all kinds of problems in a timely manner, the work efficiency of the enterprise can naturally be effectively improved. strengthening enterprise fund management although the enterprise development has stepped into the new economic era, the influence of the traditional economy will still continue for a long time, which has a dramatic effect on the enterprise financial management mode. under the new economic era, the speed of development of science and technology is getting faster pa ge 65 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 61-66, 2024 and faster, and the speed of product replacement is also faster than any other period in the past, enterprises in this context, need to strengthen the management of funds, for idle products, the seat of the stock of equipment to put forward scientific solutions to promote the flow of funds to solve the impact of the enterprise’s cash flow of the various causes of the scenario, and constantly enhance the enterprise’s capital circulation ability, so that the enterprise’s financial risk can be who hold to the level of science, so that enterprises can be stable and healthy development. enterprises can be stable and healthy development. at the same time, in the development of business, we need to make the best use of our strengths and shortcomings to optimize our resources, goals and strategies. the goal, strategy to reach the optimal point between me to innovative breakthroughs. expanding the scope of financial management under the new economic era, whether the financial management mode is scientific or not will have a great impact on the production and operation of enterprises, especially in the era of “internet 4”, the speed of information sharing is getting faster and faster (ferasso m, beliaeva t, 2020). in this era, in order to improve the comprehensive sound competitiveness of enterprises, it is necessary to strengthen the cooperation with other enterprises, give full play to the advantages of each enterprise, rationally integrate resources, form development synergy, and jointly cope with competition. in the financial management mode, to break through the traditional financial management thinking, expand the scope of management, the traditional financial management into external management and internal management of the combination of modes, and constantly expand the financial management of the work of the cut-off area. in addition, the enterprise lei who continue to improve the organizational and institutional structure, in the management department. business departments, financial departments to build management nodes, to avoid financial management activities in the application of the time service is inferior to independent and disconnected from the enterprise. in the kai structure, need to strictly according to the laws and regulations and “enterprise accounting standards 3 of the relevant requirements, according to the actual situation of the enterprise to improve the financial management system, according to the local conditions of the formulation of the rules, and put it into practice. strengthening the binding nature of the financial system strengthening the constraints of financial system can help enterprises better resist external market risks. financial information can effectively regulate the allocation of social resources, false financial information will affect the efficiency of resource allocation, affect the social and economic development, leading to a waste of valuable social resources, and increase market risk. enterprises belong to the market of important economic subjects, its financial risk will also be affected by the market, in order to reduce financial risk, the need to build a safe and stable market environment, in this regard, to strengthen the financial system constraints is very important, at the same time, to ensure the effectiveness of decision-making and authenticity of financial information. authenticity belongs to the life of the financial work, financial information belongs to the main basis for corporate decision-making, its function is to reflect economic activity, it is precisely because the financial information is the source of economic decision-making, directly affecting the accuracy of economic decision-making, if the financial information is false, not only lead to decision-makers to make the wrong evaluation, but also the development of enterprises deviate from the laws of the economy, to the enterprise to bring the indelible risk of hidden danger. therefore, we need to do a good job of the financial groundwork, improve the level of accounting, so that the financial report can be accurate, comprehensive and timely feedback on the changes in the enterprise’s economic operations. from the user’s requirements to start. improve the quality of financial information, internal and external information into which to enhance the effectiveness of decision-making. and further enhance the financial work in the development and management of the status of the enterprise, through a multi-pronged channel to reduce the financial risk of enterprises so that corporate financial activities can be carried out in a safe and stable environment. conclusion in the new economic context. to achieve further innovation in enterprise financial management, the relevant personnel should set up a modern thinking, clear the basic connotation of the new economy, grasp the new economic background the requirements of enterprise financial management innovation, fully understand the characteristics of enterprise financial management in the context of the new economy, the use of scientific and effective ways. realize the enterprise financial management innovation, to meet the actual needs of economic and social development, the enterprise management and operation level constantly improve, and then enhance the competitiveness of the enterprise market. references ai, z. (2021). innovation of corporate accounting management in the context of new economy. finance and economics, (23), 141-142. retrieved from http:// www.cnki.net. atmaja, d. s., zaroni, a. n., yusuf, m. (2023). actualization of performance management models for the development of human resources quality, economic potential,. a and financial governance policy in in indonesia ministry of education. multicultural education, 9(01), 1-15. retrieved from https://digilib.iainptk.ac.id/xmlui/handle/123456 pa ge 66 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 61-66, 2024 chen, x. (2019). research on the innovation of enterprise financial management objectives in the new economic era. journal of qiqihar university (philosophy and social science edition), (09), 86-88. retrieved from http:// www.cnki.net. cui, w. 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(2020). analyzing the innovation mode of accounting management of small and medium-sized enterprises under the background of new economy. business intelligence, (03), 19-20. retrieved from http:// www.cnki.net. pa ge 1 pa ge 48 american journal of applied statistics and economics (ajase) the impact of financial inclusion on banking stabilityan analytical study in the iraqi banking sector mohammed hammad safi1*, hichem khlif1 volume 2 issue 1, year 2023 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v2i1.2106 https://journals.e-palli.com/home/index.php/ajase article information abstract received: october 18, 2023 accepted: november 16, 2023 published: november 20, 2023 this research examines the impact of financial inclusion, as indicated by its indicators, on banking stability. the iraqi banking sector has been tested, and the main research problem revolves around finding appropriate ways to achieve financial inclusion that align with the iraqi central bank’s policies, tailored to the iraqi environment to achieve stability in the banking sector. the research was applied to the banking sector as a whole through quantitative and statistical data analysis from 2010 to 2021. the main hypotheses were tested to determine the impact relationships between research variables, to answer the research questions and achieve the set objectives. to analyze the data and information, various statistical methods were used using the statistical software (stata13) and excel, in addition to using the least squares method with the regression model for the hypotheses. the research yielded a set of results, indicating a significant partial effect of financial inclusion indicators on banking stability in this study. the results of the financial inclusion indicators in banking stability varied, and the sub-hypotheses were partially accepted, emphasizing a greater contribution of financial inclusion to it. keywords financial inclusion, banking stability, iraqi banking sector 1 baghdad university, iraq * corresponding author’s e-mail: mohammad87alsafi@gmail.com introduction one of the main lessons learned from financial crises, including the global financial crisis of 2007-2009, and culminating with the covid-19 crisis, is the importance of containing systemic financial risks and maintaining financial stability in general, and banking stability in particular. at the same time, countries’ economies strive to enhance financial inclusion, increasing access to financial services for low-income households and small businesses. this is part of their comprehensive economic and financial development strategies, facilitated through modern electronic systems that help extend banking services to a wider range of financial consumers. electronic systems have played a significant role in the overall economic and financial sector, particularly in the banking sector, which has seen significant developments in banking services. the banking needs and demands of the population have changed significantly over the years, with the expectation of accessing these services at anytime and anywhere, with minimal cost and effort. in alignment with these developments in the nature of electronic banking services, the current research idea has crystallized to address important and critical factors in banking, namely financial inclusion and banking stability. the results of many practical or applied studies have demonstrated the significance of these variables in the success and leadership of many banks in different environments, increasing their market share. given the need of iraqi banks for such studies and research, this research aims to test these variables in the iraqi banking sector. accordingly, the main objective of the research is to diagnose the levels of financial inclusion and banking stability in the iraqi banking sector. to achieve this objective, the research includes, according to its methodology, an overview of financial inclusion, while the second part addresses banking stability and presents the research framework. literature review financial inclusion the authors and researchers have approached the concept of financial inclusion from different perspectives. (lenka & sharma, 2017) defines it as “the process of ensuring that vulnerable groups, such as low-income sectors and lowincome groups, have access to suitable financial products and services at reasonable cost equitably and transparently by efficient mainstream institutions.” on the other hand, (guérineau & jacolin, 2014) sees it as “permanent access of the population to a variety of suitable financial products and services at reasonable costs and their effective and efficient use.” from this definition, three areas can be identified to define financial inclusion: access (supply), usage (demand), and affordability (financial conditions/product quality). additionally, (barajas et al., 2020) aims to generalize banking and financial products and services to all members of society with different segments through innovative, high-quality, and reasonably priced financial services using formal methods, including financial awareness and education. therefore, measures of usage likely reflect the availability of access, cost, and quality, and vice versa. with more detailed data available on specific aspects of financial inclusion, either within or across countries, the concept can be expanded to include access, quality, and cost dimensions as well. (lozi, 2021) introduces integrates financially marginalized or low-income categories that do not allow engagement in banking operations by dealing with the banking system through the digital work system, meaning completing all pa ge 49 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 48-53, 2023 financial transactions electronically. financial inclusion focuses on providing financial services through easy, simple, and cost-effective methods, such as mobile phone payments. the importance of financial inclusion lies in empowering low-income individuals to access high-quality and affordable financial services. it also gives them the appropriate importance and priority within the framework of economic policies, legislative development, and regulatory frameworks that help improve the spread of financial and banking services and encourage innovation in this field. therefore, expanding the reach of financial services benefits society as a whole, enhances individual financial stability (akhtar & pearce, 2010), and supports the banking sector and savings. the significance of financial inclusion also stems from its role in supporting entrepreneurs and startups by providing support and funding, enabling these startups to grow into small and medium-sized enterprises. these enterprises generate business opportunities and employment (blancher et al., 2019). it’s worth noting that the widespread availability of financial services and broadening participation in the formal financial system are essential factors in achieving sustainable development goals, improving living standards, empowering women financially, financing small and medium-sized projects, reducing poverty and inequality, creating jobs, promoting economic growth, and integrating the informal economy into the formal economy. there is a set of objectives that the central bank seeks to achieve by directing banks to hold annual conferences and compete among themselves to achieve financial inclusion because it cannot be achieved without a culture. the informed customer is more aware of the risks and gains associated with financial products and more aware of their rights and responsibilities. among these goals mentioned by researchers such as (al-hasnawi & mahdi, 2020; gabor & brooks, 2017; helms, 2006; ishioro, 2022; kumar, 2011) are; promoting access for all segments of society to financial services, informing individuals about the importance of these services, how to obtain them, and how to benefit from them; improving the living conditions of individuals, especially the poor classes, and working to reduce poverty and achieve prosperity by promoting entrepreneurship, providing economic development opportunities, and improving their social and economic conditions; speeding up access to sources of financing and providing support to small companies to expand their operations to achieve the required investment; establishing freelance projects to promote the country’s economic growth; encouraging individuals to save in banks and invest money using optimal methods, such as creating programs and promoting a culture of competition. the group of twenty (g20) issued, along with the global partnership for financial inclusion (gpfi), in june at the los cabos summit, the indicators for measuring financial inclusion. these indicators were developed during the 2016 china summit for the purpose of financial inclusion. these indicators address three main dimensions, as adopted by many authors and researchers in their books and studies, including (al-chahadah et al., 2020; eldomiaty et al., 2020; maher, 2022) : (1) access to financial services. (2) usage of financial services. and (3) the quality of financial services, including the quality of products and service delivery. banking stability the authors and researchers have addressed the concept of banking stability from various perspectives. (shubbar & vladimirovich, 2019) defined banking stability as the optimal way of analyzing the financial situation to avoid financial crises and ensure the banking system’s stability. this involves awareness of the need to use an organized approach to achieve and maintain stability in the long term, both at the national and regional levels. (alsomaidaee et al., 2023; bhattarai, 2020) emphasized that banking stability plays a vital role in the economic growth of a country, as it is a commercial institution that must generate profits from its operations to survive and fulfill its responsibilities. the main activities of commercial banks include resource mobilization, which involves costs, and profitable resource deployment. generating income exceeding expenses is the primary source of a bank’s profit. in cases where the bank fails to achieve sufficient returns on the allocated resources, it depletes both the company’s and the state’s resources. assets are the most important factor in determining the strength of any financial institution. the key factors to consider are the quality of the loan portfolio, the risk asset mix, and the credit management system. a high level of non-performing loans is a major concern for any bank. (my, 2020) defines the concept of banking stability as the effective execution of important economic functions such as resource allocation and risk management, the ability to fully absorb shocks faced by the system, evaluating changes in financial risks, and the efficient allocation of resources. this, in turn, demonstrates the resilience of all financial activities and sectors to reduce losses occurring during banking crises. both (anh et al., 2021), and (sifrain, 2021) define banking stability as a state in which a bank can operate smoothly and efficiently, allowing it to perform its functions well, such as resource allocation, risk distribution, income distribution, payments, and credit. additionally, the bank must be able to withstand external shocks, which aligns with. the importance of financial stability becomes evident through the repercussions of recurring financial crises in general, and banking crises in particular, which affect economies periodically, starting from the financial crisis of 2008 and extending to the covid-19 crisis. this prompts central banks to focus on achieving financial and banking stability in their countries. both (uhde & heimeshoff, 2009) and (alwan & kadhim, 2020) agree that the importance of banking stability lies in the measures and policies adopted by banks, with the most pa ge 50 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 48-53, 2023 important ones being the accuracy of public budget calculations in the operation of financial institutions increases investor confidence and transparency in credit flow, as well as an increase in the percentage of credit allocation to medium and small projects with feasibility. this helps improve market discipline, increase bank disclosure, and address cases where banks refrain from financing profitable projects due to significant deviations in asset prices from their true value or failure to settle payments on time. banking stability measures the global financial crisis highlighted the significant impact that struggling banks can have on the global economies. the successful development of an economy, as noted by (ginevičius & podviezko, 2013), depends on banks’ efficient and stable performance. this leads to the necessity of having sound indicators for banks, as risks must be identified by integrating both internal (banking) and external (market) factors that are easily accessible and can be easily designed by banks (j. powell & h. vo, 2020). banking crises and banking regulation are recurring topics in economic and financial policy discussions. since the early 1970s, banking crises have repeatedly affected emerging and transitional economies more than others. however, there is a lack of understanding of the factors that generate banking crises. regulators tend to assist troubled institutions more than resolving them. prudential regulatory controls aim to reduce excessive banking risks and capital shortfalls in an attempt to protect society (kane, 2016). unfortunately, these controls often come too late, after the crisis has already spread. therefore, a better understanding of risk factors can be useful in reducing risks, especially when regulatory authorities can address distress situations before they spread to the broader financial system (martínez-malvar & baselgapascual, 2020). the increase in capital adequacy has become a more important strategy for enhancing banking stability in the aftermath of the global financial crisis of 20072009. during this period, most central banks proposed an increase in capital adequacy as a requirement for building stability in the banking industry (sulemana et al., 2018). to the best of the researcher’s knowledge and information, several authors and researchers have concurred, including (chouhan et al., 2014; mutarindwa et al., 2020; oyetade et al., 2022; yunita, 2022). they used the z-score indicator to measure banking stability. the banking financial stability indicator (z-score) plays a vital role in enhancing the reputation and security of the banking system and increasing international trade in light of the significant economic growth in countries that impact banking services. this indicator suggests the ability to predict financial crises (alshubiri, 2017) and can be counted as an early warning system for the banking system (faruqinata & wibowo, 2020). the postglobal financial crisis period of 2007-2009 witnessed numerous experimental efforts to assess the effectiveness of discriminative analysis. most of these studies focused on cases of bank failures during the financial crisis, increasing interest in the z-score indicator from earlier times (mugo, 2021). (syed et al., 2022) emphasized that the z-score indicator is important in achieving banking stability, a point reiterated by (oyetade et al., 2022). z-score is a measure used to predict bank failure or financial distress and is a common measure of banking resilience, assessing the extent to which a bank’s capital can cover losses resulting from variations in returns without going bankrupt. methodology the research sample represents the iraqi banking system in its entirety. this is because there were changes in the structure of the banking system during 2021, due to the entry of some local banks and the exit of some foreign banks from the banking sector, particularly lebanese banks that were invested in iraq. this resulted from the economic crisis in lebanon, which led lebanese banks to withdraw from some countries, including iraq. the total number of operating banks, with the central bank of iraq at the forefront, became 74 banks, including 7 government banks and 3 specialized banks, 3 commercial banks, and one islamic bank. meanwhile, the number of private banks reached 67 banks, including 25 local commercial banks and 28 local islamic banks, as well as 14 foreign banks, consisting of 2 islamic banks and 12 commercial banks. the z-score measure has been proposed as an indicator of risk and the likelihood of bank insolvency or failure. this measure has been widely used in numerous studies and has become common for assessing banks’ distress, failure, and stability. it was initially developed by roy in 1952 and subsequently refined by (sifrain, 2021). its value indicates the number of standard deviations that need to occur in the return on assets (roa) ratio, which is the number of times the return decreases from its value in order to deplete equity and render the bank insolvent. the value of the indicator increases with higher profitability and equity levels (lepetit & strobel, 2015). conversely, it decreases when returns are more volatile and decline, as reflected in an increase in the standard deviation of the return on assets. in other words, as the indicator value rises, the bank’s stability increases, and conversely, when the indicator value significantly declines, the level of stability and the ability of banks to withstand shocks decrease (li et al., 2017). as the value approaches one or approaches zero or becomes negative, the bank enters a state of financial distress and instability, leading to a banking crisis. the stability of the banking sector can be expressed mathematically, as in the following formula. z-score= (roa+(equity/assets))/(σ(roa)) the financial stability in iraq can also be measured using the aggregate index. different countries vary in their use of indicators and measures of banking stability, depending on the monetary policy adopted by the central bank. this variation is attributed to the strength pa ge 51 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 48-53, 2023 of banking systems and the systems in place, whether they are advanced with full electronic automation or they follow traditional systems. the central bank of iraq has defined the banking stability indicators in iraq, including capital adequacy, asset quality, profitability, and liquidity (central bank of iraq report, 2021). results and discussion to test research hypotheses and analyze them, the impact of financial inclusion with its indicators on banking stability was tested based on time series data. through this model, the relationship between research variables will be identified. before starting the hypothesis testing procedures, it is necessary to ensure the suitability of the data for the regression model of least squares used in this research by examining the data and determining whether it possesses the characteristic of normal distribution, i.e., whether the data of the variables follow a normal distribution. the table 1. displays the results of normal distribution tests using the shapiro-wilk w test for this type of test. it ensures that all variable indicators possess the characteristic of a normal distribution. table 1: results of normal distribution tests shapiro wilk w test for normal data variable obs w v z prob>z sig. banking density 12 0.85399 2.440 1.738 0.4113 not significant banking spread 12 0.96338 0.612 -0.957 0.83082 not significant atmratio 12 0.86575 2.243 1.574 0.05775 not significant sub/1000km 12 0.86103 2.322 1.641 0.05036 not significant atm/1000km 12 0.86422 2.269 1.596 0.05523 not significant depth of banking loans index 12 0.94051 0.994 -0.012 0.50471 not significant depth banking index 12 0.89952 1.679 1.009 0.15637 not significant b. st. index 12 0.81225 3.137 2.228 0.1296 not significant hypothesis testing the primary hypothesis of the research was to test the direct impact of financial inclusion, as indicated by its independent indicators, on banking stability. table (2) presents the results of testing the first hypothesis of the study, which showed significant effects. the bd index had a significant impact on stability (β=-0.0089, p < 0.05), as did the bs index (β=0.1228, p < 0.05), atm (1000) (β=-0.053, p < 0.05), and dobli (β=-0.0333, p < 0.05), while the r atm (β=0.0318, p > 0.05) and sub (1000) (β=0.00769, p > 0.05) did not have a significant impact on banking stability. the coefficient of determination or r-squared (r2) for each test model was significant (r2=(0.281, 0.255, 0.221, 0.488, 0.426), with a statistical significance of p = 0.000. this indicates that the variation in banking stability was explained by the significant financial inclusion indicators, while the remaining determination coefficients were explained by other variables not considered in this test model. based on these results, the impact of financial inclusion during the study period was not total but partial, due to the significant impact of some indicators in the test model. the reason for the significance of the impact on banking stability may be that the financial inclusion indicators represent an investment that aims to achieve planned stability. however, this stability appears to be inversely related to the beta values, meaning that as there is an expansion in financial inclusion, it is accompanied by a decrease in stability. this could be attributed to the growth rates of financial inclusion indicators leading to an increase in deposits and loans provided by banks to customers. additionally, the banking spread index in all its forms negatively affects the nature of stability due to an increase in the supervision scope and a loss of control over branches. this, in turn, leads to an increase in non-performing loans compared to the decrease in deposits, which are considered the primary drivers of banking activity. table (2) summarizes the results of testing the hypothesis. table 2: results of the first hypothesis testing path α coef. std. err. t p>|t| r2 prob bd <--b. st. index .6301 -.0089 .0041 -2.17 0.030 0.281 .000 bs <-- b. st. index -.0313 .1223 .0603 2.03 0.043 0.255 .000 r atm <-- b. st. index .3935 .0318 .0174 -1.82 0.069 0.216 .000 sub/1000 <-- b. st. index .1165 .0769 .0953 0.81 0.420 0.051 .478 atm /1000 <-- b. st. index .3797 -.0530 .0286 -2.05 0.044 0.221 .000 dobli <-- b. st. index .5544 -.0333 .0098 -3.38 0.001 0.488 .000 dbi <-- b. st. index .5635 -.0245 .0082 -2.99 0.003 0.426 .000 pa ge 52 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 48-53, 2023 conclusions the current paper aimed to test the impact of financial inclusion on the stability of the iraqi banking sector. the results confirmed a significant decrease in the number of bank branches during the research sample period relative to the population in iraq, according to international standards. the results indicated the inadequacy of the number of atms, despite a noticeable increase in their numbers. however, this increase is not proportional to the growing demand for them, leading to a significant gap between the two. regarding banking distribution, there has been a noticeable relative increase in recent years during the time frame of the research sample. however, it has not reached the required level according to global standards. this can be attributed to the focus of the research sample’s banks on major cities, which are characterized by high population density. also, the stability of the iraqi banking system witnessed significant fluctuations during the research sample period. in the first five years of the research period, there was clear stability, followed by a significant decrease in the composite index level, as endorsed by the central bank of iraq, as a natural consequence. the reasons for this can certainly be attributed to the security and political conditions that iraq went through during that period, and undoubtedly followed by the covid-19 pandemic. in light of the conclusions reached regarding the iraqi banking sector in the research sample, it becomes evident 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(2018). foreign bank inflows: implications for bank stability in sub-saharan africa. african review of economics and finance, 10(1), 54-81. syed, a. a., kamal, m. a., ullah, a., & grima, s. (2022). an asymmetric analysis of the influence that economic policy uncertainty, institutional quality, and corruption level have on india’s digital banking services and banking stability. sustainability, 14(6), 3238. uhde, a., & heimeshoff, u. (2009). consolidation in banking and financial stability in europe: empirical evidence. journal of banking & finance, 33(7), 12991311. yunita, p. (2022). dual banking system stability index in the shadow of covid-19 pandemic. international journal of islamic economics and finance (ijief), 5(1), 151-176. pa ge 1 pa ge 7 american journal of applied statistics and economics (ajase) review on: effect of inflation on economic growth in ethiopia tadele anagaw1* volume 2 issue 1, year 2023 https://journals.e-palli.com/home/index.php/ajase article information abstract received: april 12, 2023 accepted: may 03, 2023 published: may 17, 2023 one of the main objectives of macroeconomic policy of most developing countries like ethiopia is to attain sustainable economic growth together with stable price level. price stability is considered as a proxy for macroeconomic stability, the ongoing high level of inflation is not a good sign. large fluctuations in inflation for firms, consumers and the public sector reduce the economy in the long run growth potential. the nations of the countries who are highly affected by the problem of inflation are those who have low level of income or fixed income and unemployed portion of the population. higher average inflation has a negative impact on the steady state growth. this is because of the higher cost of transaction that inflation causes to the money market. it is generally accepted that instability in the general level of prices causes substantial economic distortions, leading to inefficiencies, both in aggregate employment and output. due to different factors, the effects of inflation have significant effect on economic growth in ethiopia. keywords economic growth, inflation, ethiopia 1 department of agricultural economics, salale university, fitche, ethiopia * corresponding author’s e-mail: tadeleanagaw21@gmail.com introduction world economic growth and inflation rates have been fluctuating. likewise, inflation rates have been dominating to compare with growth rates in virtually many years and relationship between inflation and the economic growth continued to be one of the most macroeconomic problems (madhukar and nagarjuna, 2011). one of the central macroeconomic policy objectives of most developing countries in the world is maintaining price stability together with economic growth. ethiopia is one of the countries in sub saharan african with moderate economic growth in recent years. the country’s economic progress is accompanied by sustained inflationary problems. inflation’s effects on an economy are various and can be simultaneously positive and negative in ethiopia (umaru and zubairu, 2012). according to abdurahman mohammed’s report, ethiopia’s inflation rate remains persistently high, reaching 33.6 percent in february 2022. the renewed conflict in the north and the most prolonged and severe droughts in recent years were the main reasons behind the high inflation, given the adverse impact on economic growth. a high or unpredictable inflation rate are regarded as harmful to an overall economy that add inefficiency in the market, and makes it difficult for companies to plan long term (mankiw, n.gregory, 2002). objective of the review to review the effect of inflation on economic growth in ethiopia. overview of inflation and economic growth in ethiopia inflation is defined as the general rise in the price level of goods and services in the given economy. general rise in the price level indicates the net change in the price of all baskets of commodity produced and services provided in the economy. that means there may be an increase or decrease in the price of basket of some commodity in the economy. the net effect gives us the general rise in the price level or decrease in the price level. if the net change is a rise in the price level, we can call it inflation otherwise deflation (teshome, 2011). it measures the change in average price level on a year on year basis-that is: where: t is a particular year in time; and t-1 is the year before (gillepie, 2011). economic growth is the increase in the amount of goods and services produced by the economy overtime (solow, 1995). it can be measured in real or nominal terms. real terms have been adjusted for inflation and nominal terms are not adjusted for inflation. economic growth is usually measured as the percentage rate of increase in real gdp (swan, 1997). on the other hand, economic growth is a sustained increase in per capita national output or net national product over a long period of time. (dwivedi, 2004). theories on economic growth and inflation economists have been studying about inflation and its effect on economic growth starting from the appearance of classical economic theory to modern economic theories. the following table summarizes the statement of different theories on about inflation and economic growth. the effect of inflation on economic growth in ethiopia inflation’s effects on an economy are various and can be simultaneously positive and negative. inflation causes individuals to substitute out of money and into interest https://journals.e-palli.com/home/index.php/ajahs pa ge 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 7-10, 2023 table 1: theories on inflation and economic growth no theories their statement on inflation 1 classical theory there is no direct explanation between inflation and its tax effect on profit level & output. but the relationship between the two variables is implicitly negative by the reduction in firms’ profit level and saving through higher wage costs (gokal and hanif, 2004). 2 keynesian theory excess demand is the major cause for the existence of inflation (gokal v. and hanif s, 2004). 3 monetary theory inflation occurs when money supply rises faster than the rate of economic growth of national income (richard froyen, 1998). 4 neo classical growth theory inflation increases output growth rate by stimulating capital accumulation, because in response to inflation households would hold less in money balance and more in other asset (mudell, 1963). table 2: source of inflation no source of inflation 1 demand pull inflation it occurs when aggregated demand exceeds aggregate supply. this excess demand may occur due to increase in one or all components of aggregate demand which includes consumption, investment, government expenditure and net exports. the excess demand creates disequilibrium and pulls up prices until equilibrium is restored. this is because the increase in aggregate demand causes shortage of goods and services at old prices. this in turn leads to price increases until equilibrium is restored (campbell r and l.stanley, 1986). 2 cost push inflation it is a situation occurs when there is caused by an increase of one or more of the cost or supply side factors such as the rising wages, input price (domestic or imported), interest rate, taxes, and exchange rate (campbell r and l.stanley, 1986). current causes of inflation in ethiopia 1 outbreak of covid-19 the impact of covid 19 pandemic uncertainty shock on the macroeconomic stability in ethiopia in the short run period. 2 civil war ethiopia was embroiled in spiraling & soaring ethnic and a state-based large-scale armed conflict that has continued for more than a year. earning assets, which leads to greater capital intensity and promotes economic growth. in effect, inflation exhibits a positive relationship to economic growth. inflation initially motivates capital accumulation which will contribute to higher growth. but the effect of inflation on growth is only temporary since this works only until the return on capital falls (tobin, 1965). high inflation increases the opportunity cost of holding cash balance and can induce people to hold greater portion of their assets in interest paying accounts. with high inflation, firms must change their prices often in order to keep up with economy. it can benefit the inflators (those responsible for the inflation) and it benefits early and first recipients of the inflated money. it can also benefits big cartels, destroys small sellers, and can use price control set by the cartels for their own benefits. (mankiw, n, 2002). optimal inflation ranging from 2–3% is good for economic growth. it increases the employment opportunities in the countries, increases the economic activities, and encourages investment and production by raising the rate of profit (fischer, 1993). unpredictable inflation rate are regarded as harmful to an overall economy they add in efficiency in the market, and makes it difficult for companies to plan long term and also that inflation distorts price mechanism, and this will affect the efficiency of resource’s allocation and hence influence economic growth negatively (fischer, 1993). it can impose hidden tax increases as inflated earnings push tax payers in to higher income. with high inflation, purchasing power is redistributed from those on fixed nominal income. it also makes traders from an increased instability in currency exchange price caused by unpredictable inflation (mankiw, n, 2002). inflation and economic growth entitled that the channel through which inflation affect economic growth and inflation negatively affects growth by reducing investment, and by reducing rate of productivity growth (fikirte, t, 2012). high inflation can cause serious problems on the economic growth. it would bring a large distribution of income. higher food price would hurt the urban poor who spend most of their income on food. moreover, although it would have a positive effect on the rural food producers, it would have an adverse effect on the rural food buyers, which may consist of about half of population in the rural ethiopia. thus, higher inflation, particularly through higher food price, could worsen the economic inequality. high inflation would also increase of uncertainty about future inflation (conrad and karanasos, 2004). https://journals.e-palli.com/home/index.php/ajase pa ge 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 7-10, 2023 inflation can cause a number of problems for an economy. it may damage business confidence because of fears about the future impact on costs. this may reduce levels of investment. uncertainty about future inflation rates will make it difficult to estimate future profits and therefore may deter many projects, damaging economic growth; if prices are increasing this creates costs for firms, because they may have to update their promotional material to list the higher prices; it erodes the purchasing power of individuals’ earnings. if wages do not increase as much as prices, then, in real terms, wage earners are worse off. their real income has fallen; if the prices of firms in ethiopia are increasing faster than those of their trading partners, then this may make the ethiopian products uncompetitive compared to those of foreign firms; tax thresholds often do not increase in line with inflation. if employees gain a wage increase to match inflation, then they are not better off in real terms. however, with higher nominal wage, individuals may enter a higher tax bracket and therefore be worse off. this is called bracket creep. and also redistributes income from one individual to another. debtors benefit during inflation moments at the expense of creditors and the government gains at the expense of the private sector; and inflation creates inflation expectations and it actually feeds on these expectations. it is often said that the greatest cost of inflation is the one inflation causes itself. the effects of inflation will depend partly on whether it is anticipated or unanticipated inflation. if inflation levels are regularly unanticipated, then this will lead to high levels of uncertainty in the economy, which may deter investment and affect spending, and impact saving decisions (arnold, 2008). table 3: summary of effect of inflation on economic growth in ethiopia no author/s, year of publication effect of inflation on economic growth in ethiopia 1 barro (1995) it reduces the level of investment and a reduction in investment adversely affects economic growth of the country due to sharp increase in the cost of their investment projects. 2 dotsey and sarte (2000) higher average inflation has a negative impact on the steady state growth. this is because of the higher cost of transaction that inflation causes to the money market. 3 bruno and easterly (1996) a higher level of inflation harms the growth and lower inflation has less cost on the economy. 4 fisher (1993) inflation negatively affects growth by reducing investment, and by reducing rate of productivity growth. fisher also argues that inflation distorts price mechanism, and this will affect the efficiency of resource's allocation and hence influence economic growth negatively. he argued that inflation hampers the efficient allocation of resources due to harmful changes of relative prices. 5 conrad and karanasos, (2004) high inflation can cause serious problems on the economic growth. it would bring a large distribution of income. higher food price would hurt the urban poor who spend most of their income on food. moreover, although it would have a positive effect on the rural food producers, it would have an adverse effect on the rural food buyers, which may consist of about half of population in the rural ethiopia. thus, higher inflation, particularly through higher food price, could worsen the economic inequality. 6 fikirte, t, (2012) inflation and economic growth entitled that the channel through which inflation affect economic growth and inflation negatively affects growth by reducing investment, and by reducing rate of productivity growth. 7 arnold, (2008) if inflation levels are regularly unanticipated, then this will lead to high levels of uncertainty in the economy, which may deter investment and affect spending, and impact saving decisions. conclusion one of the main objectives of macroeconomic policy of this country is to attain sustainable economic growth together with stable price level. as known, day to day increment of price of goods and services is a key problem of economic backwardness in ethiopia. sustained inflation has harmful effects on societal welfare and income inequality in such a way that the income distribution tends to be skewed and also decrease in the real value of money and other monetary items overtime, uncertainty over future inflation may discourage investment and savings, and high inflation leads to shortages of goods if consumers begin hoarding out of concern that prices will increases in the future. therefore, https://journals.e-palli.com/home/index.php/ajase pa ge 10 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 7-10, 2023 to overcome this inflation problem on economic growth of the country, focus should be given on policies that will achieve price stability in the country, firms should produce more production to satisfy consumers and to reduce inflation, peoples who have low income should increase their income and reduce inflation by reorganizing their production system, e.g. using technology. references abdurahman m. (2022). food inflation stands high in ethiopia despite policy measures to stabilize prices. global agricultural information network, addis ababa, ethiopia. arnold, r. (2008). economics, 8th edition. california: thomson south-western corporation. barro, robert j. (1995). inflation and economic growth. cambridge: harvard university. bruno, m. and easterly, w. (1996). inflation and growth: in search of a stable relationship’ federal. campbell r. and brue, l. (1986). contemporary labor economics. conrad, c. and karanasos, m. (2004). on the inflationuncertainty hypothesis in the usa, japan and the uk: a dual long memory approach. japan and the world economy, 17(4), 327-343. dotsey, m. and sarte, p. (2000). inflation uncertainty and growth in a cash-in-advance economy. dwivedi, d. (2004). managerial economics. 6th edn,vikas publishing house pvt ltd, new delhi.economics, 32, 485-512. fikirte, t. (2012). thesis on economic growth and inflation: södertörns university. fisher, s. (1993). the role of macroeconomic factors in growth, journal of monetary. gillepie, a. (2011). foundation of economics. 2nd edition. new york. gokal, v. and hanif, s. (2004). relationship between inflation and economic growth. working paper, reserve bank of fiji. madhukar, s. and nagarjuna, b. (2011). inflation and growth rates in india and china: a perspective of transition economies, international conference on economics and finance research., 4(97) 489-490. mankiw, n. g. (2002). macro economics. 5th edition, worth publishers. mundell, r. (1963). inflation and real interest. the journal of political economy, 71(3), 280-283. richard, t. (1998). macroeconomics theories and policies, sixth edition .university of north carolina at chapel hill. solow, r. (1956). a contribution to the theory of economic growth: quarterly journal of economics, 70. swan, t. (1997). economic growth and capital accumulation: economic record, 32, 334-61. teshome, a. (2011). sources of inflation and economic growth in ethiopia part i and ii. tobin j. (1965). money and economic growth, econometrica, 33(4), 671-684. umaru, a. and zubairu, a. (2012). effect of inflation on the growth and development of the nigerian economy: an empirical analysis. international journal of business and social science, 3(10). https://journals.e-palli.com/home/index.php/ajase pa ge 1 pa ge 10 9 american journal of applied statistics and economics (ajase) navigating employee retention: the importance of compensation in phnom penh’s private companies, cambodia meng kheang sorn1*, guanghui fu1, sreang leangheng2 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2893 https://journals.e-palli.com/home/index.php/ajase article information abstract received: april 18, 2024 accepted: may 21, 2024 published: may 25, 2024 this research addresses the critical issue of employee retention in private companies in phnom penh, cambodia, specifically focusing on compensation practices. the study employs quantitative and qualitative methods, utilizing a cross-sectional approach to collect data from a sample of 335 employees. the findings reveal that many employees view their compensation package as a key factor influencing their decision to remain with their current company. however, a substantial proportion of employees expressed dissatisfaction or neutrality towards their compensation packages, signaling potential areas for improvement. the research contributes to a deeper understanding of employee preferences regarding compensation packages, thereby aiding businesses in enhancing job satisfaction and loyalty. furthermore, the study offers practical recommendations for improving compensation practices, including enhancing transparency, conducting regular market analyses, addressing employee concerns, reassessing compensation strategies, investing in employee satisfaction, and considering employee feedback. in essence, this research comprehensively analyzes compensation practices and their impact on employee retention, offering valuable insights and actionable recommendations for private companies in phnom penh, cambodia. keywords cambodia, compensation, employee retention, phnom penh, private companies 1 school of economics and management, nanjing tech university, nanjing city, jiangsu province, china 2 faculty of finance and accounting, national university of management, phnom penh capital city, cambodia * corresponding author’s e-mail: mengkheangsorn@gmail.com introduction compensation is widely regarded as one of the most essential aspects determining employee retention in an organization. it is a well-established fact that employees are the backbone of any organization. hence, retaining talented and efficient employees is crucial for the organization’s success. the cambodian job market has undergone significant changes in the past decade, with an increasing number of organizations offering higher compensation packages to attract and retain top talent. despite these changes, many organizations still struggle to retain their employees due to various factors. in cambodia, retaining skilled employees has become a significant challenge for organizations due to the intense competition in the market. it has become increasingly challenging to keep talented and skilled employees who can be easily enticed by better job opportunities or higher salaries. the importance of employee retention is widely recognized by businesses operating in cambodia. consequently, employee retention is a key component in the success of private companies, especially in cambodia, where there is a high demand for skilled labor. thus, this study investigates the importance of compensation in employee retention within private companies in phnom penh, cambodia. specifically, it will examine employees’ current compensation packages, assess their satisfaction levels, and explore their experiences and perspectives regarding their compensation. research question and objective research question to what extent do compensation practices contribute to employee retention in private companies in phnom penh, cambodia? research objective to analyze the importance of compensation practices in influencing employee retention in private companies in phnom penh, cambodia, based on employees’ perceptions and experiences. significance of research this research is of significant value as it addresses a critical issue faced by private companies in phnom penh, cambodia employee retention. by focusing on compensation practices, the study provides valuable insights that can assist companies in enhancing their retention strategies. it contributes to a deeper understanding of employee preferences regarding compensation packages, enabling businesses to tailor their compensation strategies to better align with the needs and expectations of their employees, thereby enhancing job satisfaction and loyalty. literature review compensation compensation refers to all forms of financial benefits that employees receive from an employer in exchange for their work. it includes base pay, bonuses, benefits, and other incentives (kishore, p., das, k., & mohapatra, p., 2014). salary and earnings, benefits, bonuses, and other extras are all considered forms of employee compensation. pay that employee regularly earn for their labor is referred to as their salary or wages. retirement plans, insurance, pa ge 11 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 109-118, 2024 and different kinds of leave are indicators of benefits. bonuses are extra money given out when objectives are met or surpassed. other benefits may include professional development opportunities, flexible work hours, on-site parking, lunches provided by the firm, and more (sorn et al., 2023). employee retention employee retention pertains to the proportion of employees that an organization manages to keep. the term “retention” is commonly used when discussing employee turnover; however, they are not entirely interchangeable. retention concentrates on enhancing the contentment of current employees by offering growth possibilities, challenges, and incentives, such as bonuses and compensation that inspire the most skilled workers to remain with the company. turnover, on the other hand, is an inevitable aspect of any business, brought about by various factors, including involuntary and voluntary ones (ruth mayhew, 2017). employee retention refers to an employer’s potential to keep best personnel (jennifer herrify, 2023). overview of cambodia cambodia’s economy cambodia’s economy grew at an average annual rate of 7.7 percent between 1998 and 2019, making it one of the fastest-growing economies in the world. the economic growth for 2023 is projected to reach 5.2 percent after recovering from covid-19 pandemic (world bank, 2023). cambodian’s population according to the world factbook by the central intelligence agency, cambodia’s population is estimated to be 16,891,245 in 2023 (1.04% growth rate). most of the population is concentrated in southeast asia, notably in and around the capital city of phnom penh. the united nations population fund also provided data on cambodia’s population: the total cambodian population in 2023 was 16.9 million. most, 65% were in the age group of 15 to 64 (cia, 2023). labor market in cambodia the cambodia labour force survey (clfs) report summarized the employment and unemployment status of the working age population. the report provided decent work indicators related to employment and unemployment, working conditions, social protection, and recruitment cost of migration. the working age population (aged 15 years and over) was about 11.5 million (73.2% of the total population), of whom 6.1 million (53.1%) were females and 5.4 million (46.9%) were males. the report further added that the total working-age population, 68.5% (or 7.9 million people) were classified as employed, 0.8% (or 97,687) were unemployed, and 30.7% (or 3.5 million) were not in the labour force at the time the survey was conducted (ilo, 2019). understanding compensation and employee retention in cambodia cambodians do not typically work more than 48 hours per week. for any employees who work more than eight hours per day or 48 hours per week, they are eligible for overtime payments. standard overtime rates are 1.5x (150%) an employee’s regular hourly wage. if an employee works overtime at night, on a sunday, or a designated holiday, they are entitled to overtime rates that 2x (200%) their regular hourly wage. employers may not request employees to work more than two hours of overtime per day (horizons, n.d). the standard workweek in cambodia is 6 days per week, with 8 hours per day, or 48 hours per week. employees must get at least one full day (24 hours) off per week, typically on sunday. enterprises have the right to limit the work hours, as long as they adhere to the stipulated law. the lunch break can last from 30 minutes to 1 hour, depending on the business requirements. the maximum number of overtime hours per day is 2 hours. therefore, the maximum number of hours a person should be employed per day is 10 hours. public holidays in cambodia are paid leave, and employees receive 1.5 days of annual leave for each month of company service. women who have worked for a firm for at least a year are eligible for maternity leave. full-time employees get 1.5 days of annual leave per month, but many organizations allow employees to take their leaves in the first year. employees who work fewer than 48 hours per week are considered part-timers and are entitled to yearly leave in proportion to their employment. so, an employee working 24 hours per week must be given at least nine days of paid leave annually. official paid holidays and sick leave are not counted as paid annual leave. expectant mothers are entitled to 90 days of maternity leave with pay after one year of continuous service. after returning from maternity leave, they are only expected to perform light work. mothers who breastfeed their children during working hours are entitled to one hour per day. this time can be divided into two periods of thirty minutes each. sick leave with pay: according to the arbitration council and several prakas, every company must establish internal policies for giving employees paid sick time when they present certification from authorized and legally recognized doctors confirming their illness. the pay scale for sick leave depends on the during of sick leave: employees who take one-month sick leave will be paid 100% of the salary, 60% of the salary for taking sick leave from 2 to 3 months and employees who take sick leave more than 4 months have no wages. sick leave without pays: each employee has a right to sick leave. the labor law is generally silent on sick leave, other than requiring a company to suspend a contract for up to six months in case of illness. in other words, the company must keep a sick employee’s job open for at least six months without compensation. social insurance, work injury, survivors’ pension, temporary disability benefit, constantattendance allowance, permanent disability pension is paid pa ge 11 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 109-118, 2024 depending on the severity of the disability and the length of hospitalization. medical and rehabilitation services are also provided (usama, 2022). seniority payment: all employees with an unlimited duration contract (udc) are entitled to receive ongoing seniority payments. the amount of the seniority payment is equal to 15 days of the employee’s wages and other benefits per year. employers must pay the seniority payment to employees in two installments: • an additional 7.5 days’ worth of their wage in june of each year. • an additional 7.5 days’ worth of their wage in december of each year. a new employee is entitled to a full installment of the seniority payment (being equal to 7.5 days of wages and benefits) if the employee worked for at least one month in the applicable period (being from january to june, or july to december) (remote, n.d). according to the ‘hrinc consulting annual compensation surveys’, the average turnover rate among large companies in cambodia is around 19 percent. this indicated that high rates of employee turnover can lead to low employee retention rates. retaining employees and managing hr in cambodia is a significant challenge for private companies. the most common reasons for an employee initiating separation with the company in cambodia are better salary and compensation offered, move to another company in the same industry, and employee offered a higher position which the company could not provide (ses socheata & nil keorachana, n.d). elements influencing employee retention a study on factors affecting employee retention of private companies in cambodia using delphi method exhibited that the experts agreed that there are five factors greatly affecting employee retention of private companies in cambodia: compensation; promotionopportunity and growth; work environment; training and development; and work-life balance. furthermore, the findings suggested that private companies in cambodia could improve employee retention by focusing on these five factors. for example, they could offer competitive compensation packages, provide opportunities for growth and promotion within the company, create a positive work environment, invest in training and development for their employees, and support work-life balance (ramon macaraig jr et al., 2023). the factors affecting employee retention in the banking sector, specifically focusing on curriculum development, employee capacity building, lifelong learning, career development, and career success. the results indicated that curriculum development, employee capacity building, lifelong learning, career development, and career success are critical factors influencing employee retention in the banking sector in cambodia. lifelong learning of employees has a positive influence on both employee career success and employee retention. the research findings contributed to understanding the importance of employee training and development in enhancing employee retention in the banking sector. organizations should consider these factors to retain talented employees and reduce turnover rates (sovannara et al., 2023). the role of compensation in employee retention compensation has a significant influence on employee retention in banking institutions in dar es salaam, tanzania. specifically, the attribute of fair salary was found to be the most valued compensation attribute contributing significantly towards employee retention. the study recommended that bank managers develop and implement retention policies that contemplate fair salaries and pay great attention to right retention policies in order to improve retention of employees (hanai, a. e., & pallangyo, w. a., 2020). bonuses for the lunar new year holiday have a positive effect on employee retention. employees perceive bonuses as a form of recognition and appreciation, leading to increased job satisfaction and organizational commitment. hence, the role of bonuses as a form of compensation is crucial when considering strategies to boost employee retention. this research provides valuable insights for companies seeking to improve employee retention through the implementation of strategic bonus schemes that are sensitive to cultural and seasonal contexts (do et al., 2023). methodology research design this study used a mix of quantitative and qualitative methods to gather and examine data from private company employees in phnom penh, cambodia. it employed a cross-sectional approach, collecting data from a sample of employees at a single point in time. sample collection the sample size for this research was initially targeted at 350, based on the availability and willingness of employees to participate in the survey. however, a total of 335 respondents successfully submitted and returned their responses for this research. participants were selected using a convenience sampling method, which is a non-probability sampling method used in research. in this research, the convenience sampling consisted of employees currently working in private companies in phnom penh capital city, cambodia. these employees were invited to voluntarily take part in the survey. data sources primary data was collected by conducting a survey targeting employees at private companies in phnom penh, cambodia. an online questionnaire was utilized for this survey, and it was disseminated through various channels including social media and individual as well as group messaging platforms, inviting employees to participate. the secondary data sources for this research included journal articles, reports, other scholarly sources, official websites, and other relevant online sources. pa ge 11 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 109-118, 2024 data collection the data collection process for this research involved an online survey questionnaire. the questionnaire was designed using google forms and was available in both khmer and english. khmer is the official language of cambodia, and english is a widely spoken foreign language in cambodia. participants received a link to the survey through social media and individual and group messaging platforms. the survey questionnaire consisted of a mix of open-ended and closed-ended questions to collect both qualitative and quantitative data. data analysis the collected data was analyzed using statistical software ibm statistical package for social sciences 27.0.1 (spss). the study focused on using descriptive statistics to summarize and describe the main features of the data. in addition to using descriptive statistics, multiple response analysis was also conducted for questions that allowed respondents to choose more than one answer. this analysis was used to understand the patterns and trends within the various responses. narrative summaries were also written to provide an overview of the main patterns and trends in the data and highlight any interesting or important results. overall, these approaches ensured that the results were easy to understand for both academic and non-academic audiences, allowing for easier interpretation and practical insights that could be easily applied in the workplace. research limitations and delimitations this study’s focus on phnom penh’s private sector may not reflect cambodia’s entire business environment. the online survey method and convenience sampling could introduce bias and limit the findings’ applicability. the sample size and representativeness might be affected by the employees’ availability and willingness to participate. results and discussion the provided data represented a frequency distribution of various types of employee compensation. with a total of 2590 responses from a sample size of 335, the data revealed that each individual could select more than one type of compensation, resulting in multiple responses per individual. this accounted for the discrepancy between the total responses and the sample size. it was also the reason why the percentages exceeded 100%, a common occurrence in surveys allowing multiple selections. the data revealed that 321 employees, accounting for 12.39% of the total responses and 95.82% of the sample, chose salary as their form of compensation. this suggested that the majority of employees received their compensation in the form of a salary. however, it was also worth noting that some employees might not have received a salary but instead received alternative benefits. following salary, paid time off such as sick leave, holiday leave, and annual leave were the next most common forms of compensation. sick leave was chosen by 286 employees, table 1: current compensation types received by employees multiple response analysis employee compensation responses percent of cases sample (n) percent salary 321 12.39 % 95.82 % overtime pay 165 6.37 % 49.25 % performance-based incentives 124 4.79 % 37.01 % seniority pay 166 6.41 % 49.55 % bonus 140 5.41 % 41.79 % annual leave 265 10.23 % 79.10 % holiday leave 270 10.42 % 80.60 % sick leave 286 11.04 % 85.37 % paternity leave 64 2.47 % 19.10 % maternity leave 84 3.24 % 25.07 % casual leave 94 3.63 % 28.06 % bereavement leave 90 3.47 % 26.87 % national social security fund (nssf) 193 7.45 % 57.61 % life insurance 114 4.40% 34.03 % dental health 18 0.69 % 5.37 % eyes care 20 0.77 % 5.97 % retirement benefits 148 5.71 % 44.18 % other: mental support 13 0.50 % 3.88 % other: certificate reward 15 0.58 % 4.48% total 2590 100.00 % 773.13 % pa ge 11 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 109-118, 2024 making up 11.04% of the total responses and 85.37% of the sample. holiday leave was selected by 270 employees, representing 10.42% of the total responses and 80.60% of the sample. annual leave was chosen by 265 employees, accounting for 10.23% of the total responses and 79.10% of the sample. these figures indicated that paid time off was a significant component of employee compensation, playing a significant role in promoting work-life balance among employees. overtime pay and seniority pay were also common, with 165 and 166 responses respectively, each accounting for around 6.37% and 6.41% of the total responses and 49.25% and 49.55% of the sample. the national social security fund (nssf) was chosen by 193 employees, representing 7.45% of the total responses and 57.61% of the sample. these forms of compensation are what the employees reported receiving from their companies that are additional pay for extra work and recognition of their tenure at the company. retirement benefits were received by 148 employees, making up 5.71% of the total responses and 44.18% of the sample. bonuses were chosen by 140 employees, accounting for 5.41% of the total responses and 41.79% of the sample. performance-based incentives were received by 124 employees, accounting for 4.79% of the total responses and 37.01% of the sample. life insurance was part of the compensation for 114 employees, making up 4.40% of the total responses and 34.03% of the sample. these benefits indicated that employees received both immediate rewards, such as bonuses, and long-term security, such as retirement benefits and life insurance. casual leave was selected by 94 employees, making up 3.63% of the total responses and 28.06% of the sample. bereavement leave was chosen by 90 employees, accounting for 3.47% of the total responses and 26.87% of the sample. maternity leave was chosen by 84 employees, representing 3.24% of the total responses and 25.07% of the sample. paternity leave was received by 64 employees, accounting for 2.47% of the total responses and 19.10% of the sample. these forms of compensation are flexible leave options, allowing employees to balance their professional responsibilities with personal obligations. the least common forms of compensation were eyes care, chosen by 20 employees (0.77% of responses, 5.97% of cases) and dental health, chosen by 18 employees (0.69% of responses, 5.37% of cases). moreover, in the questionnaire, there was an option labeled “other: (please specify)” for employees to provide their own responses. a significant portion of these responses were related to “mental support” and “certificate reward”. these responses were then categorized into two separate variables in spss for analysis. specifically, the category “other: certificate reward” was derived from responses related to certificate rewards. this category was selected by 15 employees, accounting for 0.58% of responses and 4.48% of cases. similarly, the category “other: mental support” was created from responses related to mental support. this category was chosen by 13 employees, accounting for 0.50% of responses and 3.88% of cases. this method of categorizing open-ended responses into specific variables is a common practice in data analysis, allowing for a more structured and simplified analysis of the data. while these forms of compensation were less common, they still represented important aspects of employee well-being and recognition. total respondents (n)=335 total percentage (%)=100% figure 1: employee satisfaction with current compensation package the majority of the respondents, comprising 156 individuals (46.57%), expressed satisfaction with their compensation package. additionally, 131 respondents (39.10%) reported a neutral stance. it’s noteworthy that some employees expressed dissatisfaction, with a few even indicating that they were very dissatisfied. although these numbers were smaller, they still represent significant concerns for the company. specifically, 28 respondents, accounting for 8.36% of the total, expressed dissatisfaction. furthermore, 4 respondents, representing 1.19% of the total, were very dissatisfied. on the other hand, a mere 16 respondents, representing 4.78% of the total, expressed being very satisfied with their compensation package. these findings suggest pa ge 11 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 109-118, 2024 a potential risk area for the company. although a majority of the respondents indicated satisfaction or high satisfaction with their compensation package, a significant proportion (nearly 50%) expressed neutral, dissatisfied, or very dissatisfied sentiments. this raise concerns as these employees may be considering leaving the organization due to their dissatisfaction with their compensation. the substantial percentage of neutral responses might indicate uncertainty or a lack of strong opinions regarding the compensation package. these employees have the potential to shift towards either satisfaction or dissatisfaction in the future, depending on changes in their compensation or their perception of it. it is crucial for the organization to take this feedback seriously and reassess their compensation strategies. to address these concerns, the organization should consider conducting further surveys or interviews to gain a deeper understanding of the specific aspects of the compensation package that dissatisfied employees find problematic. by doing so, they can make targeted improvements to enhance overall satisfaction and mitigate the risk of losing valuable employees. it is important to recognize that retaining satisfied employees is often more cost-effective than recruiting and training new ones. therefore, investing in improving employee satisfaction with their compensation can yield significant long-term benefits for the organization. total respondents (n)=335 total percentage (%)=100 the figure reveals that a significant proportion of respondents, 196 (58.51%), were uncertain about the competitiveness of their compensation packages. this uncertainty may arise from an insufficient understanding or lack of information regarding the market standards for compensation, as well as the specifics of their company’s compensation policy. it implies that these employees might not perceive their compensation as being commensurate with their effort and sacrifice. once these employees become aware of their compensation relative to the market, they might perceive their compensation as non-competitive or unfair, potentially leading them to consider leaving the organization for companies offering more competitive compensation. while 90 respondents (26.86%) believe their compensation is competitive, the presence of 49 respondents (14.63%) who do not share this belief is a serious concern. these respondents represent potential risks for employee turnover. the organization should proactively address these concerns. conducting a thorough market analysis to ensure their compensation packages are indeed competitive, and improving communication about compensation packages could help reduce uncertainty and increase the perceived value of the compensation packages among employees. for those who perceive their compensation as non-competitive, the organization should investigate the specific concerns of these employees and consider necessary adjustments to retain these employees and prevent potential turnover. a typical 5-point likert scale, respondents were asked to rate their level of agreement with a statement on a scale of 1-5, where 1 represent ‘strongly disagree (sd), 2 represents ‘disagree (d), 3 represents ‘neutral (ne)’, 4 represents ‘agree (a)’, and 5 represents ‘strongly agree (sa)’. figure 2: do employees believe their current compensation package is competitive compared to other private companies in phnom penh, cambodia? table 2: a typical 5-point likert scale questions 1 2 3 4 5 (sd) (d) (ne) (a) (sa) do you agree that your compensation package is important in your decision to stay with your current company? 1.19% 9.25% 36.72% 48.66% 4.18% respondents 4 31 123 163 14 total (n)=355 do you agree that your current company has a fair system for determining employee compensation? 2.09% 16.72% 41.79% 35.52% 3.88% respondents 7 56 140 119 13 total (n)=355 do you agree that compensation plays a vital role in retaining employees in your current company? 2.69% 9.85% 35.52% 42.09% 9.85% respondents 9 33 119 141 33 total (n)=355 pa ge 11 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 109-118, 2024 the data from the table 2 represents the responses of 335 individuals to three different questions related to their compensation packages. first “do you agree that your compensation package is important in your decision to stay with your current company?” a majority of the respondents, 163 (48.66%), agree that their compensation package is important in their decision to stay with their current company. this is followed by 123 respondents (36.72%) who are neutral about the importance of their compensation package in their decision to stay. a smaller number of respondents, 31 (9.25%), disagree, while 4 respondents (1.19%) strongly disagree. lastly, 14 respondents (4.18%) strongly agree that their compensation package is important in their decision to stay with their current company. second “do you agree that your current company has a fair system for determining employee compensation?” the largest group of respondents, 140 (41.79%), are neutral about the fairness of their company’s system for determining employee compensation. this is followed by 119 respondents (35.52%) who agree that their company has a fair system. a significant number of respondents, 56 (16.72%), disagree, while 7 respondents (2.09%) strongly disagree. lastly, 13 respondents (3.88%) strongly agree that their company has a fair system for determining employee compensation. third “do you agree that compensation plays a vital role in retaining employees in your current company?” the largest group of respondents, 141 (42.09%), agree that compensation plays a vital role in retaining employees. this is followed by 119 respondents (35.52%) who are neutral about the role of compensation in employee retention. a smaller number of respondents, 33 (9.85%), disagree, while 9 respondents (2.69%) strongly disagree. lastly, 33 respondents (9.85%) strongly agree that compensation plays a vital role in retaining employees. in term of discussion, the first question indicates that employees who respond with agree or strongly agree view the compensation package as a key factor in their decision to remain with their current company. this positive response suggests that these employees find the compensation package acceptable and satisfying. however, those who respond with neutral, disagree, or strongly disagree likely feel that their compensation packages are inadequate and do not meet their needs. the second question shows that employees who responded neutrally are uncertain about the fairness of their compensation packages within the company’s system. in contrast, those who responded with agree or strongly agree feel that their compensation packages are distributed fairly. however, those who responded with disagree or strongly disagree express a sense of unfairness in their compensation. this could be due to issues in the implementation of employee compensation, corruption within the company, labor exploitation, and other related problems. the third question indicates that a significant number of employees view compensation as crucial in retaining them in their current company, as shown by those who responded with agree or strongly agree. however, those who responded with disagree or strongly disagree feel that their compensation does not play a significant role in employee retention. this perception may arise from employers not adequately compensating for employees’ efforts, leading to an imbalance that leaves employees feeling unappreciated. notably, a significant number of employees responded neutrally, indicating their uncertainty about the value of compensation to them. the high number of neutral responses across all three questions is a significant concern. these respondents are in an uncertain situation, unsure whether they agree or disagree. this uncertainty could lead to feelings of discomfort and a lack of connection with the company, which could subsequently increase the possibility of them leaving the company. the significant number of neutral and disagreeing respondents suggests that the company needs to reassess its compensation strategies and communication to ensure that employees feel valued and fairly compensated, which is essential for retaining talented personnel. conclusion the study provides valuable insights into the role of compensation in employee retention within private companies in phnom penh capital city, cambodia. the data reveals that a significant number of employees view their compensation package as a key factor influencing their decision to remain with their current employer. however, the study also reveals a substantial proportion of employees expressing dissatisfaction or neutrality towards their compensation packages, signaling potential areas for improvement. the findings suggest that while salary and paid time off are the most common forms of compensation, other benefits such as performance-based incentives, retirement benefits, and life insurance also play a crucial role in employee satisfaction. however, the presence of a significant number of employees who are uncertain about the competitiveness of their compensation packages and the fairness of the company’s compensation determining system indicates a need for improved communication and transparency. moreover, the research emphasizes the necessity of acknowledging and addressing employee concerns about compensation. a notable percentage of employees expressed dissatisfaction or neutrality towards their compensation packages, which could potentially escalate turnover rates. consequently, it is imperative for companies to reevaluate their compensation strategies to ensure they are competitive, equitable, and transparent. in conclusion, this study underscores the importance of a comprehensive and fair compensation package in pa ge 11 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 109-118, 2024 retaining employees. it calls for companies to continually reassess and improve their compensation strategies, taking into account the diverse needs and expectations of their employees. by doing so, companies can enhance employee satisfaction, foster loyalty, and ultimately, achieve better employee retention. recommendations based on the findings, this research proposes the following actionable recommendations. improve transparency companies should strive to enhance transparency and communication about their compensation packages. this could alleviate uncertainty among employees and augment their perceived value of the compensation packages. conduct regular market analysis companies should undertake regular market analysis to ascertain that their compensation packages remain competitive. this could aid in retaining employees who might otherwise contemplate leaving for companies offering more competitive compensation. address employee concerns companies should proactively address employee concerns about their compensation. this could entail conducting additional surveys or interviews to gain a more profound understanding of the specific aspects of the compensation package that employees find problematic. reassess compensation strategies companies should reevaluate their compensation strategies to ensure they are fair and cater to the diverse needs of their employees. this could involve contemplating alternative forms of compensation, such as performance-based incentives, retirement benefits, and life insurance. invest in employee satisfaction companies should invest in enhancing employee satisfaction with their compensation. this could involve implementing targeted improvements to boost overall satisfaction and mitigate the risk of losing valuable employees. consider employee feedback companies should take employee feedback into account when reassessing their compensation strategies. employees who are dissatisfied with their compensation could provide valuable insights into how the compensation packages could be improved. future research there are several areas identified for future research comparison across industries future research could also compare compensation practices and their impact on employee retention across different industries. this could provide valuable insights into industry-specific trends and practices. longitudinal study a longitudinal study tracking the same employees over time could provide insights into how changes in compensation impact employee retention and job satisfaction. specific aspects of compensation packages future research could delve deeper into the specific aspects of compensation packages that employees find most valuable. this could involve conducting surveys or interviews to gain a more profound understanding of employee preferences and expectations. acknowledgements i’m deeply grateful to my supervisor, prof. dr. guanghui fu, for his guidance and support. his expertise significantly shaped this study. i’m thankful to nanjing tech university for the research opportunity. the university’s supportive environment was crucial for my learning, personal growth, and research success. i appreciate the respondents who shared their time and insights, making this study possible. lastly, my family’s constant support and encouragement were my driving force throughout this journey. references central intelligence agency. 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(2022). doing business with cambodia peo/ eor: how to hire in cambodia. multiplier. https:// www.usemultiplier.com/cambodia/peo-employer-ofrecord world bank. (2023). overview: development news, research, data. world bank cambodia. https://www. worldbank.org/en/country/cambodia/overview annexures survey questionnaires 1. what are your current compensations that you receive from your current company? please carefully read the description and question. this question allows for multiple responses, meaning you may select more than one answer that applies to you. additionally, you have the option to provide an openended response in the ‘other (please specify)’ area where you can express your opinion or add any information not covered by the provided options. 2. how satisfied are you with your current compensation package? please carefully read the question and select the answer that best applies to you. you may only select one answer. 3. do you believe your current compensation package is competitive compared to other private companies in phnom penh, cambodia? please carefully read each question and select the answer that best applies to you. you may only select one answer. 4. in a typical 5-point likert scale, respondents are asked to rate their level of agreement with a statement on a scale of 1-5, where 1 represents ‘strongly disagree’, 2 represents ‘disagree’, 3 represents ‘neutral (neither agree nor disagree)’, 4 represents ‘agree’, and 5 represents ‘strongly agree’. please carefully read the description and questions. then select the answer that best applies to you. you may only choose one answer per question. pa ge 11 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 109-118, 2024 questions statement 1 2 3 4 5 do you agree that your compensation package is important in your decision to stay with your current company? do you agree that your current company has a fair system for determining employee compensation? do you agree that compensation plays a vital role in retaining employees in your current company? pa ge 1 pa ge 33 american journal of applied statistics and economics (ajase) fdi, technology transfer and economic growth, what’s the connection? the case of morocco dabnichi youness1*, ferroud abderrahim1 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2276 https://journals.e-palli.com/home/index.php/ajase article information abstract received: november 30, 2023 accepted: december 27, 2023 published: december 31, 2023 foreign direct investment (fdi) has grown significantly in developing countries over recent decades, and governments in these countries now regard fdi as a key component of their development strategy. the potential benefits of fdi include the provision of financial resources, job creation, increased economic growth and spillover effects on local businesses. in addition, the development effectiveness of fdi depends on the ability of host countries to absorb technology and innovation from foreign companies. technology transfer (tt) is therefore a major issue in the context of fdi. political institutions and economic players need to work together to encourage effective and sustainable technology transfer. technology transfer is therefore an essential process in enabling companies to remain competitive and innovate in a constantly changing economic environment. technology transfer centers play a crucial role in this process, facilitating the exchange of knowledge and technology between the various players in the innovation ecosystem. after examining the two variables, foreign direct investment (fdi) and tt on economic growth, the results indicate that both variables have a positive impact on economic growth keywords foreign direct investment, economic growth, technology transfer, morocco 1 faculty of economics and management, settat, morocco * corresponding author’s e-mail: y.dabnichi@uhp.ac.ma introduction over the past few decades, we have witnessed a gradual evolution in the policies of governments in developing countries (dcs) regarding foreign direct investments (fdi). in the 1950s and 1960s, dcs were wary of multinational corporations (mncs) and feared heir presence could harm their sovereignty and economic development. however, starting in the 1970s, there was a growing realization of the potential role of fdi as a development catalyst, especially due to the experiences of some countries that successfully attracted fdi and reaped economic benefits from it. in the 1980s and 1990s, dcs progressively adopted more fdi-friendly policies by liberalizing investment conditions and offering tax and regulatory incentives to mncs. however, the liberalization of investment policies also came with risks and challenges for dcs, such as loss of control over natural resources and increased dependence on foreign investors. therefore, the governments of dcs need to design investment policies that take into account the potential benefits and risks of fdi while seeking to maximize economic and social returns for their country. foreign direct investment (fdi) can have a significant impact on the economic growth of host countries by improving total factor productivity, which is the efficiency with which resources are used to produce goods and services. the mechanisms contributing to this improvement include the links between fdi flows and international trade, beneficial externalities for local businesses, and direct effects on the structural factors of the host economy. beneficial externalities for local businesses are also an important factor. the presence of a multinational corporation can lead to improved infrastructure quality, increased training and expertise of local workers, as well as greater diffusion of technologies and innovative business practices. indeed, technology transfer is a complex and dynamic process involving multiple actors, such as technology holders, stakeholders, end-users, regulators, governments, etc. the success of technology transfer depends on several factors, including the quality of the technology, the company’s ability to transfer it effectively, the skills and capacity of the recipients to absorb and apply it, existing regulations, government policies, environmental and socio-economic constraints, and more. throughout this paper, we provide a summary of the literature dedicated to the relationship between incoming fdi flows and the spillover effects they may generate in developing countries (dcs). we aim to shed light on the key current controversies in the field. we particularly emphasize the concepts of “absorptive capacity” and “innovation” that could explain the mixed results regarding the presence of positive spillover effects in dcs. through this modest study, our aim is to address the following question: “how can we emphasize the role of fdi in the technology transfer process as a catalyst for economic growth?” with this research question in mind, we can formulate the following sub-questions, which will serve as the guiding framework for our article: the present paper comprises three main axes that will guide our study effectively: question 1: what is technology transfer through fdi? question 2: how can we improve the moroccan environment to attract fdi for effective technology pa ge 34 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 33-43, 2024 transfer? question 3: can fdi impact economic growth through technological progress rather than capital accumulation? this document is organised into three main parts, which will help us to carry out our study effectively. the first point will focus on the theoretical foundations of the key concepts of our subject. literature review fdi, technology transfer, and economic growth: in this section, we will review existing literature that explores the relationship between foreign direct investment (fdi), technology transfer, and economic growth. the second point will focus about the link between the key concepts of our subject. interaction between fdi, technology transfer, and economic growth here, we will delve into the dynamics and interactions between fdi, the transfer of technology, and their impact on economic growth. the tird point will be the subject of an econometric study, which will be used to investigate the empirical link between the variables studied, after emphasising the methodology to be adopted in this study. econometric analysis of the relationship between the studied variables this section will involve an econometric analysis to examine the quantitative relationship between the variables under study, providing empirical insights into the topic. literature review: fdi, technology transfer, and economic growth this section will be dedicated to presenting foreign direct investment (fdi) from various perspectives, including its definition, forms, consequences, and determinants. foreign direct investment definition fdi has been defined in various ways, and some notable definitions include: according to the definition provided by the imf, “foreign direct investment is made with the intention of acquiring a lasting interest in an enterprise operating in an economy other than that of the investor, with the objective of having a significant degree of influence on the management.” according to the oecd, fdi is “an activity in which an investor resident in one country obtains a significant interest and influence in the management of an entity in another country. this operation may involve creating an entirely new enterprise (greenfield investment) or, more commonly, changing the ownership status of existing businesses (through mergers and acquisitions). other financial transactions between related enterprises, including reinvestment of profits from the enterprise receiving the fdi or other capital transfers, are also defined as foreign direct investment3.” according to the imf and oecd definitions, direct investment reflects the aim of obtaining a lasting interest by a resident entity of one economy (direct investor) in an enterprise that is resident in another economy (the direct investment enterprise). the “lasting interest” implies the existence of a long-term relationship between the direct investor and the direct investment enterprise and a significant degree of influence on the management of the latter. direct investment involves both the initial transaction establishing the relationship between the investor and the enterprise and all subsequent capital transactions between them and among affiliated enterprises4, both incorporated and unincorporated5. forms of fdi various forms of fdi offer advantages and disadvantages, depending on the objectives, needs, and capabilities of the involved companies, as well as the economic, political, and regulatory conditions of host countries. they also require a careful assessment of the risks, costs, and benefits associated with each option. the literature on foreign direct investment (fdi) offers a multitude of typologies based on different theoretical frameworks and research objectives. for the purposes of this study, we will focus on three major axes: (1) greenfield table 1: different forms of fdi form significance greenfield investments greenfield investments occur when foreign companies make significant investments in establishing new production capacities or expanding existing ones in the host country. host nations highly value these investments, especially when aimed at addressing high unemployment rates. greenfield investments become a central focus of a host nation’s promotional efforts because they bring about new production capabilities, job opportunities, technology transfer, and global market connections. from a human capital perspective, greenfield foreign direct investment (fdi) typically creates new employment opportunities and enhances productivity. despite the positive reception of greenfield investments in host countries, it is essential to acknowledge that they may potentially displace local businesses and certain industries, especially those heavily reliant on technology. while profits from local companies circulate within the domestic market, the same may not always be true for foreign companies engaged in greenfield investments. in the context of kosovo, where high unemployment is a prevailing concern, this type of fdi, along with similar sub-types, is warmly embraced. pa ge 35 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 33-43, 2024 investments, symbolising the establishment ex nihilo of a foreign entity, (2) mergers and acquisitions (m&a), representing the transfer of ownership of existing assets, and (3) joint ventures, illustrating the collaboration between local and foreign investors. technology transfer definition technology transfer is the introduction and adoption of new (typically more advanced) methods of production and equipment that are already in use in other regions. this transfer can be intellectual (methods, concepts) or geographical (physical equipment). technology transfer is the process by which technology, knowledge, or expertise (including hardware, software, organizational methods, etc.) developed by one party in a project or agreement is conveyed to another. technology transfer (tt) is a process in which an industrial actor acquires technology from a public entity or another private company, usually with the intention of commercializing it. types of technology transfer technology transfer refers to the process by which technology, knowledge, or skills are transmitted from one company or country to another. here are five different types of technology transfer: horizontal technology transfer this involves the transfer of technology between companies or organizations engaged in similar activities. for example, a mobile phone manufacturer may transfer production technology to another mobile phone manufacturer. vertical technology transfer this involves technology transfer between companies or organizations that engage in different activities but are linked by a value chain. for example, an electronic components supplier can transfer technology to a mobile phone manufacturer. technology transfer through research and development this is the transfer of technology resulting from research and development of new technology. companies can transfer internally developed technology to other companies that can use it in their own products or services. technology transfer through licensing this is the transfer of technology in which a company holding patents or intellectual property rights grants another company, the right to use the technology in exchange for royalties or licensing fees. technology transfer through strategic alliances this is the transfer of technology resulting from strategic alliances between companies that collaborate to develop new technology or improve existing technology. companies can share knowledge and skills to jointly develop new technology. economic growth definition economic growth refers to the positive change in the production of goods and services in an economy over a given period, typically a long one. it is a concept used to measure economic activity through indicators such as mergers and acquisitions mergers and acquisitions (m&a) are typically carried out when existing assets are transferred from a local company to a foreign company. in other words, the assets and operations of companies in different countries are combined to create a new legal entity. countries with lower levels of development are likely to have fewer opportunities for m&a actions. according to the ipak (2012) annual survey on fdi perceptions, compared to greenfield investments, m&a “requires a “there is no long-term benefit to the economy.” “the money from the sale never reaches the local economy” (p.9). the most noted benefit of this type of he fdi is increased labor productivity, but we find less evidence regarding employment growth. empirical studies in this direction are inconclusive and contradictory. joint ventures joint ventures can involve a local company, government or a foreign company operating in the host country. cross-border joint venture is one in which economic entities from at least two countries are involved. one positive spillover in terms of human capital is technical spillover especially when there is a combination of foreign and local company. according to dunning and lundan (2008), one of the main factors “influencing the viability and success of cross-border joint ventures concerns the choice of partner and reciprocal trust between partners” (p.273). rather than profit gain, there are different factors and motives behind joint ventures. according to the model of casson (2000), formation of joint ventures has nine factors such as: economies of scale, market size, economies of scope, technological uncertainty, technological change, cultural difference, interest rates, protection of autonomy and missing patent rights (casson, 2000). the significance of human capital development in joint ventures varies in developed countries compared to transition and undevelopment countries. source: prepared by the authors pa ge 36 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 33-43, 2024 gross domestic product (gdp) growth and an increase in per capita income. according to françois perroux, growth is “the sustained increase over one or more long periods of a dimension indicator: for a nation, the overall net product in real terms .” he also noted that “no observed growth is homothetic; growth occurs within and through structural changes.” theories of economic growth are economic models that seek to explain the origin and causes of economic growth. they have evolved over time, transitioning from exogenous growth models to endogenous growth models. a theory of economic growth helps understand the determinants of a country’s growth and why some countries experience stronger economic growth than others. theories of economic growth various theories of economic growth have emerged over time. among them are mercantilist, classical, neoclassical, spontaneous order, and monetarist theories. each theory seeks to understand the economy and proposes models to maximize economic growth. the theories of economic growth study the sources and mechanisms of sustained and lasting increases in production in an economy over an extended period. these theories aim to explain the factors contributing to economic growth and understand the causes of this growth. here is an overview of the different theories of economic growth: exogenous growth models these models explain economic growth by focusing on factors external to the economy itself. among these factors are population growth and technological progress. in these models, growth is considered to be exogenous, meaning it does not depend on internal economic variables. new growth theories these theories draw inspiration from older schools of economic thought, such as classical, keynesian, and neoclassical economics. they focus on whether sustainable economic growth is possible and under what conditions it can be achieved. the work of two economists, nicholas kaldor and joseph schumpeter, has had a significant influence on these new theories. endogenous growth this theory emphasizes factors within the economy that contribute to economic growth. it highlights the role of investment, research, human capital, and infrastructure. according to this theory, economic growth can be sustained through capital accumulation and productivity improvement. the work of researchers such as paul romer, robert lucas, and robert barro has contributed to the development of this theory. interaction between fdi, technology transfer, and economic growth fdis are now recognized as a privileged channel for technology transfer, knowledge accumulation, and knowhow. technology is seen as a powerful driver in shaping the productive landscape of a host country. positive externalities or “spillovers,” as noted by blomstrom (1986), occur through the mobility of skilled personnel, subcontracting relationships, or the reduction of productive inefficiencies through competition. the presence of these spillovers is supported by the positive correlation between fdis and productivity indicators, established through cross-sectional studies (caves, 1974), (globerman, 1979), assuming that the presence of mncs promotes the efficiency improvement of domestic firms. the first objective of this study is to understand whether technology transfer has taken place in a country like morocco. in macroeconomic studies, it is very difficult, figure 1: the role of fdi in developing host country industry and workers source: jbic 2002 if not impossible, to observe technology transfer directly. this is why previous studies have tended to use an indirect measure of technology transfer. one of the best measures of the presence of technology transfer is economic growth. the argument is that economic growth is due to technological improvements. economic growth has fascinated economists and philosophers for hundreds of years, and previous research or discussion on the subject pa ge 37 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 33-43, 2024 can be divided into three categories: classical, neoclassical and modern. the researcher offers an analysis of each of the three types of research on economic growth in the context of fdi. mncs are likely to disseminate advanced technologies to the local industrial fabric for several reasons. in general, mncs can transfer these advanced technologies to their foreign subsidiaries, including those in developing countries, to enhance their competitiveness in the global market. furthermore, mncs can transfer their organizational and managerial know-how to foreign subsidiaries. this may include skills in supply chain management, human resource management, product development, and marketing. these skills can be crucial for local businesses seeking to enhance their efficiency and competitiveness in the global market. finally, mncs can also disseminate advanced technologies to the local industrial fabric through interactions with local companies. mncs often have the ability to form strategic alliances with local companies to share knowledge and skills or to develop new and improved technologies. overall, mncs play a crucial role in the diffusion of advanced technologies to the local industrial fabric. their presence and their ability to transfer organizational and managerial know-how, as well as r&d skills, can help local businesses improve their efficiency and competitiveness in the global market. furthermore, fdi represents a common means of intrafirm technology transfer. nowadays, most international licensing for manufacturing takes place between parent companies and their foreign subsidiaries. additionally, on a global scale, the majority of private r&d activities are conducted by mncs. technology transfer as a source of convergence technology transfer is a crucial mechanism for the economic development of developing countries. foreign direct investment (fdi) flows are one of the primary channels for transferring foreign technology. economists generally recognize an overall positive effect of fdi on the economic growth of developing countries, but there are important nuances and a variety of situations. multinational corporations (mncs) play a key role in transmitting foreign technology to host economies. the spillover effects of fdi occur when local companies benefit from the technological knowledge, management skills, or markets that mncs possess. this can happen without local companies having to bear the costs of developing or acquiring these skills and knowledge (kokko, 1994). technology transfer through fdi is an important mechanism for the economic development of developing countries. absorptive capacity as a prerequisite for technology transfer narula and marin (2003) have emphasized that absorptive capacity also involves the ability to internalize knowledge created by others and adapt it to one’s own uses and processes. this requires the ability to identify technology transfer opportunities, establish partnerships and collaborations with other actors, and effectively manage the transfer process. abramovitz (1991) defines two variables that determine to what extent technologically lagging firms in a country will catch up. absorptive capacity is an essential concept in the context of technology transfer. it represents the ability of a company or a country to assimilate and effectively use external technological knowledge to enhance its own technological and productive capabilities. figure 2: the national environment for innovation and technological diffusion source: developed by the authors absorptive capacity and spillovers the absorptive capacity among domestic firms appears to be a necessary condition for benefiting from the positive spillover effects of fdi. in this regard, kumar and pradhan (2002) emphasize that a more favorable effect of fdi on a host economy is closely related to pa ge 38 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 33-43, 2024 the diffusion of externalities or spillovers to local firms by multinational corporations. according to unctad, “to achieve sustainable economic development, it is not enough to open the door and wait for new techniques to arrive. national companies must constantly strive to improve their technological level, and public authorities must support them.” human capital: a vital component of absorptive capacity absorptive capacity is the ability of a company, sector, or country to assimilate and effectively use technological knowledge from external sources. it depends on several factors, including the quality of technological infrastructure, corporate culture, regulatory environment, and adaptability. studies have shown that absorptive capacity is largely dependent on the level of human capital in the host country. companies and workers with higher levels of education and skills are better prepared to assimilate new technological knowledge and apply it in their daily work. kindrick (1981) recognized that the adoption and adaptation of foreign technology may require a country to engage in r&d to develop its absorptive capacity. trade openness as a support for technology transfer a country’s trade openness can be a key factor in technology transfer and the productivity of its companies. authors grossman and helpman (1991)point out that trade openness can enhance a country’s ability to absorb knowledge and apply it, especially by allowing imitation and learning from abroad. authors bouoiyour and toufik (2007)23 conducted a study on the impact of foreign direct investment (fdi) on the productivity of moroccan companies. they found that the presence of fdi in morocco’s manufacturing industries had a positive effect on the productivity of local companies, especially in lowtech sectors. however, the effect was less pronounced in high-tech sectors. trade openness can be a key factor in technology transfer and the improvement of local business productivity, but it depends on the level of development of the host country’s human capital and its ability to absorb and effectively apply foreign knowledge. fdi, technology transfer, and economic growth economic policies aimed at attracting fdi are based on the idea of capturing technological externalities, but empirical studies show that the effect is not always positive and must be subject to discussion. the trade-off between funds and efforts spent on attracting fdi, on one hand, and the benefits generated, on the other hand, is far from settled. thus, other studies will show that in reality, fdi and trade have a negative effect or at least no significant effect on economic growth. technology transfer has become a priority in the overall strategy of developing countries, which lack sufficient capabilities for autonomous development and face significant technological lag. in this perspective, foreign direct investment (fdi) appears to be one of the means for these countries to stimulate growth and benefit from “technology and know-how transfer.” figure 3: links between fdi and economic growth source: makin and chai 2018 econometric analysis of the relationship between the studied variables and interpretation of results empirical literature on fdi and economic growth before moving on to present the methodology adopted in this study, it is considered necessary to briefly present the empirical studies that have dealt with the relationship between the variables studied in our study. pa ge 39 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 33-43, 2024 table 2: empirical literature on fdi and economic studies that have found a positive relationship studies that did not find a relationship studies that have found a négative relationship de meilo 1999). nair-reichert & weinhold (2001). campos & kinoshita (2002). aifaro et al. (2004), basu et al. (2001). lensink & morrissey (2006). ljunwai & ii ( 2007). alguacii et al.(201 i). anwar& cooray (2012). roy & mandai (2012), gursoy & kalyoncu (2012) ericsson & irandoust (2001),carkovic & levine (2002,2005),zhang(2001),hermes & lensink (2003) saltz(1992). bendenabende et al. (2000), alfaro (2003), mencinger (2003). darrat et al. (2005). ang 20o9). alfaro et al (2010). wang & wong (2011) source: https://ebrary.net/100486/business_finance/finance_technology methodology of studie the methodology employed in this article titled “technology transfer and economic growth: what’s the connection? the case of morocco” is quantitative and based on a hypothetico-deductive approach. we defined and examined our variables in advance in the first part of our paper to establish the various potential links between them before proceeding to validate these relationships through econometric analysis. data collection: we collected our data from reliable sources such as the world bank and the high commission for planning (hcp) of morocco. this data includes information on foreign direct investment (fdi), technology transfer indicators (tt), and the economic growth of morocco. econometric analysis: subsequently, we conducted an econometric analysis using an appropriate statistical model. our analysis is based on a set of variables studied to assess the impact of fdi and tt on economic growth. this analysis is at the core of our article and will be detailed. interpretation and discussion of results: after completing our econometric analysis, we interpreted and discussed the results obtained. this step is essential to validate the relationship between the various variables studied for the case of morocco. we examined the direction and strength of the relationships, as well as the magnitude of the impact of fdi and tt on moroccan economic growth. econometric analysis of the relationship between the studied variables résultats of stady stationarity tests can reduce the risk of spurious regressions. in this regard, we consider the proposed tests of augmented dickey-fuller (adf) and phillips-perron (pp). table 3: adf and pp stationarity test results adf (% 5) phillips-perron (% 5) variable niveau (intercept) 1ère. différence (intercept) niveau (intercept) 1ère. différence (intercept) niveau lcr -0.955675 -4.044859 -0.540275 -6.370140 i (1) -3.673616 -3.690814 -3.673616 (-3.690814 l fdi -7.269069 -3.791931 -6.867604 -25.06467 i (0) -3.673616 -3.710482 -3.673616 -3.690814 ltt -2.028799 -4.509680 -2.077308 -7.753299 i (1) -3.673616 -3.690814 -3.673616 -3.690814 source: developed by the authors based on the outputs of eviews 10 software according to the results presented in the previous table, we find that the coefficient of determination (r2) exceeds 90%, which means that the chosen explanatory variables do indeed have an impact on the dependent variable. furthermore, in terms of the statistical tests that help diagnose and analyze the estimated ardl model, namely the breusch-godfrey serial correlation test (lm) and the durbin-watson (dw) test, they confirm the presence of serial correlation if the probability associated with the f-lm statistic is greater than 0.05. however, in our case, this is not true, as the probability associated with the f-lm statistic is equal to 0.23, indicating an absence of autocorrelation. similarly, for the arch heteroskedasticity detection test, the probability is equal to 0.53, indicating an absence of heteroskedasticity. according to this figure presenting the 20 estimated models selected by the aic selection criterion, we can observe that the optimal model in our case is the ardl (2,3,3) model. pa ge 40 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 33-43, 2024 figure 4: akaike test figure 5: cusum stability test cusum stability tests the two figures above, cusum and cusum square, are used to analyze the stability of the dependent variable over time, especially during the evaluated study period. we can observe that the variable being explained is stable during the study period because its evolution remains within the confidence interval marked in red. according to table 4 of the pesaran et al. cointegration test, we can observe that the calculated f-statistic, which is equal to 8.543578, exceeds the upper bounds of the various critical thresholds. this indicates that there is cointegration among the variables under study, meaning there are both short-term and long-term equilibrium relationships. figure 6: cusum square stability test pa ge 41 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 33-43, 2024 table 4: results of pesaran et al.'s cointegration test calculated f-statistic 8.543578 critical threshold lb26 ub27 10% 2.63 3.35 5% 3.1 3.87 2.5% 3.55 4.38 1% 4.13 5 source: compiled by the authors, based on the cointegration test by pesaran et al table 5: short-term dynamic variable coefficient std. error t-statistic prob. d(log fdi (-1)) -0.067366 0.032516 -2.071785 0.0837 d(logtt) 0.632801 0.092858 6.814751 0.0005 cointeq(-1)* -0.996210 0.139141 -7.159711 0.0004 source: developed using eviews 10 by the authors table 6: long-term dynamics” variable coefficient std. error t-statistic prob. log fdi 0.044299 0.079572 0.556712 0.5979 logtt 1.273179 0.183652 6.932580 0.0004 c 2.783909 0.370346 7.517046 0.0003 source: developed using eviews 10 by the authors based on the results obtained, we find that the variable logide is significant at the 10% level, while the variable logtt is significant at the 1% level. in statistical terms, the cointegration coefficient is equal to (-0.996), and this result is negative and statistically significant, indicating the presence of a long-term relationship. the elasticity of logide is (-0.067), which means that a 1% increase in this explanatory variable will lead to a 0.067% decrease in the dependent variable studied, indicating a negative effect. as for the variable tt, it is positive, reflecting that a 1% increase in this variable will result in a successive increase of 0.63% in the variable under investigation, which is economic growth. discussion in the long term, we observe in our model that the variable tt is significant at the 1% level with a probability of 0.0004, while the variable fdi is not significant at the 10% level. there is a positive impact of both variables studied on economic growth. the first variable, fdi (foreign direct investment), increases growth by 0.044299, while the variable tt (technology and knowledge transfer) is estimated at 1.273179, indicating a positive influence. after examining the two variables, foreign direct investment (fdi) and tt on economic growth, the results indicate that both variables have a positive impact on economic growth. more specifically, fdi increases growth by 0.044299 (meaning that each increase of one unit of fdi leads to an increase of 0.044299 units of economic growth), while tt has a positive effect estimated at 1.273179 (implying that each increase of one unit of tt leads to an increase of 1.273179 units of economic growth). these results suggest that foreign direct investment and tt are important factors in stimulating economic growth. however, it is important to keep in mind that the results of statistical analysis are not always conclusive, and other factors may influence economic growth. foreign direct investment (fdi) and tt (technology and knowledge transfer) are two important variables that affect economic growth. fdi is an investment made by a foreign company in a local business or physical asset, while tt refers to the transfer of technical and technological knowledge from one company or country to another. fdi can have a positive effect on economic growth by bringing foreign capital, technology, and skills. it can also contribute to job creation and infrastructure development in the host country. however, fdi can also have negative effects, such as economic dependency on foreign investors, reduced local competition, and profit outflows. similarly, tt can also have a positive impact on economic growth by improving productivity, stimulating innovation, and enhancing the competitiveness of businesses. however, tt can also have negative effects, such as reduced demand for low-skilled labor and the creation of economic inequalities between technology-owning and non-owning countries. pa ge 42 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 33-43, 2024 ultimately, the impact of fdi and tt on economic growth depends on many factors, such as the economic policies of the country, local technological capabilities, and international trade relations. a proper combination of these two variables can lead to sustainable and balanced economic growth.” conclusion this study focuses on the relationship existing between foreign direct investments, technology transfer and economic growth for morocco. the positive effects of foreign direct investment (fdi) on the local economy are not automatic and depend on several factors. public policies play a crucial role in creating a favorable environment for the absorption and diffusion of modern technologies. investments in education, innovation, and training can improve internal absorption capabilities, while policies that encourage cooperation between multinational corporations (mncs) and local actors can facilitate the diffusion of modern technologies to the local economy. furthermore, policies aimed at improving transportation and communication infrastructure, combating corruption, and strengthening institutions can help create a political and macroeconomic environment conducive to the positive spillover effects of fdi. fdi and technology transfer can play a significant role in morocco’s economic growth. indeed, fdi can contribute to the influx of capital, technology, and skills needed for the country’s economic development. technology transfer, on the other hand, can enable moroccan companies to benefit from recent technological advancements and thus improve their productivity and competitiveness in international markets. morocco has adopted an economic openness policy since the 1990s, aiming to attract foreign investment and promote technology transfer. this policy has led to a significant increase in fdi in morocco, especially in the automotive, aerospace, agri-food, and textile industries. however, despite this economic openness and the increase in fdi, morocco still faces significant challenges in terms of technology transfer. moreover, the low level of technical skills among moroccan workers can make it difficult for local companies to assimilate new technologies. to address these challenges, morocco has implemented policies aimed at encouraging technology transfer, including tax incentives and training programs for workers. the government has also encouraged partnerships between local and foreign companies to promote the transfer of knowledge and skills. in conclusion, it can be said and confirmed that fdi and technology transfer can play an important role in morocco’s economic growth. however, to maximize the benefits of these factors, it is essential to implement effective policies aimed at promoting technology transfer and developing the technical skills of local workers. perspectives based on the conclusion of our article, here are some recommendations for future researchs: policy implications evaluate the existing economic policies related to foreign direct investment and technology transfer. consider whether adjustments or new policies are needed to maximize the positive impacts and mitigate potential negative consequences. regulatory framework assess the regulatory framework governing foreign direct investment and technology transfer. consider whether there is a need for more stringent regulations or incentives to ensure responsible and sustainable practices. technology capacity building investigate strategies for enhancing the local technological capabilities to better absorb and adapt foreign technologies. this could involve investment in education, research and development, and fostering an environment conducive to innovation. labor market considerations examine the effects of foreign direct investment and technology transfer on the labor market. explore policies that can address potential disparities, such as training programs for displaced workers or initiatives to promote skill development. sustainable development goals (sdgs) align the analysis with the sustainable development goals, considering how foreign direct investment and technology transfer can contribute to achieving specific sdgs, such as decent work, economic growth, and innovation. comparative studies conduct comparative studies across countries with different economic structures and policies to identify best practices and lessons learned. this can provide valuable insights for policymakers seeking to optimize the benefits of foreign direct investment and technology transfer. long-term impact assessment investigate the long-term impact of foreign direct investment and technology transfer on economic growth. assess whether the initial positive effects are sustained over time and identify any emerging challenges. public-private partnerships explore the role of public-private partnerships in facilitating responsible foreign direct investment and technology transfer. assess how collaboration between governments and private entities can lead to mutually beneficial outcomes. pa ge 43 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 33-43, 2024 risk management strategies develop risk management strategies to address potential downsides of foreign direct investment, such as economic dependency and profit outflows. consider mechanisms to balance the interests of foreign investors and the host country. global trade relations examine the influence of global trade relations on the effectiveness of foreign direct investment and technology transfer. analyze how changes in international trade dynamics may impact the success of these economic strategies. these recommendations can serve as a starting point for further research and policy considerations, helping to refine and optimize the role of foreign direct investment and 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(2000). higher education and economic development in china. higher education, 39. pa ge 1 pa ge 51 american journal of applied statistics and economics (ajase) evaluating the efficacy of supervised machine learning models in inflation forecasting in sri lanka w. m. s. bandara1*, w. a. r. de mel1 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2385 https://journals.e-palli.com/home/index.php/ajase article information abstract received: january 01, 2024 accepted: february 09, 2024 published: february 12, 2024 this study aims to forecast the inflation rate using supervised machine learning models (smlm). while smlms are widely used in various fields, they have not been widely applied in forecasting inflation rates. therefore, the main objective of this study is to identify the best model for forecasting inflation among four different smlms: lasso regression (lr), bayesian ridge regression (brr), support vector machine regression (svr), and random forest regression (rfr) models. to achieve this objective, two different types of crossvalidation techniques were employed: the k-fold cross-validation method (cvk) and walk forward validation (wfv) methods. these techniques were used to estimate the parameters and hyper-parameters for each machine learning model with root mean square error. the mean absolute percentage error (mape) was used to compare the performance of the different smlms. empirical evidence from sri lanka between 1988 and 2021 was used to test the performance of the smlms in forecasting inflation rates. the results show that the svr model with walk-forward validation is the best method for forecasting the future inflation rate of sri lanka based on the mape value. overall, this study showcases the effectiveness of supervised machine learning models (smlms) in forecasting inflation rates, emphasizing the critical role of precise cross-validation techniques. these findings are invaluable for policymakers and investors, offering advanced tools for more informed economic decision-making and highlighting the potential of machine learning in enhancing macroeconomic stability and forecasting accuracy. keywords cross-validation, macro economic, hyper-parameter, inflation forecasting, machine learning 1 deportment of mathematics , university of ruhuna, sri lanka * corresponding author’s e-mail: bandarasudarshana009@gmail.com introduction inflation, a critical economic indicator, measures the rise in the general price level of goods and services over time. it impacts individuals, businesses, and the overall economy of a country. high inflation can lead to a decrease in the purchasing power of the currency, potentially causing economic instability, social unrest, and political turmoil (maldeni, 2021) (malladi, 2023) (jayasooriya, 2015). therefore, accurate forecasting of the inflation rate is essential to take preemptive measures to mitigate its adverse effects (bandara & de mel, 2021; jaehyuk choi, 2023). forecasting inflation rates is a challenging task due to several factors. unpredictable events such as natural disasters, political and social conflicts, and global economic crises can impact the economy unexpectedly. economic variables’ complex and dynamic relationships (jaehyuk choi, 2023). hence, there is a need for advanced forecasting models that can handle the complexity of economic data and provide accurate predictions. this paper focuses on the application of machine learning (ml) approaches for forecasting inflation, with empirical evidence from sri lanka (maldeni, 2021). ml, a subset of artificial intelligence, has shown promising results in various fields, including economics. it can analyze large volumes of data, learn from it, and make predictions or decisions without being explicitly programmed. in the context of inflation forecasting, ml models can capture non-linear relationships between variables, adapt to changes, and improve their performance over time with more data. economic forecasting is crucial for policy-making and strategic planning (anagaw, 2023). accurate inflation forecasts can help the government and central banks implement appropriate monetary policies to maintain price stability. businesses can also benefit from accurate inflation forecasts for budgeting, pricing, and investment decisions. ml can contribute significantly to economic forecasting (rahman et al., 2021). it can handle large datasets, including economic indicators, market data, and social media sentiment, which traditional econometric models may find challenging. ml models can also adapt to new data, making them suitable for dynamic economic environments. inflation forecasting is a critical aspect of economic stability, particularly for emerging economies like sri lanka. the country has faced periods of high inflation, significantly impacting its economy. therefore, accurate and timely inflation forecasts are vital for maintaining economic growth and stability. this paper aims to enhance the existing literature by applying machine learning (ml) approaches to forecast inflation in sri lanka. it will assess various ml models’ performance and juxtapose them with traditional econometric models. the study’s findings could offer valuable insights for policymakers, economists, and businesses in sri lanka and other emerging economies. the current research intends to develop and evaluate machine learning models’ efficacy in forecasting inflation rates in sri lanka. by addressing the limitations of traditional forecasting methods and exploring machine pa ge 52 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 51-60, 2024 learning models’ potential, this study contributes to the existing body of knowledge. the findings could have practical implications for policymakers, central banks, and investors, enabling them to make informed decisions based on more precise inflation forecasts. therefore, this study holds significant importance, and the application of machine learning models in forecasting inflation rates is necessary to address traditional forecasting methods’ limitations. the study’s results could lead to more accurate inflation forecasts, contributing to economic stability and informed decision-making processes. best predicting performance was achieved with the blocked cross-validation method with respect to the rmse statistic. literature review the current study aims to develop and evaluate the performance of machine learning models in forecasting inflation rates in sri lanka. the study contributes to the existing literature by addressing the limitations of traditional forecasting methods and exploring the potential of machine learning models in improving inflation forecasts. the findings of this study could have practical implications for policymakers, central banks, and investors in making informed decisions based on more accurate inflation forecasts. therefore, this study is significant importance, and the application of machine learning models in forecasting inflation rates is necessary to address the limitations of traditional forecasting methods. in seminal studies, various models such as univariate models and phillips curve models have been utilized in forecasting inflation rates. (jesmy, 2010) used box– jenkins’s method to forecast the monthly mean inflation rate of sri lanka by using the historical inflation data (1952-2009). in this study, the univariate arima(1, 1, 2) was selected as the best model using adjusted r-squared statistics. (bandara, 2011)used the var models to forecast the inflation rate of sri lanka using the monthly mean historical data (1985-2005). (jere, 2016) used the univariate time series models to forecast the inflation rate of zambia by using holt’s exponential smoothing. however, due to differences between global and domestic political, social, environmental, country-specific conditions, and sample periods it becomes difficult to compare different models. standard phillips curve models (pcm), which rely on economic activity, have acted as a basis to the typical forecasting models of inflation. (stock, 1999) also argue that these pcm based models outperform the traditional inflation forecasting models. (atkeson, 2001), however, criticize this claim by showing that phillips curve forecast of u.s. inflation over a 15-year period are no better than those obtained from a random walk model. nevertheless, this instability of forecasting relationships is not limited to traditional phillips curve-based models but extends to other theoretical or ad hoc empirical models used in the literature as well [see, e.g., models that include asset prices, for example, (marcellino m. s., 2000) , (goodhart, 2000) (marcellino m. , 2002),]. although forecasting specifications built adding one indicator of real activity at the time work poorly and tend to be unstable, some improvements have been documented by (cristadoro, 2005) (wright, 2003), (granger, 2004), and (inoue & kilian, 2006), using methods that combine information obtained from many predictors. in literature, various types of cross-validation methods with traditional forecasting methods were used to forecast inflation. for example, (bergmeir & benítez, 2012)used the cross-validation techniques with time series models where the stranded 5fold cross-validation, blocked cross-validation, last block cross-validation, second block cross-validation, and second cross-validation methods were used. the best predicting performance was achieved with the blocked cross-validation method with respect to the rmse statistic. machine learning models are rarely used in forecasting inflation data. (volkan et al., 2018) forecasted the core and non-core versions of inflation in the usa by using univariate auto regressive distributed lag (ardl), multivariate time series (var), svr, k-nearest neighbour, and artificial neural network models. according to their results, ardl provided the highest prediction accuracy for forecasting core-cpi inflation, while svr outperformed the other models in forecasting core inflation. all these machine learning models work better with more volatile and irregular series. in this study, we conducted a simulation to evaluate the performance of four different supervised machine learning models, lr, brr, svr, and rfr, for inflation forecasting. the simulation involved training and testing each model using two types of cross-validation methods, walk forward validation (wfv) and k-fold cross-validation (cvk), to estimate the models’ hyperparameters. to compare the models and their performances, we used the mean absolute percentage error (mape) statistic. we also evaluated the stability and consistency of each model’s performance across different time splits using the mean root mean square error (rmse) metric. overall, our simulation aimed to identify the best machine learning model for forecasting the monthly mean inflation rate in sri lanka, considering different cross-validation methods and performance metrics. our results provide useful insights into the effectiveness of different machine learning models for time-series data and can guide practitioners in selecting the most suitable model for their specific application. the layout of this article is as follows. section 2 provides a brief overview of machine learning models, crossvalidation techniques and error calculated statistics, section 3 presents the inflation data set and the four covariates that are used in simulation study, the results of a simulation study and algorithms are presented in section 4, and section 5 is devoted to the conclusion and future work. pa ge 53 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 51-60, 2024 materials and methods models in this subsection, we briefly explain supervised machine learning models, which are used to forecast inflation data, and two cross validation techniques. lasso regression (lr) according to (ogutu et al., 2012), “lasso”, “least absolute shrinkage and selection operator”, is an l1 regularization technique that uses shrinkage, and because it automatically performs feature selection, lasso can use a greater number of variables. the lr parameter estimate can be defined as follows; (1) effectiveness of a machine learning model. it is based on re-sampling training data to train and test groups and evaluating model performance under over-fitting and under-fitting conditions. in this study, we use two types of cross validation methods, namely, k-fold crossvalidation and walk forward validation. k-folds cross validation (cvk) in regression and classification settings, the k-fold cross-validation (trevor hastie, 2009) (cvk) technique is commonly used due to its simplicity, fairness, and high effectiveness. the dataset is divided into k intervals, and one subinterval is used as test data while the remaining k-1 intervals are used as training data. by fitting the model k times and selecting the optimal k value that minimizes the root mean square error (rmse), we can evaluate the model’s performance. walk forward validation (wfv) walk forward validation (wfv), also known as time-series validation, is a technique commonly used for evaluating time series data. in this method, the entire dataset is divided into k intervals. the model is trained on the first interval and tested on the second. then, the first two intervals are combined to train the model, which is tested on the third. this process is repeated until the first k 1 intervals are used for training, and the remaining interval is used for testing. at each step, the model is fit, and the root mean square error (rmse) is computed. the optimal k value is selected based on the minimum rmse. hyper-parameter tuning these hyper-parameters control various aspects of the model, such as the regularization strength, learning rate, and number of hidden layers in neural networks. selecting optimal hyper-parameters is crucial for achieving high model performance, and grid search or random search techniques are often used to explore the hyper-parameter space and find the optimal values. error calculation methods let n,yt, and ŷt be the number of fitted points, the actual value of the response variable y at time t, and the predicted value of yt, respectively. mean absolute percentage error (mape) the mean absolute percentage (armstrong, 1992) error (mape) can be calculated by using the following formula. mape= 1/n ∑n (i=1) (|yt-ŷt|)/yt. mape works best in the absence of extreme values in the data set. root mean square error (rmse) the root mean square error (hyndman, 2006) (rmse) is given by; where ||β||1=∑n 1|βi | is the l1norm penalty on β, which induces sparsity in the solution and λ≥0. bayesian ridge regression (brr) in bayesian ridge regression (hoerl, 1970), the estimate β is obtained by using the l2 norm, and it is given by; (2) where ||β||2=∑n 1 β 2 i is the l2norm penalty on β and λ≥0. in this case, we obtain the posterior distribution to estimate β with normal likelihood and normal prior distribution. support vector regression (svr) svr (smola & schölkopf, 2003) gives us the flexibility to define how much error is acceptable in our model. it will compute the parameter estimates by utilizing the following minimization problem. minimize min 1/2 ||β||2 (3) under constant |yi-βi xi≤ϵ| (4) where we set the absolute error less than or equal to a specified margin, called the maximum error, ϵ. we can tune ϵ to gain the desired accuracy of our model. random forests regression (rfr) rfr is a tree-based algorithm with each tree depending on a set of random variables (cutler et al., 2012). let x = (x1,x2,...,xp)’ be a p-dimensional random input vector and y be the response variable. moreover, we assume that pxy (x,y) is the unknown joint distribution of x and y . the objective of the rfr is to find a function f(x) to predict the response variable y by minimizing the risk function. exy (l(y, f(x))) (5) where l(y, f(x)) = (y f(x))2 is the squared error loss function. here, one can define f(x) as f(x) = e(y |x= x), and in regression setting, f(x) can be written as f(x)=1/j ∑j (j=1) hj (x) (6) with respect to a collection of basis functions h1 (x), h2 (x),...,hj (x). cross validation methods cross validation is a method used to increase the pa ge 54 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 51-60, 2024 the rmse and mape are commonly used measures of the forecasting error. data set in this study, we consider the monthly inflation rate data in sri lanka from january 1988 to august 2021 (tradingview, 2023) figure 1 depicts this data. simulation study the dataset used for the simulation study includes monthly data starting from january 1988. the dataset contains a total of 405 data points. to evaluate the performance of the model, the dataset was divided into three subsets: a training dataset consisting of 368 data points from january 1988 to february 2018, a test dataset consisting of 24 data points from march 2018 to march 2020, and a validation dataset consisting of 12 data points from april 2020 to august 2021. the simulation studies were performed using python 3.8.5. in this simulation, we extend our previous study by exploring four different supervised machine learning models: lasso, bayesian ridge regression (brr), support vector regression (svr), and random forest regression (rfr) to forecast the monthly mean inflation rate in sri lanka. in the simulation, we randomly select n rows from the whole data set for different sample sizes ranging from 50 to 405. we repeat each sample size 100 times and calculate the mean of the rmse for each machine learning model. the results for each machine learning model are plotted against the sample size, and the mean rmse is used as a measure of the model’s performance. the plots show the model performance for different sample sizes and highlight the optimal sample size required for each model. overall, this simulation aims to evaluate the performance of four different machine learning models and identify the best model for forecasting the monthly mean inflation rate in sri lanka using cvk. figure 2 compares the performance of the lr, brr, svr, and rf models using the cvk approach at different figure 1: monthly mean inflation rate of sri lanka (1988-2021) the time series plot in figure 1 shows a stochastic behaviour of the inflation data, and one can notice that there are a few unusual data points, especially one at june in 2008. the reasoning for this may be that during this time period, the war in sri lanka was at a critical stage. in order to fit the above four machining learning models, we have to convert the inflation rate data into a machine learning data set by introducing a new set of variables. the response variable, y (t) is the inflation rate at time t. here, we use four predictor variables: the first, second, third, and fourth differences of y (t) and denote them as y (t 1),y (t 2),y (t 3) and y (t 4), respectively. figure 2: comparison of model performance using k-fold cross-validation sample sizes ranging from 50 to 405. the figure includes four subplots, each representing a different machine learning model. the x-axis represents the sample size, while the y-axis represents the mean rmse value obtained for each sample size. the results demonstrate that the performance of each model varies with the sample size, with some models performing better than others at certain sample sizes. the lr and brr models consistently exhibit the lowest mean rmse values across all sample sizes. on the other hand, the svr and rf models perform comparatively worse, particularly at smaller sample sizes. these findings suggest that the lr and brr models may be more suitable for predicting the monthly mean inflation rate in sri lanka, especially when dealing with smaller sample sizes with cvk. pa ge 55 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 51-60, 2024 figure 3 is presented to compare the performance of lr, brr, svr, and rfr models using the wfv technique. each subplot in the figure represents a different time split, and the mean rmse is used to measure the model’s performance. the plots demonstrate the stability and consistency of the models’ performance across different time splits, indicating that wfv is a valuable technique for evaluating the performance of time-series data models. the simulation’s objective is to assess the effectiveness of the four machine learning models and identify the most suitable model for predicting the monthly mean inflation rate in sri lanka using wfv. figure 3: comparison of model performance using wfv table 1: comparison of smlm ‘s rmse with cvk and wfv techniques at different sample sizes rmse sample size 50 100 200 350 405 lr_cvk 0.091351 0.082883 0.079515 0.078016 0.077575 lr_ wfv 0.08694 0.083908 0.078658 0.07758 0.078942 brr_cvk 0.092358 0.078415 0.078144 0.076632 0.077401 brr_wfv 0.084405 0.08116 0.079336 0.076359 0.075956 svr_cvk 0.093935 0.080772 0.074827 0.073928 0.076598 svr_ wfv 0.100356 0.078344 0.076048 0.07283 0.071805 rf_cvk 0.120294 0.099488 0.086512 0.082122 0.081185 rf_ wfv 0.111612 0.101143 0.091514 0.083301 0.080442 the table 1 presents the performance of four different machine learning models, lr, brr, svr, and rf, using two different techniques: cvk and wfv. the models were tested on five different sample sizes ranging from 50 to 405. the performance of each model was measured using mean rmse. the results indicate that, in general, the models performed better with larger sample sizes. additionally, some models showed better performance with a particular technique. for example, lr, brr, and rf models achieved lower rmse values using wfv technique, while svr showed better performance using cvk technique. overall, the table highlights the importance of selecting an appropriate technique and sample size when building machine learning models for time-series data. it also provides useful insights into the performance of different models under different conditions, which can be helpful in selecting the most suitable model for specific applications. algorithm 1 is a pseudo code for the lr algorithm, which is used to predict the response (y) dependent on predictor (x) with an error tolerance ϵ. the algorithm starts with data preprocessing and initialization, followed by weight calculation based on the chosen method. the lr algorithm iteratively cycles through β until the desired result is achieved. finally, the algorithm returns the calculated β values. pa ge 56 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 51-60, 2024 figure 4 shows the fitted test inflation rates data for the lr and brr models using two different techniques: cvk and wfv. the lr model with cvk technique predicts the inflation rates data using lasso regression while minimizing the prediction error with k-fold crossvalidation. on the other hand, the brr model with wfv technique uses bayesian ridge regression to predict the inflation rates data while weighing the features by their variance. from the graph, both lr and brr models with cvk technique provide similar fitted test inflation rates data for the entire period from october 2018 to september 2020. however, the lr model with cvk technique predicts slightly higher inflation rates compared to the brr model with cvk technique. on the other hand, the brr model with wfv technique predicts lower inflation rates than the lr model with cvk technique, especially from february 2019 to september 2020. overall, the lr model with cvk technique and the brr model with wfv technique provide different predictions for the inflation rates data, indicating the importance of choosing the appropriate model and technique for inflation rate prediction. performs the β cycle until specific conditions are met. overall, the brr algorithm provides a robust and efficient solution for linear regression analysis, and its implementation can be tailored to different research needs based on the choice of weight calculation method. figure 4: fitted test inflation rates data for lr and bbr models with cvk and wfv techniques the presented pseudo (algorithm 2) code outlines the implementation of brr algorithm, which is a popular technique for linear regression analysis. the algorithm takes input of response y dependent on predictor x and error tolerance ϵ, and outputs the brr solution. the algorithm involves preprocessing of data by normalizing x and y, followed by initialization of u, ŷ, and other variables. it then calculates the weight by either part_pac, iw, or critic method, centralizes rw, and algorithm 3 is the pseudo code for svr, a popular regression algorithm used in machine learning. svr involves choosing a set of support vectors from the input data and constructing a linear model to minimize the error between the predicted values and the actual values. the algorithm involves several iterations where support vectors are chosen, and the model is updated with the chosen support vectors until convergence is reached. figure 5 displays the fitted test inflation rates data for the svr and rfr models using two different techniques: cvk and wfv. the svr model with cvk technique predicts the inflation rates data using support vector regression while minimizing the prediction error with cvk. on the other hand, the rfr model with wfv technique uses random forest regression to predict the inflation rates data while weighing the features by their variance. from the graph, both svr and rfr models with cvk technique provide similar fitted test inflation rates data for the entire period from october 2018 to september 2020. however, the svr model with cvk technique predicts slightly higher inflation rates compared to the rfr model with wfv technique. on the other hand, the rfr model with wfv technique predicts lower inflation rates than the svr model with cvk technique, especially from february 2019 to september 2020. pa ge 57 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 51-60, 2024 overall, the svr model with cvk technique and the rfr model with wfv technique provide different predictions for the inflation rates data, indicating the importance of choosing the appropriate model and technique for inflation rate prediction. the choice between these models and techniques may depend on the specific requirements of the application and the underlying data characteristics. table 2 presents the predicted values of the test data for four different machine learning models: rfr, svr, lasso, and brr. the table includes the actual values and predicted values for each model with cvk. and wfv. the table spans from october 2018 to september 2020, with monthly predictions for each model. overall, the table provides a comparison of the performance of the different models in predicting the test data over the twoyear period. figure 5: fitted test inflation rates data for svr and rfr models with cvk and wfv techniques table 2: predicted values of the test data for smlm date rfr svr lasso brr real cvk wfv cvk wfv cvk wfv cvk wfv 2018 oct 3.3 3.98 3.86 3.42 3.61 4.38 4.38 4.23 4.23 2018 nov 3.1 3.11 3.10 3.66 3.72 3.52 3.52 3.50 3.50 2018 dec 2.8 3.42 3.79 3.55 3.57 3.44 3.44 3.47 3.47 2019 jan 3.7 2.82 2.81 3.12 3.11 3.14 3.14 3.12 3.12 2019 feb 4 2.74 2.71 4.48 4.49 4.15 4.14 4.20 4.20 2019 mar 4.3 3.54 3.55 4.00 4.11 4.35 4.35 4.31 4.31 2019 apr 4.5 4.27 4.47 4.54 4.66 4.63 4.63 4.60 4.60 2019 may 5 4.68 4.59 4.60 4.74 4.81 4.81 4.78 4.78 2019 jun 3.8 4.94 4.81 5.21 5.38 5.32 5.32 5.32 5.32 2019 jul 3.5 3.67 3.74 3.27 3.42 3.96 3.97 3.84 3.84 2019 aug 3.4 3.81 3.42 4.00 4.03 3.80 3.80 3.80 3.80 2019 sep 5 3.51 3.80 3.66 3.71 3.73 3.73 3.74 3.74 2019 oct 5.4 5.77 5.68 5.97 6.08 5.46 5.46 5.55 5.55 2019 nov 4.4 6.51 6.40 5.00 5.26 5.68 5.68 5.62 5.62 2019 dec 4.8 4.49 4.41 3.83 4.00 4.55 4.56 4.43 4.43 2020 jan 5.7 4.78 4.76 5.25 5.38 5.11 5.11 5.16 5.16 2020 feb 6.2 6.08 6.04 5.90 6.09 6.03 6.03 6.07 6.07 2020 mar 5.4 6.66 6.48 6.02 6.28 6.44 6.44 6.42 6.42 2020 apr 5.2 4.97 5.01 4.66 4.89 5.52 5.52 5.42 5.42 pa ge 58 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 51-60, 2024 2020 may 4 5.06 5.21 5.12 5.29 5.41 5.41 5.41 5.41 2020 jun 3.9 3.33 3.04 3.56 3.72 4.15 4.15 4.08 4.08 2020 jul 4.2 3.53 3.36 4.29 4.36 4.20 4.20 4.23 4.23 2020 aug 4.1 3.85 3.97 4.39 4.49 4.54 4.54 4.56 4.56 2020 sep 4 4.24 4.21 4.09 4.21 4.39 4.39 4.35 4.35 algorithm 4 presents the pseudo code for rfr. rfr is a popular ensemble learning method used for both classification and regression problems. the algorithm builds a specified number of decision trees using a bootstrapped sample of the training data and selects a random subset of features at each node to split on. the final prediction is the average of the predictions from all the trees in the forest. the rfr algorithm is known for its ability to handle high dimensional data and avoid overfitting. implementation, when choosing a model for practical applications. in conclusion, the results suggest that the svr model with wfv may be a suitable choice for predicting the inflation rate in sri lanka, but further validation and evaluation may be required to ensure the reliability of the results. table 3: mape values in test data of each smlm cvk wfv model mape model mape lr 13.42 lr 13.43 brr 13.04 brr 13.40 svr 12.79 svr 13.34 rfr 15.82 rfr 15.63 table 3 shows the mape values for four different supervised machine learning models (smlms) used to predict the inflation rate in sri lanka. the table presents the mape values for each model in two columns for two different cross validation methods cvk and wfv. upon examining the mape values, it can be concluded that the support vector regression (svr) model outperformed all the other models for both feature extraction techniques. for wfv, svr had the lowest mape of 13.34%, followed by bayesian ridge regression (brr) at 13.40%, lasso regression (lr) at 13.43%, and random forest regression (rfr) at 15.63%. similarly, for the other feature extraction technique, svr had the lowest mape of 12.79%, followed by brr at 13.04%, lr at 13.42%, and rfr at 15.82%. therefore, svr is the most accurate model for predicting the inflation rate in sri lanka based on the given features. however, it is important to note that the differences in mape values among the models were relatively small, with differences of only a few percentage points. therefore, it may be more appropriate to consider other factors, such as computational complexity and ease of figure 6: forecasted valuessvr with wfv techniques the graph shows the actual values and forecast values of a certain variable over a period. the variable is denoted by yt while the forecasted values are generated using a machine learning model, namely support vector regression (svr). the graph consists of two lines: one line representing the actual values and another line representing the forecasted values. the actual values are plotted as points on the graph, while the forecasted values are connected by a line. the graph enables a visual comparison between the actual values and the forecasted values. the closeness of the forecasted values to the actual values can be seen from the graph. looking at the graph, it can be observed that the svr model generally performed well in forecasting the variable. however, there are some instances where the forecasted values deviate from the actual values. for example, in february 2021, the actual value was 3.3, but the forecasted value was 2.992228, which is considerably lower. similarly, in may 2021, the actual value was 4.5, but the forecasted value was 3.827932, which is also lower. on the other hand, in september 2021, the actual value was 5.7, and the forecasted value was 6.004278, which is slightly higher. the closeness of the forecasted values to the actual values can be seen from the trend line of the forecasted values, which closely follows the trend line of the actual values. overall, the graph provides a clear visualization of the performance of the svr model in forecasting the variable. it highlights the instances where the model performed well and the instances where it deviated from the actual values. the insights from the graph can be used to further refine the machine learning model and improve its forecasting accuracy. pa ge 59 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 51-60, 2024 table 4 shows the forecasted values generated by a support vector regression (svr) model using the wavelet-based feature vector (wfv) technique. the table includes the date, the actual values of the variable being forecasted (yt), and the forecasted values generated by the model. the mape value of 10.16 % indicates that the average error of the forecasted values is approximately 10% of the actual values. the table demonstrates that the model was relatively accurate in predicting the values for the first few months, but the accuracy decreased in later months, with the largest discrepancy occurring in may. the model showed improvement in june and july but still underestimated the actual values in august and september. overall, the table suggests that the svr model with the wfv technique may be a suitable choice for forecasting the variable of interest, but further analysis and model refinement may be necessary to improve accuracy. conclusions in conclusion, the support vector regression (svr) model with the walk forward validation (wfv) technique demonstrated superior performance in forecasting the inflation rate in sri lanka. the model’s accuracy was measured using the mean absolute percentage error (mape) value, which was found to be 10.16%. the predicted values of the svr model with wfv technique were compared to the actual inflation rates, and it was observed that the model’s forecasts closely matched the actual values. however, the model’s sensitivity to unusual data was noted, which could be addressed by using a more robust machine learning model. overall, the findings suggest that the svr model with wfv technique can be a valuable tool for forecasting inflation rates in sri lanka. as a future direction, it would be beneficial to consider the presence of outliers in the data and explore robust machine learning models that can handle them effectively. additionally, incorporating other relevant economic and financial indicators into the model can potentially improve the accuracy of inflation rate forecasts. referances anagaw, t. 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(2021). a machine learning approach to ccpi-based inflation prediction. proceedings of sixth table 4: foretasted values for svr model with wfv technique date yt forecast 2020 oct 4.0 4.296212 2020 nov 4.1 4.323517 2020 dec 4.2 4.425066 2021 jan 3.0 4.485011 2021 feb 3.3 2.992228 2021 mar 4.1 4.185231 2021 apr 3.9 4.532705 2021 may 4.5 3.827932 2021 jun 5.2 5.180228 2021 jul 5.7 5.517756 2021 aug 6.0 5.830174 2021 sept 5.7 6.004278 mape 10.16 pa ge 60 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 51-60, 2024 international congress on information and communication technology. lecture notes in networks and systems, p. 236. https://doi.org/10.1007/978-981-16-2380-6_50 malladi, r. k. (2023). enchmark analysis of machine learning methods to forecast the u.s. annual inflation rate during a high-decile inflation period. computational economics. https://doi.org/10.1007/ s10614-023-10436-w marcellino, m. 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(2003, september). forecasting u.s. inflation by bayesian model averaging (september 2003). international finance discussion paper, 1-33.http:// dx.doi.org/10.2139/ssrn.457360 pa ge 1 pa ge 12 4 american journal of applied statistics and economics (ajase) forecasting the global price of corn: unveiling insights with sarima modelling amidst geopolitical events and market dynamics raksha khadka1*, yeong nain chi1 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2776 https://journals.e-palli.com/home/index.php/ajase article information abstract received: july 05, 2024 accepted: august 07, 2024 published: august 10, 2024 corn is pivotal in global agriculture, serving diverse purposes in the food, feed, and biofuel sectors. despite its economic significance, corn price volatility, influenced by supply-demand dynamics, climate variations, and geopolitical tensions, poses challenges in decision-making processes. this necessitates accurate price forecasting of corn for producers and government alike to formulate effective policies that uphold stability and enhance efficiency within the corn market. using long-term records of the monthly global price of corn spanning from january 2014 to december 2023, this study employs sarima modeling techniques to forecast the global price of corn. to find a solution, the auto.arima() function from the “forecast” package in r 4.3.2 for windows was employed to identify both the structure of the series (stationary or not) and type (seasonal or not) and sets the model’s parameters, which takes into account the aic, aicc or bic values generated to determine the best fitting seasonal arima model. following the box–jenkins methodology, the best-fitting sarima (0,1,1) (0,0,1) [12] model was identified, supported by the lowest aic value. the ljung–box q–test further validated the model’s adequacy in capturing the data’s behavior, with a non-significant p-value of 0.7013. this analysis uncovered valuable insights into the fluctuations of corn prices, providing a comprehensive understanding of the interplay between economic factors and external influences. this study underscores the practical utility of sarima modeling for farmers and other relevant stakeholders in anticipating market fluctuations and devising adaptive strategies in response to evolving corn market dynamics. keywords corn, price, time series, forecasting, sarima 1 department of agriculture, food, & resource sciences, school of agricultural and natural sciences, university of maryland eastern shore (umes), princess anne, md 21853, usa * corresponding author’s e-mail: rkhadka@umes.edu introduction corn stands as one of the paramount grain crops globally, holding the prestigious rank of third, trailing only behind wheat and rice. its cultivation sprawls across more than 100 countries, with the united states spearheading production, contributing approximately 40% of the world’s total output. alongside the us, other key corn-producing nations encompass china, brazil, mexico, indonesia, india, france, and argentina (darekar & reddy, 2017). beyond its sheer volume of production, corn plays a pivotal role in various sectors, including both food and industrial domains. notably, corn finds its way into the production lines of diverse products, prominently featuring in the creation of starch (yu & moon, 2021). corn’s multifaceted utility extends to its applications in livestock feed production (fauziah et al., 2023), corn oil extraction (wheals et al., 1999), and the production of ethanol, a renewable and widely used biofuel. ethanol production from corn boasts an impressive conversion rate, generating 2.7 gallons of ethanol per bushel of corn (baker & zahniser, 2006). given its indispensable role in various industries, any fluctuations in the price of corn reverberate across sectors, impacting stakeholders at various levels. price volatility in commodity markets stems from a myriad of factors, encompassing intricate interplays of supply-demand dynamics, crop yield variations, geopolitical tensions, economic downturns, and even global health crises. corn prices are inherently susceptible to these forces and have witnessed significant oscillations over time. in this context, harnessing the power of time series analysis emerges as a potent tool for navigating the complexities of pricing decisions. time series analysis entails the systematic examination of data points recorded over sequential time intervals. these data, arranged chronologically, offer insights into the temporal evolution of a specific variable. represented mathematically as y(t), where ‘y’ denotes the variable of interest and ‘t’ denotes time, time series data enable analysts to discern patterns, trends, and anomalies, thereby empowering informed decision-making (montgomery et al., 2015). by leveraging historical trends, forecasting techniques embedded within time series analysis equip market participants with valuable foresight, facilitating proactive adjustments to pricing strategies in anticipation of future market dynamics. moreover, the significance of corn transcends mere economic considerations. it is deeply intertwined with agricultural practices, environmental sustainability, and food security on a global scale. as populations burgeon and climates fluctuate, the resilience and adaptability of corn cultivation become increasingly pivotal. understanding the intricate dynamics governing corn prices not only informs commercial decisions but also holds implications for broader socio-economic and environmental contexts. furthermore, the evolution of technology, particularly pa ge 12 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 124-135, 2024 in data analytics and computational methods, has revolutionized the landscape of market analysis. advanced statistical models and machine learning algorithms offer unprecedented capabilities in extracting actionable insights from voluminous datasets. integrating these technological advancements with traditional economic principles enhances the efficacy of pricing strategies, positioning market participants to navigate the intricacies of the corn market with greater precision and agility. in light of these considerations, this paper embarks on a comprehensive exploration of corn price dynamics, employing a multifaceted approach that melds economic theory with cutting-edge analytical methodologies. through a nuanced examination of historical trends, statistical modeling, and forecast projections, this study endeavors to shed light on the underlying drivers of corn price fluctuations, thereby empowering stakeholders with actionable intelligence to optimize pricing strategies and mitigate risks in the volatile landscape of commodity markets. literature review the fluctuations in global corn prices over the years have been influenced by a myriad of interconnected factors, resulting in a complex and dynamic market landscape. these fluctuations can be attributed to shifts in global demand, disruptions in supply chains, geopolitical events, disease outbreaks, sudden climate changes, increased demand for corn-based products (such as biofuels), and speculation in commodity markets. the covid-19 pandemic in 2020 exemplified how external shocks can significantly impact corn prices and production, particularly in the united states. disruptions in ethanol, gasoline, and oil markets led to a notable decrease in corn prices, as highlighted by schmitz et al. (2020). beghin & timalsina (2020) observed a decrease in corn prices from $3.74 per bushel in december 2019 to $2.94 per bushel in may 2020, while liu et al. (2024) noted that the pandemic influenced subsequent increases in global corn prices. following the economic slowdown as a result of covid-19, biofuel prices experienced a significant decline in 2020, followed by their main feedstocks, maize, and oilseeds (elleby et al., 2020). additionally, the russiaukraine conflict, which began in 2022, exacerbated the situation by causing an energy crisis and disrupting food production and commodity markets, including corn prices, as both countries are major exporters of staple crops (avalos & huang, 2022). the conflict also highlighted the paradoxical potential for increased biofuel usage to moderate rising oil prices, consequently boosting demand and prices for corn, a crucial feedstock for ethanol production. furthermore, fluctuations in both supply and demand for corn, driven by factors such as natural conditions, imports, changing needs in animal feed, food production, and alternative energy sources, have contributed to consumer-level price volatility (baladina et al., 2021). the seasonal fluctuations in crop production further exacerbate the challenge of market strategy and investment planning due to price uncertainty (brandt & bessler, 1983). consequently, accurate price forecasting becomes essential to assist farmers, stakeholders, consumers, policymakers, and investors in making informed decisions. numerous studies have focused on forecasting commodity prices, including corn, utilizing various time series forecasting models. table 1 presents some of the identified models used for forecasting corn prices, along with the evaluation tools and performance metrics employed by different researchers. similarly, table 2 illustrates the diversity of applications of time series forecasting models in value forecasting across different fields, showcasing the range of models utilized and the performance metrics evaluated by various authors. table 1: sarima models identified by authors for commodity price forecasting commodities models identified evaluation tools/performance metrics contributors corn (2,1,0)(3,1,1)12 mse, rmse, ljung-box q (lv & wu, 2022) soybean (0,1,3)(0,0,2)12 mse (chi, 2021) red lentil (2,1,2)(0,1,1)52 aic, ljung-box q (divisekara et al., 2020) potato (1,1,1)(1,0,0)12 sbc, aic (chandran & pandey, 2007) tomato (2,1,1)(1,0,1)12 aic (mutwiri, 2019) bajra (0,1,1)(0,1,1)12 aic, sbc, mad, mape, mse (sharma & burark, 2015) table 2: sarima models identified by various authors for value forecasting forecasts models identified evaluation tools/performance metrics contributors monthly mean surface air temperature (2,1,1)(1,1,2)12 me, rmse, mae, mpe, mape, mase, bic (asamoah-boaheng, 2014) frequency of monthly rainfall (1,0,1)(1,1,1)12 s.e, variance, aic (adams et al., 2019) temperatures (1,1,1)(1,0,1,)12 aic (chen et al., 2018) pa ge 12 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 124-135, 2024 corn plays a crucial role in ethanol production, a biofuel often blended with gasoline. over the last two decades, ethanol production has been the only use of corn in the united states that has seen a notable increase, consuming about 40% of the u.s. harvest on average over the past five years (avalos & huang, 2022). higher oil prices incentivize gasoline blenders to increase the ethanol content in their products, potentially mitigating oil price spikes but simultaneously boosting corn demand and prices (avalos & huang, 2022). this inflation was fueled by post-pandemic economic adjustments since mid2020, and persistent constraints on aggregate supply due to disruptions in global supply chains (goryunov et al., 2023). economic challenges, including accelerating inflation since 2021, further compounded the situation, impacting countries worldwide. in the usa, inflation reached close to 10%, while it was even higher in the euro area (goryunov et al., 2023). although inflation rates began to decrease by 2023, they remained elevated, reflecting the enduring impact of the factors influencing corn prices on the global economy. dohlman et al. (2024) forecasts suggest that there will likely be a decrease or stability in crop prices from 2024 to 2033. the united states department of agriculture (usda) also anticipates a decline in corn prices to $4.50 per bushel, followed by a period of stabilization around the 2025/26 timeframe (dohlman et al., 2024). due to the abundant supply of corn in the united states, it is anticipated that corn prices will experience a decline throughout 2024 (uga cooperative extension, 2024). in addition to understanding the factors influencing price fluctuations, forecasting models play a crucial role in providing valuable insights for decision-making processes across various sectors, ranging from agriculture to climate science. by utilizing sophisticated modeling techniques and evaluating performance metrics, researchers strive to enhance the accuracy and reliability of forecasting models to better navigate the complexities of global markets and environmental systems. materials and methods the main purpose of this study was to demonstrate the role of the time series model in predicting processes and to pursue the analysis of time series data using long-term records of the monthly global price of corn from january 2014 to december 2023. the monthly global price of corn, (units: u.s. dollars per metric ton, monthly, not seasonally adjusted) from january 2014 to december 2023, is available to the public from international monetary fund, global price of corn [pmaizmtusdm], retrieved from fred, federal reserve bank of st. louis; https://fred.stlouisfed.org/ series/pmaizmtusdm, march 10, 2024. the average monthly global price of corn from january 2014 to december 2023 was $200.6 u.s. dollars per metric ton with a standard deviation of $84.35 (minimum: $144.0, maximum: $348.5, and median: $171.9). time series analysis is based on the underlying assumption that the data is stationary. thus, it is crucial to identify whether time series data is stationary or non-stationary. data is considered stationary if its mean and variance do not change over time. conversely, non-stationary data exhibit a long-term increase or decrease over time, along with periodic fluctuations and changes in variance. a series is strictly stationary if the marginal distribution of y at time t [p(yt)] is the same as at any other point in time. p(yt) = p(yt+k) and p(yt, yt+k) does not depend on t (t ≥ 1 and k is any integer). this implies that the mean, variance, and covariance of the series yt are timeinvariant. however, a series is said to be weakly stationary if the following conditions are met: e(y1) = e(y2) = …..= e(yt) = µ (1) var(y1 )=var(y2 )=.....=var (yt )= γ0 (a constant) (2) cov(y1,y(1+k)= cov(y2,y(2+k)) = ….cov(yt,y(t+k)) = γk (depends only on lag k) (3) because the statistical properties of time series data change over time in non-stationary data, it is difficult to make predictions and draw conclusions. we cannot determine the appropriate model. non-stationary data may sometimes result in false regressions, meaning the regression equation shows a significant relationship between two variables when there wasn’t any. the dickey-fuller test was developed by david dickey and wayne fuller in 1979. it tests for the presence of trends and seasonality in data. this test tests the presence of unit roots (phillips & perron, 1988) in an autoregressive model. if a unit root is present, it means the data is non-stationary suggesting that it is difficult to model and forecast the future values. consider the following hypothesis: null hypothesis (h0) = the model is non-stationary (the time series has a unit root) alternate hypothesis (h1) = the model is stationary (the time series does not have a unit root). if the test statistic produced by the dickey-fuller test is at the significance level of 0.05, the null hypothesis is rejected and the data set suggests stationarity. again, if the test statistics are greater than the significance level i.e., >0.05, the time series suggests the non-stationarity of data which implies there is a need for differencing to make the series stationary before applying time series models such as arima. it is a method of transforming a non-stationary time series into a stationary time series. it is used in removing the trend in the time series (mcgonigle et al., 2022). this is an important step in preparing data to be used in an arima model. when the difference between the current period and the previous period is made, it is called firstorder differencing and can be denoted as i(1). it can be expressed as: δyt = yt y(t-1) (4) where yt represents time series data at time t and the current value we are trying to model. if the values exhibit non-stationarity properties, the process is repeated twice and thus called second-order differencing and can be pa ge 12 7 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 124-135, 2024 denoted as i(2). it can be expressed as: δ2 yt = (yt – y(t-1) ) (y(t-1) – y(t-2)) (5) where yt represents time series data at time t and the current value we are trying to model. this process is continued until the values show stationary properties (constant mean and variance). a series that is stationary after being differentiated d times is said to be integrated of order d, denoted by i(d). however, when a series is stationary without differencing is said to be i(0). autoregressive (ar) model ar models are used to forecast future values only based on their previous values, typically called lags. thus, the forecasted value ‘y’ and time ‘t’ in ar is the function of its past values yt-1, yt-2, yt-3, yt-4, ………. thus, yt = f (y(t-1), y(t-2), y(t-3), y(t-4),…., εt) (6) the model can be mathematically expressed as: yt =β0 +β1 y(t-n) + εt (7) where: yt represents the value of the variable at time t, β0 is the constant term, β1 is the coefficient of the lagged variable, yt−n represents the effect of the nth period’s value on the current period, εt is the error term at time t and represents the deviation of the actual value from the predicted value based on the model. ar models that depend only on one lag in the past are called first-order auto-regressive model or ar(1) models and can be expressed as: yt = β0 +β1 y(t-1) +εt (8) ar models that depend only on two lags in the past are called second-order auto-regressive model or ar(2) models and can be expressed as: yt = β0 +β1 y(t-1) + β2 y(t-2) + εt (9) the variables of interest in the ar model are forecasted using the linear combination of the past values of the variable and the term ‘autoregression’ indicates that it is a regression of the variable against itself. hence, the ar model of order p can be represented as: yt = β0 +β1 y(t-1) + β2 y(t-2) + β3 y(t-3) + β4 y(t-4) + β5 y(t-5) + β6 y(t-6) + …………… βp y(t-p) + εt (10) the ar models are also called long-memory models as they can take into account a large range of past observations to predict the current value. moving average (ma) model models used to forecast the series based solely on the past errors in the series i.e., error lags are called moving average (ma) models. in ma, the forecasted value ‘y’ at a time ‘t’ is the function of the lags of its error value at the time ‘t’, yt = f(εt, ε(t-1), ε(t-2), ε(t-3),…………….) (11) the model ma(q) can be mathematically expressed as: yt = µ + εt + θ1 * ε(t-1) + θ2 * ε(t-2) + θ3 * ε(t-3) + θ4 * ε(t-4) + …….+ θq * ε(t-q) (12) where: yt represents time series data at time t and the current value we are trying to model, µ is the mean of the time series data and the expected value of yt when all other terms in the equation are zero, εt is the error term at time t and represents the deviation of the actual value from the predicted value based on the model, and θ represents the weight assigned to the lagged error terms in the model. ma models that depend upon only one error lag are called first-order ma models, denoted by ma(1): yt = µ + εt + θ1 * ε(t-1) (13) the second-order ma model, denoted by ma(2) is: yt = µ + εt + θ1 * ε(t-1) + θ2 * ε(t-2) (14) moving average models are the short memory models since the errors in them don’t last long into the future. auto-regressive moving average (arma) model arma is the combination of ar and ma models and is used to describe the behavior of the time series and to forecast future values based on historical data. the arma(p,q) model can be mathematically represented as: yt = c + β1 y(t-1) + β2 y(t-2) + β3 y(t-3) + ….……..… + βp y(t-p) + θ1 * ε(t-1) + θ2 * ε(t-2) + θ3 * ε(t-3) + ...………… + θq * ε(t-q) + εt (15) where: yt represents time series data at time t and the current value we are trying to model, c is a constant term, β1, β2, β3, ……., βp are the coefficients of the autoregressive part, θ1, θ2, θ3, ……, θq are the coefficients of the moving average part, and εt is the error term at time t. arma model is purely stationary without difference and a blend of ar and ma models. auto-regressive integrated moving average (arima) model arima models, also known as box-jenkins models (montgomery et al., 2015), are a statistical method useful for forecasting data based on their temporal structures. this model is useful for analyzing and forecasting time series exhibiting trends and seasonal patterns. it is, in simple terms, an integration of the auto regression and moving average model with differencing made to make the time series stationary. its parameters can be represented as (p, d, q) and can be expressed as arima (p, d, q), where, p = number of lag observations or the order for the autoregression, d = order of differencing made to make the data stationary for the non-stationary series, and q = order of the moving average. seasonal auto-regressive integrated moving average (sarima) model a widely used variation of the arima model called the sarima model (chen et al., 2018) was proposed by box and jenkins. this model shows the significance of seasonality (vaswani et al., 2023). it integrates both the seasonal and non-seasonal components (adams et al., 2019) and can be represented as: arima (p, d, q) (p, d, q)(s), where, p = non-seasonal auto regression order, d = non-seasonal differencing order, q = nonseasonal moving average order. similarly, p = seasonal auto regression order, d = seasonal differencing order, q = seasonal moving average order, and s = period in a seasonal pattern. pa ge 12 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 124-135, 2024 box-jenkins approach the box-jenkins approach to modeling was developed by george box and gwilym jenkins in the early 1970s. this methodology consists of three major stages: identification, estimation, and diagnostic checking. at the identification stage, stationarity is checked. the process advances to model estimation if the data is stationary; otherwise, the data is transformed to make it stationary through decomposition and differencing methods. the autocorrelation function (acf) and partial auto-correlation functions (pacfs) are visualized in this stage. after identifying the model structure, parameters for the model are estimated by testing least squares estimation, such as mean error (me), root mean squared error (rmse), mean absolute error (mae), mean percentage error (mpe), mean absolute percentage error (mape), and mean absolute scale error (mase). me represents the average error, whereas rmse is the square root of the average of the squared errors. similarly, mae represents the average of the absolute errors, mpe is the average of the percentage errors, and mape is the average of the absolute percentage of errors. likewise, mase measures prediction accuracy, and acf1 is the first-order autocorrelation of the residuals. lower values indicate a better-fitting model. maximum likelihood estimation is also one of the techniques used for estimating models, such as akaike’s information criterion (aic), aics, bayesian information criterion (bic), etc. to determine the representativeness of the model regarding the dataset, diagnostic tests are conducted after model estimation (young, 1977). the diagnostic test ensures whether or not the model adequately captures the underlying patterns and features of the data. a famous test called the box-ljung test (ljung & box, 1978), used in time series analysis, determines the presence/absence of autocorrelations among the residuals. the residuals are examined for randomness and autocorrelation, i.e., the graphs, test statistics, acfs, and pacfs of the residuals are used for model verification. the steps of identification, estimation, and diagnostics are repeated if the model is not verified as the best model. after completing the above-mentioned stages, the model is ready to forecast the future values of any time series data. results and discussion a forecast of the global price of corn was attempted using the sarima (seasonal auto-regressive integrated moving average) model. r 4.3.2 for windows was used to model and forecast the monthly global price of corn from january 2014 to december 2023, retrieved from fred and converted into a time series object. the time series plot (figure 1) and its lag plot (figure 2) of global corn figure 1: time series plot of the monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data figure 2: lag plot of the monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data pa ge 12 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 124-135, 2024 prices recorded from january 2014 to december 2023 show fluctuation over time but show some trends and patterns. seasonal variations are evidenced in the prices which can be potentially influenced by weather events, harvest periods, demand, etc. there are fluctuations within each year. there appear to be periods of both increases and decreases in the prices over the years. the price was lowest in 2020 and was a record high in 2022. its stationarity was checked using the augmented dickey-fuller (adf) test. the value of adf statistic was -2.3498 which reflects that the data is weakly stationary. the p-value suggests the level of significance of any observation. the high p-value (typically > α = 0.05) suggests that we fail to reject the null hypothesis of nonstationarity. here, the p-value (0.4313) obtained from the adf test for the original data suggested rejecting the null hypothesis of non-stationarity. autocorrelation (figure 3) and partial autocorrelation (figure 4) were also visualized. in the acf plot (figure 3) it can be seen that there is a gradual decay of spikes but never cut off to zero meaning the data needs to be made stationary for further testing. using decomposition, the monthly global price of corn time series was decomposed into three components trend, seasonal, and random and each component was visualized (figure 5) which shows the decomposed corn price for the various years recorded. it also can be observed that the existence of the seasonal variation is constant over time. the random effect also seems to be constant over time. however, the trend of the series seems to be constant till 2020 and then gradually rising upwards peaking at 2022 and gradually sliding downwards after that. the seasonal component was removed to make the data stationary (figure 6), and the stationarity was again tested using the adf test. figure 3: acf plot of the monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data figure 5: decomposition of the monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data figure 6: time series plot of seasonally adjusted monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data figure 4: pacf plot of the monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data pa ge 13 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 124-135, 2024 figure 7: time series plot of the first difference of the seasonally adjusted monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data figure 9: pacf plot of the first difference of the seasonally adjusted monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data figure 11: acf plot of the twelfth difference of the seasonally adjusted monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data figure 12: pacf plot of the twelfth difference of the seasonally adjusted monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data figure 8: acf plot of the first difference of the seasonally adjusted monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data figure 10: time series plot of the twelfth difference of the seasonally adjusted monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data pa ge 13 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 124-135, 2024 given the p-value of 0.5211, which is quite high, we again failed to reject the null hypothesis and the data is still non-stationary. so, differencing (figures 7 & 10) was attempted. the test statistics thus obtained were negative and significant the p-value was 0.01 in both cases which is < α = 0.05 suggesting the stationarity of data. the differenced series removed the trends and seasonality, making the data more stationary and ready for modeling. acf (figures 8 & 11) and pacf (figures 9 & 12) from both cases were also visualized. these plots show the acf and pacf of the global corn price series with 95% confidence limits. it can be seen from the acf plot that the spike is significant till the first lag and there are no significant spikes in the pacf plot. in terms of identifying and fitting the model, the bestfitting seasonal arima model was determined by using auto.arima() function in r which automatically took into account the aic, aicc, or bic values. the notation in the arima model represents the parameters of the arima model. it indicates the order of the autoregressive (ar), differencing (i), and moving average (ma) components of the model. in this case, an arima (0,1,1)(0,0,1) [12] model was obtained suggesting the series has a seasonal pattern and a first-order non-seasonal moving average term and a first-order seasonal moving average component has been influencing the series. thus, it is arima (0,1,1) for the non-seasonal part and arima (0,0,1) for the seasonal part, with a seasonal period of 12 months. this simply also suggested a moving average term and a seasonal moving average term. the coefficient for the ma term was 0.4628, and for the seasonal ma was -0.3777 and the estimated variance of the residuals was 105.9 (table 3). the loglikelihood of the model is the measure of how well the model fits the data and it was -446.29 in this case. the model was selected based on aic and was evaluated using its summary statistics. table 3: parameters of the arima(0,1,1)(0,0,1)12 model parameter estimate standard error difference 1 ma1 0.4268 0.0919 sma1 -0.3777 0.1312 sigma2 = 105.9: log likelihood = -446.29 aic = 898.57, aicc = 898.78, bic = 906.91 rmse = 10.16116, mae = 7.525733, mape = 3.665245 source: own computation based on fred (january 2014 ~ december 2023) data the residual analysis was performed for the model verification. it can be seen from the standardized residuals (figure 13) that the residuals of the model have zero mean and a constant variance concentrated around 2 and -2. it can be visualized from an acf plot of residuals (the second panel of figure 13) that there is no autocorrelation among the residuals; the autocorrelation is zero. this implies zero mean and constant variance among the residuals. hence, the residuals follow a white noise process. the adequacy of the model was also judged by the ljung-box test and the box-ljung test which were used to assess the autocorrelation of the residuals from the model where autocorrelation means that the values of the series at different points in time are correlated with each other. in the ljung-box test, the test statistics q* are compared against a chi-squared distribution with degrees of freedom equal to the number of lags used in the test minus the model degrees of freedom. the p-value (0.9303) obtained suggested no significant autocorrelation in the residuals. in the box-ljung test also, the x-squared statistic is compared against a chisquared distribution. the p-value (0.7013) in this test also indicated no significant autocorrelation in the residuals. this can also be visualized from the p-value of the residuals (the third panel of figure 13) that its value exceeds the 5% confidence interval showing no significant departures from the white noise of the residuals. also, from the normality plot of residuals (figure 14), the residuals follow the normal distribution process i.e., there is a symmetric distribution of the residuals around the mean residual. thus, sarima (0,1,1) (0,0,1) [12] was selected to be the best-fitting model for this time series. forecasts were generated using the fitted arima model and the forecasted values were visualized (figure 15 and figure 16). the forecast values – january to december 2025 – seem to be relatively stable over the period, with a slight upward trend. the forecast values for each month in the first year (2024) are generally lower than those in the second year (2025) (table 4). pa ge 13 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 124-135, 2024 figure 13: ljung-box test of the seasonally adjusted monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data figure 14: residual plots of the seasonally adjusted monthly global price of corn (january 2014 ~ december 2023) source: own computation based on fred (january 2014 ~ december 2023) data table 4: the forecasted monthly global price of corn for 2024 and 2025 month point forecast lower 95% confidence limit upper 95% confidence limit 2024 2025 2024 2025 2024 2025 january 211.4124 238.2565 191.2432 138.4384 231.5816 338.0746 february 212.1315 238.2565 176.9903 136.8447 247.2727 339.6682 march 212.5306 238.2565 167.1102 135.2757 257.9509 341.2373 april 208.9084 238.2565 155.1393 133.7303 262.6775 342.7827 pa ge 13 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 124-135, 2024 may 217.2596 238.2565 156.2742 132.2074 278.2450 344.3056 june 219.4492 238.2565 152.0154 130.7060 286.8831 345.8070 july 229.7617 238.2565 156.4444 129.2253 303.0790 347.2877 august 236.5466 238.2565 157.7841 127.7644 315.3091 348.7486 september 227.4025 238.2565 143.5477 126.3226 311.2574 350.1904 october 228.1253 238.2565 139.4701 124.8992 316.7805 351.6138 november 235.2223 238.2565 142.0137 123.4934 328.4309 353.0196 december 237.9829 238.2565 140.4332 122.1046 335.5326 354.4084 source: own computation based on fred (january 2014 ~ december 2023) data figure 15: forecasted monthly global price of corn under upper and lower confidence levels source: own computation based on fred (january 2014 ~ december 2023) data figure 16: observed and the forecasted monthly global price of corn source: own computation based on fred (january 2014 ~ december 2023) data the fluctuations in global corn prices over the years reflect the intricate interplay of multifaceted factors, necessitating a comprehensive understanding of the market dynamics. global demand for corn is influenced by diverse factors, including population growth, dietary preferences, industrial uses, and government policies related to biofuel production and food security. shifts in these demand drivers can lead to significant price fluctuations, affecting stakeholders across the corn supply chain, from producers to consumers. disruptions in supply chains, whether due to natural disasters, trade conflicts, or logistical challenges, can have profound effects on corn prices. for instance, extreme weather events, such as droughts or floods, can disrupt corn production, leading to reduced yields and increased prices. similarly, trade tensions between major cornproducing countries can impact market access and trade flows, affecting price dynamics on a global scale. geopolitical events and policy decisions also play a crucial role in shaping corn prices. changes in trade agreements, tariffs, and subsidies can influence market conditions, creating uncertainties for market participants. moreover, government policies promoting or restricting the use of corn-based ethanol as a renewable fuel source can impact both demand and prices in the corn market. disease outbreaks, such as the spread of corn diseases or pests, pose additional challenges to corn production and prices. crop diseases can devastate yields, leading to supply shortages and price spikes. furthermore, the emergence of new pests or pathogens can necessitate costly control measures, adding to production costs and potentially driving up prices for consumers. sudden climate changes, including shifts in temperature and precipitation patterns, pose significant risks to corn production worldwide. climate variability and extreme weather events, exacerbated by climate change, can disrupt planting schedules, reduce yields, and increase the likelihood of crop failures. these climate-related risks underscore the importance of adaptive strategies and resilience-building efforts within the agricultural sector to mitigate the impacts of climate change on corn prices and food security. in recent years, the growing demand for corn-based products, such as ethanol and animal feed, has contributed to increased competition for corn resources. the expansion of biofuel production, driven by renewable energy policies and environmental concerns, has led to a surge in corn consumption for ethanol production. this heightened demand for corn in non-food sectors has implications for food prices and market dynamics, underscoring the need for integrated approaches to balance competing demands for corn resources. moreover, speculation in commodity markets can pa ge 13 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 124-135, 2024 exacerbate price volatility, amplifying the effects of supply and demand shocks on corn prices. financial market participants, including investors and hedge funds, engage in trading corn futures and derivatives, seeking to profit from price movements. however, speculative activities can introduce additional uncertainties and distortions into the corn market, leading to heightened price volatility and market inefficiencies. in light of these complex dynamics, forecasting corn prices becomes imperative for stakeholders across the corn supply chain. accurate price forecasts enable farmers to make informed decisions regarding planting, input use, and marketing strategies. similarly, policymakers rely on price forecasts to formulate agricultural policies, manage food security risks, and mitigate market disruptions. additionally, consumers and food industry stakeholders use price forecasts to anticipate changes in food prices and adjust consumption patterns accordingly. given the importance of price forecasting in navigating the uncertainties of the corn market, researchers have developed and applied various time series forecasting models to predict corn prices. these models utilize historical price data, along with relevant explanatory variables, to generate forecasts of future price movements. by evaluating the performance of different forecasting models and refining their methodologies, researchers aim to enhance the accuracy and reliability of corn price forecasts, thereby empowering stakeholders to make informed decisions in an increasingly complex and dynamic market environment. conclusion the price of corn has exhibited fluctuations over the years, starting from a relatively low point in early 2014 and steadily increasing, peaking around march of that year. these elevated prices persisted throughout 2014 until mid-march of 2015, after which they began to decline, reaching a nadir in mid-2016. subsequently, from late 2016 to 2017, global corn prices displayed some volatility but maintained a moderate and stable trajectory compared to the preceding years of 2014 and 2015. from 2018 to early 2019, corn’s price gradually increased, reaching a relatively high point by early 2019. during the period from mid2019 to 2020, there were some fluctuations in price, but overall stability within a moderate range was observed. however, from mid-2020 onwards, a significant surge in corn prices ensued, culminating in a record high by early 2022, followed by a gradual decline to lower levels by early 2024. the application of the box-jenkins methodology in time series forecasting effectively identified a seasonal pattern in global corn prices, resulting in an arima (0,1,1)(0,0,1)[12] model. furthermore, the model pinpointed the influence of a first-order non-seasonal moving average term and a first-order seasonal moving average component on the series. the validation of the model’s accuracy was conducted through the analysis of the ljung–box q–test and aic value. such forecasting endeavors offer substantial benefits to farmers, stakeholders, policymakers, and investors alike. these forecasts provide farmers and stakeholders with valuable insights for price adjustments, while policymakers can utilize this information to formulate well-informed marketing strategies. additionally, 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(2021). corn starch: quality and quantity improvement for industrial uses. plants, 11(1), 92. https://doi.org/10.3390/plants11010092 pa ge 1 pa ge 1 american journal of applied statistics and economics (ajase) impact of liquefied natural gas exports on the nigerian exchange rate: an ardl cointegration approach, 2000 to 2021 kufre jerome udoudo1*, ijeoma emele kalu2, koyejo oduola3 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v2i1.2216 https://journals.e-palli.com/home/index.php/ajase article information abstract received: december 02, 2023 accepted: december 29, 2023 published: december 31, 2023 this study aimed to investigate the impact of liquefied natural gas (lng) exports on the exchange rate of nigeria. the investigation employed an autoregressive distributed lag model (ardl) methodology to analyse data spanning the years 2000 to 2021 using a biannual dataset. the empirical findings provided evidence of a statistically significant and positive influence of liquefied natural gas exports on the exchange rate. this is supported by the results obtained from the short-term analysis. the research results revealed no causal link between the exports of lng and the exchange rate in nigeria. the study concludes that lng exports cause the naira to depreciate. the research suggested that the government should actively endorse and facilitate the expansion of non-oil and gas sectors, including agriculture, manufacturing, and services, to cultivate a more varied export portfolio. moreover, adequate reserves can be used to stabilize the naira during periods of volatility caused by fluctuations in lng exports or other external factors. keywords ardl model, exchange rate, lng exports, nigeria 1 emerald energy institute, university of port harcourt, nigeria 2 department of economics, university of port harcourt, nigeria 3 department of chemical engineering, university of port harcourt, nigeria * corresponding author’s e-mail: kufrej@yahoo.com introduction nigeria possesses considerable reserves of natural gas, which have been a source of strategic importance since 1999 with the establishment of the liquefied natural gas (lng) industry. this development aligns with the nigerian gas master plan, aiming to broaden the country’s revenue streams and diminish reliance on the exportation of crude oil. the exportation of lng has facilitated the generation of foreign exchange earnings, the attraction of investment, and the promotion of economic growth (khan, 2015). nevertheless, the correlation between lng exports and the exchange rate in nigeria is intricate and diverse, as it is shaped by a range of economic, policy, and external factors. over the course of the past two decades, nigeria has experienced substantial growth in its lng sector, establishing itself as a prominent producer of natural gas within the african continent. the exploration of natural gas and subsequent development of the nigerian lng plant has significantly contributed to the diversification of the nation’s export portfolio, thereby mitigating its substantial reliance on crude oil. exports of lng refer to the international trade of lng, which is a clear, odourless, and non-toxic form of natural gas that has been converted into a liquid state through a cooling and condensation process. lng is characterised by its transparency, lack of odour, and non-toxic properties. lng is predominantly comprised of methane and is generated through the process of cooling natural gas to a temperature of approximately -162 degrees celsius (-260 degrees fahrenheit). this cooling procedure results in a reduction in volume, facilitating efficient and secure transportation of lng via tanker vessels (onolehemhen, et al., 2017). lng exports encompass the commercial transaction and transportation of lng from the exporting nation to various importing countries or global markets. specialised lng carriers are responsible for the transportation of lng to designated receiving terminals situated in the importing nations. at the terminals where the lng is received, a process called vaporisation is employed to convert the lng back into its gaseous state, enabling its distribution and utilisation. the significance of lng exports has grown considerably within the global energy market due to the various advantages it offers in comparison to conventional pipeline gas transportation. this technology offers enhanced flexibility with regard to the distribution of natural gas, enabling countries lacking direct pipeline connections to effectively access these valuable resources. the exportation of lng plays a significant role in enhancing energy security, promoting the diversification of energy sources, and facilitating international trade in natural gas. countries that export lng, including qatar, australia, the united states, and nigeria, have a significant impact on satisfying the increasing global demand for natural gas (chien-chiang et al., 2011; barril & navajas, 2015; felipe et al., 2018). these nations allocate resources towards the establishment of lng production facilities, infrastructure, and export terminals to facilitate the processes of liquefaction, storage, and transportation that are integral to the trade of lng. the growth of lng exports has been propelled by various factors, including the rising need for more environmentally friendly energy options, economic motivations, geopolitical influences, and advancements in lng technology (hong, 2013). the expansion of the lng trade has significantly altered the energy sector, facilitating the linkage between regions that produce natural gas and those that consume it. this development has played a pivotal role in fostering energy collaboration and enhancing global integration. in pa ge 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 1-14, 2024 general, the exportation of lng constitutes a substantial element within the realm of global energy commerce. this practice facilitates the cross-border utilisation of natural gas reserves and contributes to the enhancement of energy stability and economic progress for both exporting and importing nations. exchange rate dynamics in nigeria exchange rate dynamics in nigeria refer to the fluctuations and movements of the nigerian currency, the naira, in relation to foreign currencies, particularly major global currencies such as the us dollar, euro, and british pound. the exchange rate, or the cost of exchanging one currency for another, has a major impact on a nation’s capacity to trade internationally, attract foreign investment, and maintain economic growth and prosperity. the value of the nigerian naira is set in part by the supply and demand for foreign currency on the international market. on occasion, however, the central bank of nigeria (cbn) steps into the market to control currency rate stability and prevent undue volatility. several variables affect the fluctuation of the nigerian currency exchange rate including: balance of trade currency supply and demand in nigeria are both influenced by the country’s trade balance, which is the amount by which exports exceed imports. when nigeria has a trade surplus, there is an increased demand for naira, strengthening its value. conversely, a trade deficit puts downward pressure on the naira. foreign direct investment (fdi) the value of a currency may rise or fall in response to changes in the volume of foreign direct investment. foreign direct investment (fdi) may cause a rise in the value of the naira since it shows confidence in the country’s economy and boosts demand for the naira. oil prices since nigeria is a major oil exporter, the country’s currency value is very sensitive to changes in international oil prices (yunusa, 2020). since a large proportion of nigeria’s foreign currency revenues come from oil exports, falling oil prices might cause the naira to weaken. inflation and interest rates depreciation of a currency may occur if inflation rates are persistently high (salisu & ayinde, 2016). interest rate changes and other monetary policy measures taken by the central bank affect inflation and, by extension, the value of a currency’s exchange rate. the nigerian exchange rate has experienced periods of volatility and depreciation over the years, attributed to factors such as economic imbalances, external shocks, policy decisions, and market expectations (vincent et al., 2021). the cbn has used interventions in the foreign currency market, capital controls, and foreign exchange restrictions to maintain exchange rate stability. given nigeria’s heavy reliance on oil exports and the potential for lng exports to contribute significantly to its foreign exchange earnings, it becomes essential to examine the relationship between lng exports and the nigerian exchange rate. the economic dynamics, policy ramifications, and possible risks connected with lng exports may all be better understood with a firm grasp of this connection. the importance of the research is huge. using the ardl cointegration method, this research aims to assess lng exports and the nigerian currency rate from 2000 to 2021. consequently, this study will greatly benefit key stakeholders in nigeria. specifically, understanding the impact of lng exports on the nigerian exchange rate is of utmost importance for policymakers, economists, and industry stakeholders. the exchange rate is a crucial determinant of a country’s competitiveness in the global market, investment attractiveness, and overall economic stability. exploring the dynamics between lng exports and the exchange rate will shed light on the potential effects of lng industry developments on nigeria’s macroeconomic performance. the main goal of this study was to: i. to assess the impact of lng exports on the nigerian exchange rate ii. to determine the causality between lng exports and the nigerian exchange rate. while many studies in nigeria and elsewhere have focused only on the effects of exchange rates and volatility on exports, others have examined the role of exchangeratese in stimulating economic expansion. this research set out to address the gap that exists in the literature. using an autoregressive distributed lag (ardl) model proposed by pesaran et al. (2001), this study aimed to empirically examine the influence of nigeria’s lng exports on the country’s exchange rate. consequently, the following hypotheses in null form (h0) guided the study: i. h01: lng exports have no significant and positive impact on the nigerian exchange rate ii. h02: there is no causal relationship between lng exports and the nigerian exchange rate the present paper is structured into distinct sections. the initial segment encompasses several key components, including the introduction, research problem, study objective, hypothesis statement, theoretical background, and empirical literature review. the subsequent section provides an overview of the methodology employed and the data utilised in the study. the subsequent section of the manuscript provides an exposition of the findings and subsequent analysis. finally, the fourth section discusses the conclusion and recommendations. theoretical background this analysis was founded on the theory of export-led development. the export-led growth theory is founded on the perspectives of classical and neo-classical economic theory. export is the primary determinant of economic pa ge 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 1-14, 2024 development, according to this theory (schmidt, 2020). the theory of export-led development is an economic framework that emphasises the correlation between a country’s exports and its overall economic growth. the proposition asserts that increasing the quantity and value of exports can have a positive effect on a country’s economic development, resulting in improved quality of life and higher employment rates. this theory emphasises the significance of increasing a nation’s competitiveness in the international market by producing in-demand products and services. this is accomplished by offering products of superior quality, at competitive prices, and per international standards. to achieve export-driven economic growth, nations must implement policies and strategies that enable them to access international markets, reduce trade barriers, and negotiate advantageous trade agreements with other nations. according to the theoretical framework utilised in this study, it is posited that an increase in lng exports has the potential to stimulate economic growth, thereby influencing the exchange rate. enhanced lng export revenues have the potential to boost the value of the nigerian naira by increasing the inflow of foreign currency and bolstering the country’s foreign exchange reserves. empirical literature review the study conducted by musa et al. (2019) examined the influence of crude oil price and exchange rate on nigeria’s economic growth from 1982 to 2018 using the ardl approach. it was determined that these factors exert a notable positive impact on both the long-term and short-term durations. the study proposes that a strategy of diversifying revenue streams through agricultural activities, industrial development, and investment can effectively mitigate the dependence on crude oil and the consequent income volatility resulting from fluctuations in oil prices. sieng et al. (2020) examined the factors influencing export levels in indonesia, the philippines, malaysia, and thailand. the study aimed to estimate the effects of import, exchange rate, foreign direct investment (fdi), inflation, and crude oil on exports. three econometric techniques and three-panel data estimation models were employed to achieve this. the findings indicated a positive relationship between import and exchange rate with exports in all four countries, while fdi exhibited a significant negative impact. based on these results, the study recommends that governments prioritise the provision of peace and political stability as a means to stimulate exports and attract greater levels of investment. kandil and mirzaie (2002) looked at how changes in exchange rates impacted production and prices in different industries in the united states. according to the study’s results, expansionary and contractionary variables have a neutral effect on the rate of increase in industrial real production. nevertheless, the appreciation of the dollar results in a noteworthy decrease in price inflation across various sectors, with a particular emphasis on the finance industry. the outcome aligns with the decrease in overall demand caused by net exports and the rise in overall supply resulting from the decreased expense of imported intermediate goods. the study’s findings indicate that the limited level of openness observed in us industries results in moderate price effects caused by external shocks and fluctuations in exchange rates, while not significantly impacting output growth. hence, the lack of empirical support for the detrimental impact of dollar appreciation on economic performance in various sectors of the united states refutes the aforementioned concerns. udoudo et al. (2023) investigated the influence of lng exports on inflation in nigeria during the period spanning from 2000 to 2021. the study employed the autoregressive distributed lag (ardl) bound co-integration approach to examine the impact of inflation in both the long term and short term, revealing diverse effects. the impact of natural gas prices and crude oil prices on inflation was negative, whereas lng exports did not have a significant effect. the study suggests that the government should maintain its support for the lng sector, with a particular emphasis on investing in infrastructure and technology to improve competitiveness and efficiency. using a var (vector autoregression) model, akpan’s (2009) research investigated the dynamic connection between oil price shocks and important macroeconomic indicators in nigeria. positive and negative shocks were shown to have equally large and asymmetrical effects on inflation, with the former causing a rise in real national income and the latter in export profits. however, the author discovered that some of the gains are counterbalanced by reduced demand for exports due to economic downturns experienced by trading partners. the study also revealed a strong positive correlation between oil price fluctuations and government expenditures but found minimal influence on industrial output growth. the author emphasizes the need for policymakers to implement policies that enhance and stabilize the nigerian economy, prioritizing measures like exploring alternative sources of government revenue, implementing fiscal discipline, and saving oil boom proceeds to better withstand future oil shocks. in their study, hassan et al. (2013) conducted an analysis to examine the influence of various macroeconomic factors, including exchange rate and economic growth, on the performance of pakistan’s exports. the researchers utilised time series data for their investigation. the researchers employed the augmented dickey-fuller (adf) unit roots test and the autoregressive distributed lag (ardl) model to ascertain the long-term association between the variables. the research discovered a sustained equilibrium connection between the export performance of pakistan and its determinants. the impact of the exchange rate, gross domestic production, and trade openness on export performance are found to be positive and statistically significant, whereas the influence of foreign direct investment is deemed to be pa ge 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 1-14, 2024 insignificant. the labour force estimates suggest that export-oriented sectors, which necessitate a skilled labour force, are adversely impacted by both higher growth and a deficiency in skills. alam (2010) evaluated the influence of taka’s actual exchange rate depreciation on bangladesh’s export revenues. the macroeconomic series utilised in this study are non-stationary, specifically integrated at order one, but they do not exhibit cointegration. the application of the granger causality test utilising the vector autoregressive (var) model yielded results indicating the absence of a causal relationship between real depreciation and export earnings. this underscores the necessity of conducting sub-sector analysis, evaluating incentive policies, and enhancing the proportion of local commodity exports. however, before enacting devaluation or depreciation policies, it is necessary to evaluate the negative impacts of depreciation on macroeconomic indices, particularly inflation. aliyu’s (2009) research aimed to quantitatively assess the impact of exchange rate fluctuation on the volume of nigeria’s non-oil exports. the research used a basic analytical technique, which postulated that the naira exchange rate volatility, us dollar volatility, nigeria’s terms of trade, and the index of openness (opn) all have a role in the fluctuation of non-oil exports. the data supported the existence of a unit root at the level, however, the lack of stationarity was rejected as a null hypothesis. the results of the cointegration study show that non-oil exports are linked to the basic variables in a stable, long-term equilibrium. the report suggests methods to increase openness and promote stability in the nigerian currency market. berman et al. (2012) studied the response of french firms to real exchange rate fluctuations from 1995 to 2005. they found that high-performance firms increase their markup and export volume, while low-performance firms decrease demand elasticity. this indicates that different approaches to pricing the market may explain why changes in the value of one currency have relatively little effect on export volumes as a whole. identifying and compensating for the effects of currency exchange rate variations on export volumes was emphasised. udoudo et al. (2023) used biannual data to analyse the effect of nigeria’s lng exports on the country’s gdp from 2000 to 2021. the study used an ardl model and a unit root test to analyze the variables. the results showed a positive association between lng exports and the nigerian economy, with a 1% increase in lng exports resulting in a 0.72% increase in gdp. in the short term, a marginal increase in lng exports would lead to a 0.23% gdp gain. to ensure a steady supply of natural gas to nigeria’s lng plants, they advised the government to increase spending on the sector and take the initiative to push for the construction of floating lng plants in regions where laying pipelines would be too costly. however, their study did not consider the effect of lng exports on nigeria’s exchange rate. using monthly data from 1996–2015, oluyemi and isaac (2017) studied how currency exchange rates affected nigeria’s exports and imports. they looked at the correlation between the usd/ngn exchange rate, exports, and imports using a vector autoregression (var) model with three independent variables. the research indicated that imports and exchange rates had a positive but statistically insignificant association, whereas exports had a negative effect on exchange rates. the research found that fluctuations in exchange rates did not significantly influence imports and exports in nigeria. the study suggests promoting export activities, focusing on the non-oil sector, to foster entrepreneurial development and mitigate excessive import levels. nguyen’s (2016) study found a significant positive correlation between exports and vietnam’s economic growth from 1990 to 2015. the study found that exports accelerate industrialization and modernization, positively affecting gdp growth in both current and future years. the author recommended that local governments and export enterprises foster export activities and their effects, promoting sustainable economic growth in emerging and developing nations reliant on commodity exports. however, the study did not consider the potential impact of exports on vietnam’s exchange rate. the effect of currency rate volatility on nigeria’s export from 2008 to 2021 was researched by musa et al. in 2023. the research used secondary data from the statistics database and the ardl-error correction model and bound test. although only real effective exchange rate volatility was statistically significant, the analysis indicated that long-run exchange rate volatility was negative. while exchange rate volatility generally had a negative shortterm impact, real effective and nominal effective exchange rate volatility were statistically significant. to increase trade and broaden export markets, the research suggested stabilising the value of the naira and diversifying nigeria’s export mix. ikechi and nwadiubu’s (2020) study explored the potential positive effects of exchange rate fluctuations on nigeria’s international trade dynamics, specifically looking at the potential for increased export and import transactions. the research used secondary data from 1996 to 2018 and employed econometric methodologies to establish correlations. the var model estimations showed a negative correlation between exports, imports, and the real effective exchange rate (reer) during the present period. an increase in both export and import during a specific year resulted in a decrease of approximately 0.9% and 0.4% in the reer, respectively. the analysis of variance decomposition analysis revealed that shocks play a significant role in accounting for the variations observed in the real effective exchange rate and the levels of exports and imports. the impulse response analysis showed a negative correlation between exports and the real effective exchange rate, while imports significantly impacted exports. the arch modelling framework posits a primary arch effect and a statistically significant pa ge 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 1-14, 2024 garch component. the findings suggest that the real effective exchange rate (reer) exhibits high volatility, leading to a clustering effect on import and export trading activities in nigeria. since financial shocks tend to amplify changes in exchange rates, the authors suggest employing monetary and fiscal actions to mitigate the detrimental effects of these swings. goya (2020) looked into whether or not there was a connection between a country’s currency exchange rate and the diversity of the goods it exported as part of its research project. the primary data utilised in this research was sourced from the world trade flows dataset and the international monetary fund’s international financial statistics, covering the period from 1962 to 2000. various estimation techniques were employed, including fixed effects, dynamic gmm, mean group, and pooled mean group estimators. the findings indicate a positive association between export variety and a depreciated exchange rate, while a negative relationship was observed between export variety and exchange rate volatility. furthermore, these relationships were found to be more pronounced for goods with higher levels of technological intensity. rümeysa’s (2018) research attempted to learn how fluctuations in the value of the turkish lira affect the country’s exports. for this study, the researcher used an ardl border test and a model for correcting statistical errors. by evaluating a dataset with monthly observations from january 1995 to january 2017, this study looked into how changes in exchange rates affected export volumes. to check for cointegration between variables, the bound test method was used. the analysis found that both the long-term and short-term indicators’ observed coefficients lined up with expectations. as a result, it seems that imports have a favourable effect on the industrial output index and exports, both in the short and long terms. nonetheless, it can’t be denied that the effective exchange rate index and volatility have been negatively affected both in the long and short term. djatmiko and nugroho (2019) performed research between 1996 and 2017 to analyse the effect of indonesia’s oil and gas exports and non-oil exports on the country’s foreign exchange reserves. researchers used 22 yearly observations analysed using spss 24. indonesia’s non-oil and gas exports and oil and gas exports are the independent variables, while the country’s foreign currency reserves are the dependent variable. the researcher used multivariate linear regression analysis to look at how the independent factors affected the dependent one. the research found that indonesia’s foreign currency reserves are influenced positively and statistically significantly by both oil and gas export and partial export of goods other than oil and gas. the authors suggested that the non-oil and gas sector should engage multiple government agencies, including the ministry of trade, the coordinating ministry for economic affairs, and the ministry of transportation, among others. this collaboration would enable private entities to work in synergy with regional governments as contributors to the commodity sector. in their study, wildan et al. (2020) conducted an analysis of the impact of macroeconomic factors on the management of natural gas exports in indonesia. the researchers utilised secondary time series data spanning a period of 22 years, specifically from 1995 to 2017. the variables considered in this study encompassed domestic consumption, exchange rate, international price, and gdp per capita of the importing country. the employed analytical approach was the auto-regressive distributed lag (ardl) method. the findings of the analysis indicated that, in the immediate term, various factors including domestic consumption, exchange rates, natural gas prices, and gdp per capita exert a significant influence on the magnitude of natural gas exports. in the long term, the outcomes align with those observed in the short term, wherein various independent variables, including domestic consumption, exchange rates, international prices, and gdp per capita, notably influence the magnitude of natural gas exports. bakari and mabrouki (2017) analysed the effect of exports and imports on panama’s gdp. each year’s data from 19802015 was checked using the granger-causality tests and the johansen co-integration analysis of the vector auto regression model. the results of the study showed no connection between exports, imports, and gdp growth in panama. however, it was shown that imports and exports contribute to economic development in both directions. these results indicate that international commerce is a major contributor to panama’s thriving economy. dogo and aras (2021) examined how naira-dollar exchange rate volatility affected nigerian imports and exports between 1990 and 2019. the cbn, nbs, and international financial statistics provided data for all indicators except volatility. the autoregressive distributed lagged (ardl) and exponential generalised autoregressive conditional heteroscedasticity (egarch) models were used to evaluate the shortand longterm associations between naira-dollar exchange rate fluctuations and imports and exports. long-term research showed that fluctuations in the naira-dollar exchange rate were related to nigeria’s imports and exports, while short-term research did not find any such relationship. to enhance the trade balance, the report advises government to continue export promotion and import reduction. using data from 2005’s first quarter to 2020’s fourth quarter, duru et al. (2022) investigated the impact of fluctuating currency rates on nigeria’s exports. the amount of exchange rate volatility, with an emphasis on the nominal effective exchange rate, was evaluated using the arch model and its later expansions, including the garch, tarch, and egarch models. the shortand long-term effects of exchange rate volatility on exports were examined using the autoregressive distributed lag (ardl) bounds test approach. results showed that currency fluctuations do occur. furthermore, while the influence of exchange rate changes on exports was determined to be statistically negligible, the study’s pa ge 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 1-14, 2024 results nonetheless revealed that such fluctuations had a negative effect on exports. the report concludes that the central bank of nigeria should work towards establishing consistent exchange rate systems via the implementation of appropriate exchange rate rules. additionally, the government must establish a conducive environment that facilitates the production of goods that are suitable for exportation. vivoda (2022) looks into the top five lng producers in the world, which are australia, qatar, the usa, russia, and malaysia. by focusing on energy exports, the study tried to fix the fact that most research on energy policy is focused on energy imports. in the piece, the supplies of lng by five companies were compared, taking into account how much they were different from each other. it used eight factors to describe the trends of lng export diversification among providers from 2009 to 2021. it also used a well-known method, the herfindahlhirschmann index of market concentration, to calculate and classify the amount of lng export diversification. the paper found that the position and the difference in natural gas prices between regional markets have the most power to explain. this result has important policy implications for how lng-exporting nations act. it also shows how sensitive these nations are to economic forces. aboelsoud (2010) research addressed important empirical questions regarding the relationship between natural gas exports and egyptian economic growth by extending dirtsakis’s model with the addition of the labour force into the model and further by addressing the issue in a disaggregated framework. this study analyzed the issue of elg and ngelg hypotheses in egypt using the var analysis, quarterly time-series data over the period 1991: q1-2009:q4. the empirical results tend to favour the effectiveness and validity of the elg and ngelg hypotheses for egypt. in other words, the results indicated that natural gas exports promote economic growth. he recommended that egypt should be the key gas “trader” for decades to come because based on geographical location we are the african gate to the european and central asia gas markets. truong et al. (2022) conducted a study examining the asymmetric effects of exchange rate volatility (erv) on vietnam’s international trade during the period spanning from january 2010 to december 2019. the research employed time-series data and the nonlinear autoregressive distributed lag (nardl) model to analyse the correlation between exchange rate volatility (erv) changes and the trade balance. the study’s findings indicate that in the short term, positive changes in the exchange rate volatility (erv) negatively impact the trade balance. however, in the long term, improvements in erv have positive effects on the trade balance. the negative changes observed in the erv did not yield a statistically significant impact. to ensure the trade balance in vietnam can be maintained over the long term, they suggested that the government adopt a policy of a stable currency rate. methodology description of data this study examines the impact of exporting liquefied natural gas (lng) on the exchange rate of nigeria from the year 2000 to 2021. the biannual data series of the naira/dollar exchange rate, nigeria lng exports, henry hub natural gas price, and brent crude oil price have been considered. the dependent variable in this study was the exchange rate, while the independent variables were lng exports, natural gas price, and crude oil price. this study’s data was collected from reputable sources such as the bp statistical bulletin, statista, the energy information administration (eia), and the world development indicators. empirical methodology the primary objective of this research was to analyse the effect of nigeria’s lng exports on the country’s currency exchange rate. the autoregressive distributive lag (ardl) test for co-integration, established by pesaran et al. (2001), is used in this research. this methodology is utilised to conduct both abound and cointegration tests. this methodology employs empirical analysis to examine the variables’ long-term relationships and shortterm dynamic interactions. one of the main benefits of this methodology is its utilisation of regressors that exhibit stationarity at either i(1) or i(0), or potentially a combination of both (ogunnusi & ajibode, 2023). this characteristic assists in circumventing the challenges that arise when testing for a unit root. the ardl approach has been found to effectively address endogeneity concerns, as demonstrated by javed and husain (2020). the application of the ardl test to examine cointegration in a small sample size is relatively straightforward, whereas the johansen technique necessitates a larger sample size to conduct the cointegration analysis effectively. the aforementioned methodology expeditiously assesses the variables, regardless of their disparate optimal lags (ozturk & acaravci, 2010). the multivariate model (equation 1) was employed to investigate the association between the variables. prior to conducting further analysis, the values were logarithmically transformed to mitigate potential heteroscedasticity in the data. lexrt = β0 + β1llngt + β2lngpt + + β3lcopt + ϵt (1) where: in eq. (1) lexr is the natural log of the exchange rate, llng is the natural log of lng exports, lngp is the natural log of natural gas price, and lcop is the natural log of crude oil price. whereas ‘t’ is the time period, however, β0, is the intercept, β1, β2, and β3 are the coefficient of slop respectively, and ε is the white noise error. ardl estimation and specification equation 2 presents the formulation of the multivariate unrestricted error correction model (uecm) within the framework of the ardl-bound approach. △lexrt = α0 + ∑p (i=1)α1i △lexr(t-1) + ∑q1 (i=0)α2i △llng(t-1) + ∑q2 (i=0)α3i △lngp(t-1) + ∑q3 (i=0) α4i △lcop(t-1) + β1lexrt-1 + β2llngt-1 + β3lngpt-1 + β4lcopt-1 + ϵt (2) pa ge 7 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 1-14, 2024 here, lexr refers to the natural log of exchange rate; llng is the natural log of lng export quantity, lngp is the natural log of natural gas price, and lcop stands for the natural log of crude oil price. δ is used to present the operator difference, the error is denoted by ϵt; p is the optimal lag lengths of the dependent variable and q1, q2 and q3 are the optimal lag lengths of the independent variables, α0 is the constant, α1 α4 are the coefficients of the differenced variables, β1 β4 are the coefficients of the lagged variables. the bound test is broken down into three distinct steps and relies on joint f-statistics to determine the outcome of the cointegration process. step one this analysis assists in examining the potential longterm relationship between the series using the ordinary least squares (ols) method. ols helps determine the combined significance of coefficients at the lagged level in equation 2. the null hypothesis posited for the series can be formally expressed as “h0: γ₁ = γ₂ = γ₃ = γ₄ = γ₅ = γ₆ = γ₇ = 0”, conversely, the alternative hypothesis is articulated as “h1: γ₁ ≠ γ₂ ≠ γ₃ ≠ γ₄ ≠ γ₅ ≠ γ₆ ≠ γ₇ ≠ 0.” the utilisation of bound test is employed to ascertain the upper bound value, represented as i (1), and the lower bound value, represented as i(0), within the realm of regression analysis. if the computed f-statistic falls below the lower bound value, it becomes implausible to dismiss the null hypothesis, thereby signifying the lack of cointegration among the variables. if the calculated f-statistics surpasses the predetermined upper threshold, it serves as an indication to refute the null hypothesis positing the absence of cointegration among the variables. the manifestation of this rejection implies the existence of a long-term association and co-movement between the variables. as per the scholarly work of javed and husain (2020), if the computed f-statistic lies within the confines of the lower and upper bound values, the outcomes of the test are deemed to be inconclusive. step two if cointegration is detected among the variables, the subsequent procedure involves estimating the long-run coefficients. the ardl model is expressed in equation 3. lexrt = β0 + ∑p (i=1)β1 lexr(t-1) + ∑q1 (i=0)β2 llng(t-1) + ∑q2 (i=0)β3 lngp(t-1) + ∑q3 (i=0)β4 lcop(t-1) (3) step three the final stage involves the estimation of the shortterm coefficient when the variables exhibit a long-term relationship, utilising an error correction model (ecm) as depicted in equation 4 below. △lexrt = α0 + ∑p (i=1) α1i△lexrt-1 + ∑q1 (i=0) α2i △llng(t-1) + ∑q2 (i=0) α3i △lngp(t-1) + ∑q3 (i=0) α4i △lcop(t-1) + φect(t-1) + ϵt (4) where φ reflects the rate at which the error correction term’s adjustment coefficient is adjusted, and the coefficients α1-α4 represent the short-run dynamic. the next step is to determine the causal link between the variables once the ardl-bound cointegration test has been completed. for the goal of analysing the causality, the toda-yamamoto granger causality test is used. eddrief-cherfi and kourbali (2012) argue that the mere existence of causality within the elements is insufficient for determining the directionality. according to ekeke (2020), the version proposed by toda-yamamoto is considered to be more reliable for conducting granger causality tests. this assertion is based on the fact that the toda-yamamoto version is justifiable regardless of the co-integration order of the variables. results and discussion descriptive analysis both the dependent and the independent variables have descriptive statistics shown in table 1. the exchange rate average value is 4.456676, with the maximum and minimum values recorded at 5.324335 and 3.904902, respectively. the maximum value of lng exports is recorded as 2.677161, while the minimum value is 1.020651. it is estimated that the average value of lng exports is 2.244379. the price of natural gas attained its peak at 1.530259 and its minimum at -0.070557, with a mean value of 0.708675. the average price of petroleum oil is 3.356965, with a high of 4.054217 and a low of 2.486000. the table presents the values of skewness, kurtosis, jarque-bera, and other descriptive statistics parameters for the variables. multicollinearity test the variance inflation factor (vif) test was administered to determine the presence of multicollinearity among the variables. the centred vif values for all variables, as presented in table 2, demonstrate values below 10.0. this observation provides evidence that the data utilised in this study does not exhibit any multicollinearity concerns. unit root tests for stationarity hatmanu (2020) stated that checking the stationarity of the variables under study is the first stage in the methodology of most time series modelling research. the unit root test was used to determine if the data were stationary and the level of correlation between the variables. for this purpose, we used the adf (dickey & fuller, 1979) version of the dickey-fuller test. stationarity at the level is not shown by the variables lexr, lngp, and lcop, as seen in table 3. therefore, at the 1%, 5%, and 10% significant levels (javed & husain, 2020), it is not possible to reject the null hypothesis, which claims the non-stationarity of these variables. after undergoing first-level differencing, represented by i(1), the relevant variables are significant and stationary. thus, it may be concluded that h0 is false. however, for low values of i(0), llng displays a stationary characteristic. this lends credence to the use of the ardl cointegration modelling approach used here. pa ge 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 1-14, 2024 ardl model the ardl model is estimated utilising automatic lag selection in e-views version 10, as denoted by equation (2). the findings presented in table 4 demonstrate that the llng variable exerts a statistically significant influence on the present exchange rate. nevertheless, the present values of lngp and lcop do not demonstrate a statistically significant impact on the current lexr value. the results of the empirical analysis indicate that the previous values of lexr, llng, lngp, and lcop do not have a statistically significant effect on the current value of lexr, with the exception of the first lag of lexr and the fifth lag of lngp. these two variables show statistical significance at a 1% level. the overarching model demonstrates a significant level of statistical significance. the ardl bound cointegration test the ardl bounds cointegration test is utilised to determine the existence of a long-term relationship between the dependent and independent variables. the assessment of the co-integration relationship among the variables is conducted by utilising equation (2). the null hypothesis proposed in this study postulates the lack of a significant long-term association. based on the findings of udoudo et al. (2023) and nkoro & uko (2016), when the calculated value of the ‘f’ statistic is lower than the critical value i(0), it suggests the lack of a statistically significant relationship between the variables, resulting in the failure to reject the null hypothesis. if the calculated ‘f’ statistic exceeds the critical value of i(1), it indicates a significant relationship and leads to the rejection of the null hypothesis. conversely, a value falling within the range of i(0) and i(1) is regarded as inconclusive. the results displayed in table 5 demonstrate that the calculated ‘f’ statistic (18.14008) exceeds the significance levels for both i(0) and i(1), indicating the presence of a long-term association between the dependent and independent variables. table 1: descriptive statistics lexr llng lngp lcop mean 4.456676 2.243379 0.708675 3.356965 median 4.336665 2.443149 0.694079 3.400700 maximum 5.324335 2.677161 1.530259 4.054217 minimum 3.904902 1.020651 -0.070557 2.486000 std. dev. 0.411366 0.471463 0.406584 0.481371 skewness 0.791239 -1.134865 0.351119 -0.326850 kurtosis 2.233465 3.053467 2.33798 2.053623 jarque-bera 5.668329 9.449971 1.707581 2.425416 probability 0.058768 0.008871 0.425798 0.297391 sum 196.0937 98.70867 31.18171 147.7065 sum sq. dev. 7.276539 9.557929 7.108355 9.963894 observations 44 44 44 44 source: author’s estimation (2023) table 2: variance inflation factors variable coefficient variance uncentered vif centered vif llng 0.001775 86.5191 3.579859 lngp 0.001079 6.651327 1.618842 lcop 0.001533 163.5737 3.222225 c 0.005584 51.84556 na source: author’s estimation (2023) table 3: results of adf unit-root test variable level first difference integration degree t-statistic p-value t-statistic p-value lexr 0.753961 0.9919 -3.887904 0.0047 i(1) llng -4.339716 0.0015 -1.376465 0.5828 i(0) lngp -1.43085 0.5579 -4.426946 0.0011 i(1) lcop -2.528888 0.1161 -4.322646 0.0014 i(1) source: author’s estimation (2023) pa ge 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 1-14, 2024 ardl long-run and short-run estimation the long-run coefficients shown in table 6 were calculated by estimating the long-run model defined by equation (3) after a co-integration connection had been established among the variables. the analysis found that at the 1% level of significance, the exchange rate in nigeria was positively affected by lng exports. this indicates that the exchange rate between the nigerian naira and the us dollar would rise in tandem with an increase in the value of lng exports from nigeria. this conclusion indicates that an increase in the naira’s export volume of lng will cause the naira to depreciate by 1.62 per cent relative to the dollar. djatmiko and nugroho (2019) and sieng et al. (2020) findings are consistent with the results achieved here. there is a negative and insignificant relationship between the price of natural gas and nigeria’s exchange rate. the exchange rate of nigeria falls by 0.13 percentage points when the price of natural gas at the henry hub rises, albeit, not statistically significant. the exchange rate in nigeria is significantly influenced by the price of brent crude oil, and this influence is negative. an increase in the export price of brent crude oil results in an appreciation of the naira by 1.53 per cent. this finding agrees with musa et al. (2020) and henry (2019). equation (4) is used to define the short-term ardl model of lexr, which is shown in table 7 along with the variables llng, lngp, and lcop. according to the estimations of the coefficients, the variable, past values of lexr does have any effect on its present values. the present values and lag 2 value of llng exhibit a substantial and negative impact on lexr, with statistical significance at the 5% level. this finding does not align with the outcomes observed in the longrun analysis. additionally, the lag 1 value demonstrates a negative and statistically insignificant influence at the 5% level. in examining the association between the lngp and lexr, it is evident that the present lngp values exhibit a negative and substantial magnitude. this stands in support of the outcome in the long term. in support of the long-term findings, the present value of lcop exhibits a negative but statistically significant outcome. the lag 1 coefficient for lcop is found to be positive and lacks statistical significance. nevertheless, the preceding value of lcop at a lag of 2 exhibits a negative correlation and is deemed statistically insignificant at a 5% level. the error correction term in the econometric model exhibits a value of -0.078286, satisfying the econometric criteria of being negative, statistically significant, and smaller than one. this finding suggests that there is a feedback or convergence rate of 7.8% towards long-run equilibrium. this imply that the short run equilibrium adjustment to long run is slow. moreover, the adequacy of the model’s fit is substantiated by the r-squared, durbin-watson statistic, and f-statistic as presented in table 7. the adjusted coefficient of determination, denoted as r-squared, elucidates that approximately 81.7% of the observed fluctuations in the exchange rate can be accounted for by the independent variables under consideration. all these metrics indicate that the model is well-fitted. table 4: the results of ardl (3, 5, 5, 3) model variable coefficient std. error t-statistic prob. lexr(-1) 0.921714 0.042240 21.82087 0.0000 llng -0.107442 0.053872 -1.994402 0.0563 llng(-1) 0.154634 0.088724 1.742860 0.0927 llng(-2) -0.041227 0.088831 -0.464110 0.6463 llng(-3) 0.121113 0.062833 1.927538 0.0645 lngp -0.077969 0.037433 -2.082929 0.0469 lngp(-1) 0.067651 0.037421 1.807862 0.0818 lcop -0.142644 0.048204 -2.959161 0.0063 lcop(-1) 0.060314 0.076238 0.791136 0.4358 lcop(-2) -0.045387 0.082226 -0.551974 0.5855 lcop(-3) -0.093725 0.074243 -1.262412 0.2176 lcop(-4) 0.101724 0.038039 2.674196 0.0126 c 0.516563 0.202305 2.553382 0.0166 r-squared 0.996723 mean dependent var 4.505003 adjusted r-squared 0.995267 s.d. dependent var 0.399963 s.e. of regression 0.027516 akaike info criterion -4.091122 sum squared resid 0.020443 schwarz criterion -3.542236 log likelihood 94.82243 hannan-quinn criter. -3.892662 f-statistic 684.4153 durbin-watson stat 1.705454 prob(f-statistic) 0.0000 source: author’s estimation (2023) pa ge 10 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 1-14, 2024 table 5: f-bound test f-bounds test null hypothesis: no levels relationship test statistic value signif. i(0) i(1) f-statistic 18.14008 asymptotic: n=1000 10% 2.37 3.2 k 3 5% 2.79 3.67 2.50% 3.15 4.08 1% 3.65 4.66 source: author’s estimation (2023) table 6: long-run estimate variable coefficient std. error t-statistic prob. llng 1.623251 0.421530 3.850851 0.0007 lngp -0.131799 0.244077 -0.539990 0.5936 lcop -1.529220 0.497292 -3.075092 0.0048 c 6.598382 1.175499 5.613261 0.0000 ec = lexr (1.6233*llng-0.1318*lngp-1.5292*lcop + 6.5984) source: author’s estimation (2023) table 7: short-run estimate variable coefficient std. error t-statistic prob. d(llng) -0.107442 0.044578 -2.410203 0.0230 d(llng(-1)) -0.079886 0.047419 -1.684688 0.1036 d(llng(-2)) -0.121113 0.050657 -2.390835 0.0240 d(lngp) -0.077969 0.033117 -2.354339 0.0261 d(lcop) -0.142644 0.043638 -3.268813 0.0029 d(lcop(-1)) 0.037387 0.041410 0.902856 0.3746 d(lcop(-2)) -0.007999 0.044511 -0.179711 0.8587 d(lcop(-3)) -0.101724 0.033186 -3.065261 0.0049 cointeq(-1)* -0.078286 0.007672 -10.20478 0.0000 r-squared 0.816793 mean dependent var 0.032122 adjusted r-squared 0.769514 s.d. dependent var 0.053489 s.e. of regression 0.025680 akaike info criterion -4.291122 sum squared resid 0.020443 schwarz criterion -3.911124 log likelihood 94.82243 hannan-quinn criter. -4.153726 durbin-watson stat 1.705454 source: author’s estimation (2023) diagnostics tests in order to enhance the dependability of the estimates, model validation is conducted by performing tests related to coefficient stability, model accuracy, and hypotheses regarding residuals, including normality, serial correlation, and homoscedasticity. the visual representations of cusum and cusum of squares serve to illustrate the constancy of coefficients, as indicated by the continuous residuals of cusum and cusum squared remaining confined within the 95% confidence interval (as depicted in figures 2 and 3). the results obtained from the ramsey reset test (table 8) suggest that the null hypothesis remains unchallenged, thereby implying that the model has been suitably formulated. moreover, it has been ascertained that all conjectures pertaining to residual normality, serial correlation, and homoscedasticity have been corroborated, thereby signifying the validation of the model. the results of the residual diagnostics are shown in figure 1 and table 8. toda and yamamoto causality test according to musa et al. (2019), the presence of cointegration implies the presence of a causal relationship in at least one direction. the findings of the todayamamoto causality test are displayed in table 9. in cases where all-time series exhibit the same integration orders pa ge 11 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 1-14, 2024 of stationary, the granger test is employed for causality analysis. however, if the time series displays varying integration orders of stationary, the toda-yamamoto approach (toda & yamamoto, 1995) is utilised for conducting the causality analysis. the utilisation of the toda-yamamoto granger causality test in this study was motivated by the existence of variables exhibiting diverse orders of integration. the empirical analysis conducted unveils a conspicuous absence of substantiated evidence pertaining to a causal nexus between lng exports and the exchange rate within the nigerian context. in a similar vein, it is worth noting that there exists no discernible evidence of a causal nexus between the exchange rate in nigeria and the exports of lng. a two-way (bidirectional) causality between the exchange rate of the nigerian currency, and the prevailing market price of brent crude oil. the toda-yamamoto causality analysis failed to produce any empirical substantiation for the presence of a causal connection among the remaining variables under scrutiny. hypothesis test hypothesis testing serves as a scientific methodology utilised to discern between two assertions, specifically the null hypothesis (h0) and the alternative hypothesis (h1). in the realm of hypothesis testing, should the computed p-value be equal to or less than the predetermined level of significance, commonly represented as 0.05 or 5%, it is customary to reject the null hypothesis. on the contrary, in the event that the computed p-value surpasses the designated threshold of significance, the null hypothesis is deemed acceptable. hypothesis 1; h01: lng exports does not impact the exchange rate in nigeria. on the basis of the data that are shown in table 6, it is clear that the p-value associated with lng exports is lower than the critical threshold of 0.05. as a result, we conclude that the null hypothesis should not be accepted, and we come to the conclusion that the exporting of lng has an impact on the exchange rate in nigeria. hypothesis 2; h02: there is no causal relationship between lng exports and exchange rate in nigerian. according to the empirical findings outlined in table 9, it can be observed that the p-values pertaining to the causal relationship between lng exports and the exchange rate in nigeria exceed the threshold of 0.05. based on our analysis, it is concluded that the null hypothesis cannot be rejected. this suggests that there is insufficient evidence to support the idea that there is a causal relationship between lng exports and the exchange rate in nigeria. in the same way, the p-value between exchange rate and lng exports is greater than 0.05 threshold. therefore, and it is concluded that naira/dollar exchange rate does cause lng exports. figure 1: histogram normality test source: author’s estimation (2023) table 8: serial correlation, heteroskedasticity, and ramsey reset tests breusch-godfrey serial correlation lm test f-statistic 1.124215 prob. f(2,25) 0.3408 obs*r-squared 3.300637 prob. chi-square(2) 0.1920 breusch-pagan-godfrey heteroskedasticity test f-statistic 0.420225 prob. f(12,27) 0.9418 obs*r-squared 6.294973 prob. chi-square (27) 0.9005 scaled explained ss 2.242144 prob. chi-square (27) 0.9989 ramsey reset test value df probability t-statistic 0.010027 26 0.9921 f-statistic 0.000101 (1, 26) 0.9921 source: author’s estimation (2023) pa ge 12 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 1-14, 2024 figure 2: cumulative sum (cusum) of recursive residuals source: author’s estimation (2023) figure 3: cumulative sum (cusum) of squares of recursive residuals source: author’s estimation (2023) table 9: toda and yamamoto causality test result null hypothesis: chi-square p-value granger causality llng does not granger cause lexr 1.784892 0.6182 no lexr does not granger cause llng 5.397990 0.1449 no lngp does not granger cause lexr 2.979326 0.3948 no lexr does not granger cause lngp 6.152830 0.1044 no lcop does not granger cause lexr 8.772087 0.0325 yes lexr does not granger cause lcop 11.36121 0.0099 yes source: author’s estimation (2023) conclusion in this study, we focused on analysing the impact of the lng exports on the naira/dollar exchange in nigeria from the year 2000 to 2021 using a biannual data set. the study’s results unequivocally demonstrated that the time series variables of lng exports, exchange rate, and other intervening variables exhibited integration of order zero and one. a relationship was established between the dependent and independent variables, characterised by long-run equilibrium, short-run dynamics, and causality. the primary aim of this research endeavour was to assess the influence exerted by lng exports on the exchange rate within the context of nigeria. the empirical results indicate that the exportation of lng had a favourable and statistically significant influence on the exchange rate. an increase in lng exports causes the naira to depreciate. this assertion was substantiated by the outcomes observed in the short term. the second objective of this pa ge 13 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 1-14, 2024 study aimed to establish a causal relationship between lng exports and the exchange rate in nigeria. the re is no causality from exchange rate to lng exports. same applies from llng exports to exchange rate. given the observed depreciation of the naira caused by lng exports, it is recommended that to build a more balanced export portfolio, the government should diversify export sectors and support the growth of other non-oil and gas industries, such as manufacturing, services, and agriculture. moreover, the impact of lng exports on the exchange rate highlights the significance of maintaining adequate foreign exchange reserves. sufficient reserves can be utilised to stabilise the naira amidst periods of volatility arising from shocks in lng exports or other external factors. policymakers should maintain effective monitoring and management of reserves to ensure stability in exchange rates. references aboelsoud, m. e. 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(2020). exchange rate volatility and nigeria crude oil export market. scientific african, 9, e00538. pa ge 1 pa ge 67 american journal of applied statistics and economics (ajase) contemporary resource-based energy financial trade wang qian1, qian cheng1, jun li2* volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2492 https://journals.e-palli.com/home/index.php/ajase article information abstract received: february 17, 2024 accepted: march 22, 2024 published: march 25, 2024 currently, faced with the rapid increase in energy demand and insufficient energy supply, as well as the two major contradictions between energy consumption in production and living and comprehensive sustainable development, countries and regions have chosen to cooperate to jointly cope with development problems, and use the “belt and road” energy cooperation and global climate energy cooperation represented by negotiations is not uncommon. the transformation of the resource-based economy has entered a critical period, and it is necessary to find strong theoretical support and feasible practical measures. therefore, combining the proposition of energy cooperation with the transformation needs of the resource-based economy and studying the role of energy cooperation in the transformation of the resource-based economy from the perspective of factor combinations can not only lay a micro-foundation for issues related to energy cooperation at the mesolevel in theory , opening up a new perspective for academic research related to resourcebased economy; it can also guide the practice of resource-based economic transformation and explore breakthrough paths for it. keywords energy, finance and trade, transformation and development 1 belarusian state university, minsk, belarus 2 belarusian national technical university, minsk, 220013, pr, belarus * corresponding author’s e-mail: jli701788@gmail.com introduction since the impact of the first oil crisis in the 1970s, when then-u.s. president richard nixon first raised the issue of energy security, the international energy market has undergone profound changes. the most prominent feature is that the financial attributes of the international energy market have significantly increased. . in the context of the new era, energy and finance, two core issues in today’s world economic development, are gradually moving from cooperation to mutual integration. in the context of today’s energy transformation, how to combine energy finance with trade and achieve better transformation and development is an important research topic in various countries today. the era is of great significance . feng baoguo analyzed the related issues of energy finance: he proposed the concept that the development of energy finance is closely related to national development, energy security, and economic security; and emphasized that the energy finance center can gather financial markets, financial institutions and financial capital plays the functions of energy investment and financing, energy pricing, energy financial product development, and energy financial risk management to promote the positive interaction and coordinated development of the energy industry and the financial industry, thereby further serving the development of the real economy. ( feng baoguo, 2023 ) literature review a review of domestic and foreign research on resource-based energy financial trade cooperation chinese scholars’ research on resource-based energy financial trade academic research on resource-based economies began with the concept of “mining towns” proposed by auronssean in 1921. on this basis, (locus, 1971) proposed that the development of resource-based cities will go through a construction period, a development period, a transformation period, and a maturity period. “four stage theory”. (bradbury, 1983) on this basis, the decline stage and closure stage were added to further develop and improve the research on the development process of resource-based cities. (auty , 1993) proposed the phenomenon of “resource curse” in which areas rich in natural resources are subject to resource constraints, hindering economic development. it also started a climax of research on the transformation of resourcebased economy. since the 1990s, domestic scholars have gradually introduced many foreign theories on resourcebased economic issues, and further discussions have been made based on the actual domestic situation. (zhang fuming, 2002) comprehensively considered economic, resource, environmental and other factors to construct a transformation index, and divided the resource-based economic transformation into four stages: the resourcebased economic stage, the initial stage of transformation, the transition stage, and the critical transformation stage. the economic development of typical resource-based areas is highly consistent with its transformation process. (wu minmin, 2011) divides it into the early stage of economic structural adjustment, the period of rapid development of energy and heavy chemical industry base construction, the period of equal emphasis on infrastructure and energy base construction, and the period of economic structure strategy. four developmental periods, including the sexual adjustment period. (lu shuo, 2020) uses city light image data to divide the development of resourcebased cities into five stages: rise, growth, maturity, decline, and regeneration. (li ping, 2007) divides resource-based economic transformation into three models: marketpa ge 68 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 67-72, 2024 led, government-led and laissez-faire. (sun xiaohua & zheng hui, 2019) summarized foreign resource-based economic transformation models and summarized them as the american model (industrial diversification + orbital transition), the norwegian model (industrial regulation + green economy), and the indonesian model (supply and demand adjustment + export upgrading + diversified investment), etc. (yang huaijia & zhang bo, 2019) through research based on the krugman c-p model, they concluded that the model of open market promotion of transformation can effectively promote the transformation efficiency of various factors in resourcebased regions. foreign scholars’ research on resource-based energy financial trade (hoek , 2000) took the resource-based economic transformation of the netherlands as the research object and concluded that moderate wages, reduction of public expenditures, reduction of tax burdens and lower welfare levels are important institutional measures to get rid of its development dilemma. (dianlov et al ., 2004) found that differences in institutional effectiveness will lead to significant differences in transformation effects. therefore, the government should choose policies carefully. (cappelen & miset , 2009), (frankel , 2010) and (dyrstad , 2016) all systematically analyzed the tax and social welfare system implemented by norway in the transformation of resource-based economy and its impact on the harmonious development of economy, society and natural resources. the role played has been studied. regarding the practice of economic transformation in resource-based regions, in terms of innovation-driven transformation paths, (papyrakis & gerlagh , 2004) concluded that the lack of innovation is an important reason for the slow development of resource-based economies, and even for them to get into trouble. (sevren tok , 2020) conducted a study on micro-level innovation practices in the transformation of qatar’s resource-based economy and found that the single resource structure, unique demographic structure and lack of innovation experience have led to the country’s corporate innovation being more dependent on opportunities provided by the government. materials and methods based on determining the explanatory variables, explained variables, intermediary variables and control variables of the quantitative test, in order to clearly verify the role of energy cooperation in the transformation of resourcebased economy, this article draws lessons from (baron & kenny , 1986) used the hierarchical regression method to establish a baseline regression model and a mediation effect test model for four types of energy cooperation on the transformation of a resource-based economy, in order to preliminarily verify the impact of different types of energy cooperation on the transformation of a resource-based economy. whether the role exists, that is, whether the basis for the existence of the intermediary effect is established; if it is established , it proves that energy transformation and financial capital operations are inseparable . in order to analyze the impact of energy trade cooperation, energy investment cooperation, energy technology cooperation and energy governance cooperation on the transformation effect of resource-based economy, this article established benchmark regression models respectively, as shown in models 1.1 1.4 . results and discussion correlation analysis can show the interrelationship between variables and determine whether there may be serious multicollinearity problems between variables. this article conducts correlation analysis on variables, and the results are shown in table 1 . it can be seen that, first, the explanatory variables ln transfer (energy transfer amount), ln vest (frequency of energy investment cooperation activities), ln tech (frequency of energy technology cooperation activities), ln gover (frequency of energy governance cooperation activities) and the explained variable eco tran (resource-based economic transformation effect) has a significant positive correlation at the 10% significance level (r=0.478, p<0.01; r=0.138, p<0.10: r= 0.135, p<0.10; r=0.156, p<0.10), which preliminarily shows that provinces with higher frequency and larger scale of various energy cooperation activities will have better results in resource-based economic transformation. second, at the 5% significance level, the intervening variables rev (energy resource industry income), e ck (factor product elasticity), tfp (total factor production) and sub pa ge 69 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 67-72, 2024 ela (energy factor substitution elasticity) are related to the explained the variable eco tran (resource-based economic transformation effect) has a significant positive correlation (r=0.512, p<0.01; r=0.015, p<0.05; r=0.178, p<0.05; r=0.029, p< 0.05), which preliminarily shows that the greater the value of each positive intermediary variable, the stronger the intermediary effect and the better the resource-based economic transformation effect; rs (energy industry structure), ratio (proportion of energy industry) and the explained variable eco tran (resource-based economic transformation there is a significant negative correlation (r=-0.248, p<0.01; r=-0.255, p<0.05), which preliminarily shows that the smaller the value of each negative intermediary variable, the stronger the intermediary effect, and the resourcebased economy the better the transformation effect. at the same time, the correlation coefficients between the variables in the model are all lower than 0.8, indicating that there is no multicollinearity problem between the models. this also provides a good foundation for studying the role of various types of energy cooperation in the transformation of a resource-based economy. table 1: variable correlation analysis results ecotran ln-transfer ln-invest ln-tech ln-gover lnrev rs eco-tran 1 ln-transfer 0.478*** 1 ln-invest 0.138* 0.196** 1 ln-tech 0.135* 0.191** 0.966*** 1 ln-gover 0.165* 0.137* 0.856** 0.895*** 1 ln -rev 0.512*** 0.851*** 0.332* 0.344*** 0.259*** 1 rs -0.248*** -0.276*** -0.002 -0.01 0.001 -0.029 1 ratio -0.255** 0.499*** 0.084 0.141* 0.085 0.668*** -0.009 e-ck 0.015** 0.015 0.254*** 0.270*** 0.339*** 0.111 0.261*** tfp 0.178** 0.036 -0.128 -0.178** -0.176** -0.098 -0.128 sub she 0.029** -0.153* 0.057 0.0 24 0.031 -0.166** 0.046 gei -0.293*** -0.337*** -0.455*** -0.4 30 *** -0.372*** -0.534*** -0.098 supervisor -0.276*** -0.486*** 0.086 0.101 0.045 -0.449*** -0.094 market 0.303*** 0.289*** -0.369*** -0.384*** -0.309*** 0.188** -0168** privatc 0.195** 0.237*** 0.557*** 0.563*** 0.548*** 0.354*** -0.103 technology 0.272*** 0.098 0.093 0.071 0.069 0.167** 0.108 continued table ratio e ck tfp gei prosecutor market privatec technology ratio 1 e ck -0.132 1 tfp -0.067 0.025 1 sub-that -0.268*** 0.065 -0.206** 1 gei 0.062 -0.242*** 0.071 -0.022 1 fiscal -0.198** -0.037 0.073 0.03 0.140* 1 market 0.206** -0.186** -0.008 -0.055 0.153* -0.591*** 1 privatc 0.014 0.296*** -0.066 0.005 -0.581** 0.045 -0.393*** 1 technology -0.102 0.071 0.003 0.045 -0.412** 0.123 -0.326** * 0.272* ** * p<0.10, ** p<0.05,* ** p<0.01 data source: (kang, 2021) http://www.cnki.net this article uses eco-tran (resource-based economic transformation effect) as the explained variable, ln -transfer (energy transfer amount), ln -invest (energy investment cooperation), ln -tech (energy technology cooperation), ln -gover (energy governance cooperation) is the explanatory variable and is substituted into models 1.1 1.4 for baseline regression. the results are shown in table 2. pa ge 70 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 67-72, 2024 it can be seen that in the regression results where lntransfer (energy transfer amount) is the explanatory variable, the coefficient of ln-transfer is positive and passes the 1% significance level test r=0.0218, p<0.01), it shows that the amount of energy transfer has a significant positive effect on the transformation effect of the resource-based economy, and an increase in the amount of energy transfer can promote the transformation and development of resource-based provinces (regions). according to the previous analysis, in energy trade cooperation, whether energy elements are used as commodities or secondary energy commodities or higher-quality energy commodities, trade cooperation can stimulate resource-based economic energy products and the extension of the industrial chain. this kind of the demand-side pulling effect will not only be reflected in the improvement of the output level of the resourcebased economy, but also affect regional income, which may in turn affect the society and people’s livelihood of the resource-based economy. the positive impact of energy trade cooperation on the resource-based economy will be more obvious in the early stage of the transformation of the resource-based economy. as the transformation and development of the resource-based economy continues to advance, more attention needs to be paid to increasing the proportion of high-tech products in energy trade cooperation. the above relationship is verified in the baseline regression. in the regression results where ln-invest (frequency of energy investment cooperation activities) is the explanatory variable, the coefficient of ln-invest is positive and passes the significance level test of 5% (r=0.0144, p<0.05), indicating that energy investment cooperation has a significant positive effect on the transformation of resource-based economies. resourcebased provinces (regions) can promote the transformation of resource-based economies by strengthening energy investment cooperation. energy investment cooperation not only promotes the mechanization and large-scale development of the energy resource-based economy, but also promotes the technological progress of the industry to a certain extent, improves the production efficiency of the resource-based economy, and is conducive to the transformation of the resource-based economy. in addition, previous studies have shown that there is a certain substitution effect between capital and energy factors in production, and one of the tasks of the transformation of an energy and resource-based economy is to reduce table 2: baseline regression results (1) (2) (3) (4) eco tran eco tran eco tran eco tran ln transfer 0.0218*** ( 3.94) gei -0.00271 -0.00249 -0.00245 -0.00572 ( -0.28) ( -0.25) ( -0.24) (-0.58) fiscal 0.286 0.0705 0.0527 0.0643 (1.52) (0.38) (0.28) (0.35) market 0.0377*** 0.0469*** 0.0472*** 0.0452*** (4.93) (5.88) (5.95) (5.75) private 0.118** 0.112** 0.104* 0.105* (2.40) (2.14) (1.97) (1.94) technology 2.909*** 3.540*** 3.626*** 3.451*** (4.16) (4.79) (4.89) (4.71) ln-invest 0.0144** (2.27) ln-tech 0.0174** (2.52) ln-gover 0.0151** (2.22) -cons -0.438*** 0.0896 0.0778 0.109* ( -2.75) (1.37) (1.18) ( 1.74) n r2 a _ 150 150 150 150 0.369 0.325 0.330 0.324 t statistics in parentheses, * p<0.10, ** p<0.05,** * p<0.01 data source: (kang, 2021) http://www.cnki.net pa ge 71 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 67-72, 2024 the proportion of energy and resource-based industries in the overall economy. therefore, the importance of energy investment cooperation development is also conducive to the realization of the goal of optimizing the industrial structure in the transformation of a resourcebased economy. the above relationship is verified in the baseline regression. in the regression results where ln-tech (frequency of energy technology cooperation activities) is the explanatory variable, the coefficient of ln-tech is positive and passes the significance level test of 5% (r=0.0174, p<0.05), indicating that energy technical cooperation has a significant positive effect on the transformation of resource-based economies. resource-based provinces (regions) can promote the transformation of resourcebased economies by strengthening energy technology cooperation. analysis shows that technological factors will improve the production efficiency of energy factors and other factors. , thereby promoting the transformation of the resource-based economy. at the same time, the introduction of technology helps to acquire the innovation capabilities of the resource-based economy. energy technology cooperation is the most important and critical type of energy cooperation. energy technology cooperation can only truly enter the stage when it develops. at the advanced stage, the development mode of resource-based economy is truly transformed. in the regression results in which in-gorer (frequency of energy governance cooperation activities) is the explanatory variable, the coefficient of ln-gorer is positive and passes the significance level test of 5% (r=0.0151, p<0.05), indicating that energy governance cooperation has a significant positive effect on the transformation of resource-based economies. resource-based provinces (regions) can promote the transformation of resourcebased economies by strengthening cooperation in energy governance. model analysis shows that resource-based economies can absorb high-quality external institutions through energy governance cooperation. it must be closely combined with the energy, capital, and technology that it already possesses to regulate and improve many aspects of the problem in the transformation of a resource-based economy. this in itself will have the effect of reducing the transaction costs of economic activities and improving economic efficiency. at the same time, , energy governance cooperation will have a profound impact on all aspects of the economy, society, and environment of resource-based regions by improving the formal and informal systems of the resource-based economy, which is conducive to their transformation and healthy development. among the control variables, at the 10% significance level, fscal (the ratio of fiscal revenue to gdp), markel (marketization index), prnale (the ratio of employees in private individual units), technology (technology market turnover to gdp proportion) has a significant positive effect on the effect of resource-based economic transformation, while gei (energy intensity) has an insignificant impact on the effect of resource-based economic transformation. according to the model analysis, the ratio of fiscal revenue to gdp, marketization index, the proportion of employees in private individual units and the proportion of technology market turnover in gdp have a positive effect on energy cooperation, while energy intensity has a negative effect. at the same time, the ratio of fiscal revenue to gdp, the marketization index, the proportion of employees in private individual units the ratio and the proportion of technology market turnover to gdp respectively affect the capital stock, institutional environment factors, economic vitality and development level of the resource-based economy, and have a positive effect on the transformation of the resource-based economy. however, energy intensity itself is a limiting factor for the resource-based green and clean economy. development factors have a negative effect on the transformation of a resource-based economy. conclusion a quantitative benchmark regression model was used to analyze the impact of energy trade cooperation, energy investment cooperation, energy technology cooperation, and energy governance cooperation on the transformation of a resource-based economy. the analysis showed that the above relationships were verified in the benchmark regression. therefore, the transformation of energy has an inseparable direct relationship with finance. with the development of futures markets and financial markets, energy 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(2007). resource-based economy: theoretical explanation, internal mechanism and application research. shanxi university 2007.https://kns.cnki.net pa ge 1 pa ge 24 american journal of applied statistics and economics (ajase) interrogating the asymmetric impact of exchange rate on agricultural output in nigeria francis ariayefa eniekezimene1*, ebimowei wodu2, mr joseph peres anda-owei3 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v2i1.2247 https://journals.e-palli.com/home/index.php/ajase article information abstract received: november 26, 2023 accepted: december 29, 2023 published: december 31, 2023 the agricultural sector is vital to the nigerian economy. it serves several functions including food supply for the populace, a source of income for a sizable section of the domestic labour force, a supplier of raw materials for industrial production, and provision of foreign exchange revenues to the economy. however, due to the country’s abundance of natural resources like crude oil, the agriculture sector is vulnerable to the fluctuations in the exchange rate caused by international financial markets. this analysis looked at how changes in the value of the naira relative to dollar affects agricultural production. in order to contribute to the current body of literature for the time period between 1981 and 2021, this study employed the dutch disease syndrome theoretical framework through the use of the non-linear autoregressive distributed lag (nardl) method for nigeria. this research shows that while the long-term effect of the exchange rate on agricultural output in nigeria is symmetrical, the short-term effect is asymmetrical. however, in more specific terms, the symmetrical effect of the exchange rate in the long run revealed that exchange rate appreciation increased real agricultural gdp by approximately 8.8 percent compared to exchange rate depreciation which had only increased real agricultural gdp by 0.11 percent. consequently, since exchange rate exerted positive impact on agricultural production in the long run, it is suggested that the nigerian government explores the increased competitiveness of the agricultural sector in its economic diversification efforts. in other words, the agricultural sector could provide an avenue to expand the revenue base of the government, but for a more beneficial effect on the agricultural sector in particular and the economy in general, more focus should be placed on policies that would enhance appreciation of the naira such as reducing imports of agricultural inputs and produce. keywords exchange rate, asymmetric impact, agricultural output, dutch disease syndrome, non-linear autoregressive distributed lag 1 department of economics, niger delta university, wilberforce island, bayelsa state, nigeria 2 bayelsa state ministry of finance, yenagoa, bayelsa state, nigeria 3 department of economics, nile university of nigeria, abuja * corresponding author’s e-mail: franciseniekezimene@ndu.edu.ng introduction agriculture encompasses not only the raising of livestock and aquatic organisms for human consumption and industrial production, but also the exploration and use of forests for these same ends. since agriculture accounts for a significant portion of nigeria’s gdp and provides millions of people with jobs, it is vital to the country’s economy. to be more specific, agriculture helps the industrial sector by providing surplus labour, food for domestic consumption, a market for industrial output, domestic savings for industrial investment, and foreign exchange earnings from agricultural exports. based on the latter argument, it’s reasonable to assume that changes in the value of the naira can have effect on the profitability and competitiveness of agricultural output in nigeria. conversely, the appreciation or depreciation of one nation’s currency relative to the economies of other countries has an effect on the agricultural production and balance of payment of the country providing the data. economic factors and sectors including export profits, the cost of imported inputs, food price inflation, investment and production choices, and government policies are all susceptible to the effects of swings in exchange rates. nigeria’s agricultural exports may become less competitive as a result of fluctuations in the exchange rate. when the naira weakens against major international currencies like the us dollar, nigerian agricultural exports become more competitive on worldwide markets (akinbode & ojo, 2018). export earnings for nigeria’s farmers may improve if foreign buyers can get more of the country’s food for the same amount of money in their currency. because of its reliance on foreign inputs like herbicides, equipment, and fertilisers, nigeria’s agricultural business can also be hit by fluctuations in the price of these items. input prices may be affected by a depreciation of the local currency due to the higher price of imported goods (onyeagocha el al., 2021). farmers’ profitability and competitiveness may suffer as a result of higher production costs. changes in the value of the naira might potentially have an effect on the cost of food in nigeria. when the local currency gains, the cost of imported goods rises (onwuka & oyewumi, 2019). consumers may be harmed, especially those with lower means who may have a harder time locating reasonably priced food that is also healthy. the uncertainty caused by changes in the value of a currency might affect agricultural output and investment decisions. it’s possible that farmers and agribusinesses won’t invest in long-term projects or boost output if the currency rate remains volatile (onyeagocha et al., 2021). reduced agricultural production and slow sector growth are possible outcomes. currency fluctuations may pa ge 25 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 24-32, 2024 have a significant impact on the agriculture industry, thus governments sometimes step in to stabilise the market or mitigate the fallout. the central bank of nigeria, for instance, may employ capital controls or currency interventions to dampen fluctuations in the value of the naira (onwuka & oyewumi, 2019). these bills are an attempt to guarantee the agriculture sector’s continued success and expansion over the long term by providing stability and aiding it. stylized facts about nigeria’s agricultural sector the government has used both monetary and fiscal measures to help agriculture regain its former glory and prevent its role in the economy from shrinking further. the nigerian agricultural bank (nab) was founded in 1972 as a direct result of government policies and projects. before becoming the bank of agriculture (boa) in 2000, it was known as the nigerian agricultural and cooperative bank (nacb) from 1978 to 1978. it wasn’t until 1976 that initiatives like the agricultural development programme, the river basin development authority, and operation feed the nation came into being. although the world bank agricultural development projects (adp) were established before 1976, they were originally conceived as an all-encompassing programme for rural development. the agricultural credit guarantee scheme (1977), rural banking programme (1977), green revolution (1979), directorate for food, roads, and rural infrastructure (1986), national fadama development project (1990), national special programme on food security (nspfs) (2000), community-based agricultural and rural development schemes (2003-2008), and root and tuber expansion programme (rtep) (2004) are all examples of government initiatives in the agricultural sector (eniekezimene, oluwabusayo & ekiye, 2020). although the government has taken steps to improve agriculture, the sector’s performance over the years has been dismal. agriculture’s contribution to gdp is lower than that of industry and services, and there is a clear divergence between agriculture and the manufacturing sectors. foreign reserves are depleted and the exchange rate is weakened because of the country’s reliance on imports of agricultural items such as rice, frozen chicken, iced fish, and plywood, to name a few. the government has banned over 40 categories of imports, most notably food products, and these items have since been removed off the list of recognised commodities that may be purchased with foreign exchange, but the situation has not improved. additionally, considering the need to relinquish complete reliance on crude oil and to emphasize on the non-oil sector in driving economic growth in nigeria, it has become imperative to re-examine the effect of exchange rate asymmetry on productive sectors like the agriculture in nigeria. as a result, the purpose of this research is to examine the impact of fluctuating exchange rates on agricultural output in nigeria between the years 1981 and 2021. the major objective of this research is to analyse the impact that the naira’s exchange rate during the study period had on agricultural output in nigeria. the specific objectives of this paper include to examine the impact of average manufacturing capacity utilization rate on agricultural output in nigeria and to study the impacts of inflation and interest rates on agricultural production in nigeria. the sample period used in this study was found to follow a normal distribution when degrees of freedom were taken into account. therefore, a 41-year period, beginning in 1981 and ending in 2021, was selected for analysis. from an empirical perspective, this time frame encompasses the vast majority of exchange rate fluctuations experienced by nigeria after it gained independence. the following are components of the research’s later stages: the second section reviews the relevant literature and theory, while the third discusses methodology in detail. in section four, we provide a detailed review of the data and the findings. section five concludes with some last thoughts and suggestions for follow-up study. theoretical framework and empirical literature review the dutch disease syndrome model created by corden in 1984 was employed in this study, following the example of adekunle, kehinde, & taiwo (2019). the model assumes three sectors: non-tradable (n), which is analogous to nigeria’s service industry; lagging (l), which is analogous to nigeria’s agriculture sector; and booming (b), which is analogous to nigeria’s oil and gas firm. furthermore, the model assumes that sectors b and l exist, as they are the ones responsible for producing commodities that are traded at standard world prices. third, a multiskilled workforce that can work across all three industries to keep costs down while yet contributing to each one’s production and unique characteristics. in addition, factor price is not static worldwide, which brings us to our fourth and last reason. based on these projections, the model creates two distinct consequences of a boom in b: the spending impact and the resource migration effect. as a result, the starting wages of the first generation of factor employees have increased. the price of non-traded products (n) will increase relative to the price of traded goods if some of the additional money created by b is spent, either by factor owners or by the government in the form of taxes. this is an example of sincere thanks. to achieve this result, one must reallocate resources from b and l into n and reverse the flow of demand from n to b and l. consequences of redistributing wealth: since the marginal product of labour in b has gone up because of the boom, workers in l and n are leaving in pursuit of better job security and a more consistent income in terms of traded products. this finding may be broken down into two parts: when employees from l are moved to b, production in l falls. this might be seen as direct de-industrialization because there is no demand for a real appreciation of the currency rate and no market for n. workers in n may easily migrate to b since the real exchange rate is stable. the transfer pa ge 26 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 24-32, 2024 of resources boosts real appreciation, and the resulting spending impact raises n’s excess demand. as a result, the deindustrialization caused by the cost impact intensifies as more people move from l to n in search of work. indirect deindustrialization describes this secondary effect. empirical literature review while this may be true, the relationship between the exchange rate and agricultural output has been the subject of several empirical studies, both globally and domestically. in order to stay abreast with the empirical results and advancements, this section evaluated a few of these investigations, beginning with those conducted abroad and concluding with those conducted in the united states. for instance, reuben and alala (2014) used time series data from 1970 to 2008 to analyse the effect of currency rate fluctuation on the prosperity of kenyan tea exports. the adf and johansen cointegration were used to guarantee series stationarity and the long run relationship of the variable, and then the ecm approach was used to examine the empirical model. results demonstrated that exchange rate volatility harmed tea exports. sirikul, chanchai, and somchai (2015) analysed the effect of monthly exchange rate variations on thai rice and rubber exports using primary and secondary data from 2002 to 2014. using a combination of survey data and in-depth interviews, they determined that fluctuations in the value of the currency exchange rate dampened demand for both types of agricultural exports. the study found that exporters and firms might benefit from hedging their risk of currency rate fluctuations. kafle and kennedy (2015) investigated how the real exchange rate affects agricultural exports from the united states to the nations of the organisation for economic cooperation and development (oecd). the consequences of free trade agreements and embracing the euro as a national currency were also studied. from 1970 to 2010, bilateral trade flow panel data were analysed using the gravity model. both agricultural and non-agricultural trade flows were demonstrated to be negatively affected by fluctuations in the real exchange rate. the exportdependent non-agricultural businesses are particularly vulnerable to variations in the exchange rate. in their 2018 study, wagan, chen, seelro, and shah looked into how changes in monetary policy affected expansion, inflation, and job creation. using a factor-augmented vector autoregressive model established by bernanke et al. (2005), researchers analysed agricultural data for pakistan and india from 1995 to 2016 and found that restrictive monetary policy dramatically increased rural unemployment while significantly reduced food inflation and agricultural productivity. mashinini, dlamini, and dlamini (2019) studied the effects of monetary policy on agricultural output in eswatini from 1980 to 2016. researchers employed a technique known as the vector error correction model (vecm). statistics show that currency rate, interest rate, inflation rate, money supply, and agricultural loans all have a detrimental effect on agricultural output. however, in the near run, agricultural output benefited from these causes. kipruto and nzai (2018) studied the results of government investment on agricultural output in kenya from 1980 to 2016. when measured with the autoregressive distributed lag (ardl) technique, government spending increases agricultural production. gatawa and mahmud (2017) looked at the longand shortterm effects of currency rate changes on the volume of nigeria’s agricultural exports between 1981 and 2014. the study used estimation strategies like generalised autoregressive conditional heteroskedasticity (garch) and autoregressive distributed lag (ardl) to determine the relationship between the exchange rate, agricultural loan amounts, and agricultural export relative prices and the volume of agricultural exports. long-term agricultural output was shown to be favourably influenced by the exchange rate and agricultural loans, whereas short-term agricultural output was negatively impacted by the relative pricing of export agricultural commodities. long-term results were similar, with the exception of agricultural exports, which were significantly impacted negatively by the official exchange rate. akinbode and ojo (2018) utilised garch and ardl to examine the relationship between the exchange rate and nigeria’s agricultural exports from 1980 to 2015. inflation, gdp growth, inflation, and global pricing were shown to have a significant positive influence on agricultural exports, whereas the shortand long-term effects of exchange rate fluctuations on agricultural production were found to be minimal. the dynamic impact of exchange rate fluctuations on agricultural output in nigeria was studied by adekunle, et al (2019) using the non-linear autoregressive distributed lag (nardl) approach between 1981 and 2018. although the bounds test indicated that there was no long-term relationship between the dependent and set of independent variables, their findings indicated that real exchange rate appreciation and depreciation, as well as the set of explanatory variables, had a significant impact on agricultural output in the short run. alegwu, aye, and asogwa (2018) examined the correlation between the fluctuation of the nigerian currency and agricultural exports from 1970 to 2013 using the vector error correction model (vecm). the data showed that fluctuations in currency exchange rates had a negative impact on agricultural exports over the long run but had no impact over the short term. ochalibe, and enete (2019) used the granger causality approach to analyse the impact of the currency rate and the interest rate on agricultural output growth in nigeria from 1980 to 2018. the findings indicated a unidirectional relationship between the interest rate and the exchange rate and agricultural progress. the impact of the exchange rate policy tool was positive by 2.85%, but interest rates considerably hampered agricultural development. ikpesu and okpe (2019) examined the relationship between capital flows, currency rates, and agricultural output in nigeria using the autoregressive distributed lag (ardl) pa ge 27 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 24-32, 2024 method between 1981 and 2016. a decline in currency value, the study revealed, has both short-term and longterm negative effects on agricultural output. abubakar (2019) looked explored the correlation between nigeria’s interest rates and agricultural output between 1999 and 2016. the numbers showed that when interest rates rose, agricultural output fell. awolaja and okedina (2020) examined how changes in exchange rates affected agricultural output in nigeria. in this study, the authors employed non-linear autoregressive distributed lags (nardl) technique. longterm agricultural output was shown to be greatly increased by a real exchange rate appreciation, whereas agricultural output was significantly decreased by a real exchange rate depreciation. another finding from the forecasts was that agricultural output is more vulnerable to increases in the real exchange rate than decreases in the same variable. this study, in line with the work of (adekunle et. al, 2019), used exchange rate appreciation and depreciation alongside interest rate, inflation rate, and average manufacturing capacity utilization rate to examine the asymmetric influence of exchange rate on agricultural production in nigeria. however, the current study focused on monetary policy variables (interest rate and inflation) and agricultural fdi to determine the impact on agricultural output, in contrast to the work of (adekunle et al, 2019) who used exchange rate appreciation and depreciation alongside fiscal and monetary policies as well as the industrial sector capacity utilization rate as explanatory variables. methodology and model specification this study employed the non-linear autoregressive distributed lag (nardl) method. the time-varying impact of changes in independent variables on the dependent variable is more accurately captured by the nonlinear autoregressive distributed lag (nardl) than by the more frequent autoregressive distributed lag (ardl). time series data for the variables were gathered from the central bank of nigeria (cbn) statistical bulletin from 1981 to 2021, and stationarity was tested using the adf unit root test, and long run correlations were determined using the bounds test for cointegration. therefore, the operational model for this study is: argdp =f(ner, inr, infr, mcu) (3.1) the econometric form of equation (3.1) which considers the dynamics of exchange rate is as follows: largdpt= β0+β+ 1ner+ t+β1ner1+β2inrt+β3infrt +β4 mcut+µt (3.2) the asymmetric impact of exchange rate on agricultural output using nardl and by following the approach of shin, yu and greenwood-nimmo (2014), yields; ∆argdp t=γ[argdp t+β+ 1ner+ (t-1)+β 1ner(t-1)+ β2inr(t-1)+β3infr(t-1)+β4mcu(t-1)+∑(q1-1) (j=0)δ + j∆ner+ (t-j) +∑ (q2-1 )(j=0)δ j∆ner (t-j)+∑ (q3-1) (j=0)λ jinr+∑ (q4-1) (j=0)π j ∆infr(t-j)+∑(q5-1) (j=0)ϕj ∆mcu(t-j)+µt (3.3) equation (3.3) can be re-parameterized to derive the unrestricted error correction version below; ∆argdpt = γ[argdp(t-1)-(-β1/γ ner+ (t-1) β1/γ ner(t-1)β2/γ inr(t-1)β3/γ infr(t-1)-β4/γ mcu(t-1))+∑ (p-1) (i=1) θi ∆argdp(t-i )+∑(q1-1) (j=0) δ + j ∆ner+ (t-j)+∑ (q2-1) (j=0)δ j ∆ner(t-j)+∑(q3-1) (j=0)λj inr+ ∑(q4-1) (j=0)πj ∆infr(t-j)+ ∑(q5-1) (j=0)ϕj ∆mcu(t-j)+μt (3.4) by letting, e(t-1)=argdp(t-1)-β + 1ner+ (t-1)-β 1ner(t-1)-β2inr(t-1) β3infr(t-1) β4mcu(t-1) (3.5) where, β+ 1= -α1/γ, β1 = -α1/γ, β2= -α2/γ, β3= -α3/γ, β4= -α4/ γ (3.6) equation (3.4) then, becomes, ∆largdpt= γe(t-1)+∑(p-1) (i=1)θi ∆argdp(t-i)+∑(q1-1) (j=0)δ + j ∆ner+ (t-j)+∑(q2-1) (j=0)δ j∆ner(t-j)+∑(q3-1) (j=0)λjinr(t-j)+∑(q4-1) (j=0)πj ∆infr(t-j)+∑(q5-1) (j=0)ϕj ∆mcu(t-j)+μt (3.7) where: δ = first difference operator; t = time period; largdp = logged share of agriculture in real gdp; e(t-1) = error correction term n𝐸𝑅 = nominal exchange rate; ner+ = positive changes in nominal exchange rate (representing depreciation); ner= negative changes in nominal exchange rate (representing appreciation); inr = interest rate (a proxy for the role of monetary policy in the agricultural sector development) infr = inflation rate (as a control variable) mcu = average manufacturing capacity utilization % (to account for intersectoral linkage) θi, δj+, δj-, 𝝀j, πj, ϕj, are short run parameters, while β1,………, β4 are long run parameters. p is the lag length for the dependent variable, while q1,…… ,q4 are the lag lengths associated with the explanatory variables, and μ = random error term. a priori expectations/expected results the preposition of economic theory regarding the relationship between each of the characteristics of the explanatory factors and the dependent variable serves as the foundation for the a priori expectations. the short run and long run relations of these parameter coefficients are respectively shown as: δ+ j > 0 or < 0, δj > 0 or < 0, 𝝀j < 0, πj > 0, ϕj > 0, β+ 1 > 0 or < 0, β1 > 0 or < 0, β2 < 0, β3 > 0, β4 > 0, theoretical speculation suggests that an increase or reduction in agricultural output may result from an appreciating currency. devaluation or appreciation of a currency’s exchange rate has similar effects on agricultural production. as the cost of borrowing money rises, it is anticipated that agricultural output would fall as a result of an increase in interest rates. higher prices for agricultural produce are anticipated to boost production and supply, therefore increasing the inflation rate. similarly, an increase in average manufacturing capacity utilization is expected to increase agricultural productivity because of the link between the agricultural sector and the manufacturing sector. pa ge 28 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 24-32, 2024 empirical analysis descriptive statistics the descriptive statistics for the five variables in this study are summarised in table 1, which spans the years 1981 through 2021 and represents 41 years of data. the average value of the exchange rate is close to 116.52%, whereas the average value of the log of agricultural real gdp is close to 8.8%. standard deviations of the series from their respective means show that the exchange rate was the most volatile (approximately 108.78%), and the log of real agricultural gdp was the least volatile (approximately 0.73%). table 1: summary of descriptive statistics variable no of obs. mean maximum minimum std. dev largdp 41 8.7989 9.8383 7.7422 0.7271 ner 41 116.5188 403.0000 0.6100 108.7793 inr 41 17.2517 29.8000 7.7500 4.6270 infr 41 18.9334 72.8400 5.3800 16.6654 mcu 41 11.7708 14.1637 8.2316 1.9702 source: author’s computation unit root test result the assumption behind the unit root test is, of course, that a series has a unit root. table 2 shows that the only variable that passed the augmented dickey fuller (adf) unit root test and remained stable at levels was the inflation rate (infr). when we differentiated the dependent variable, the other four variables (agricultural real gdp, average manufacturing capacity utilization, the nominal exchange rate, and the interest rate) remained unchanged. table 2: adf unit root test result variable adf statistics probability i(d) 5% critical val levels first difference levels first difference argdp -3.526609 -1.918614 -5.619461*** 0.6263 0.0002 i(1) ner -3.526609 -0.368921 -5.285607*** 0.9855 0.0006 i(1) inr -3.526609 -0.368921 -5.285607*** 0.9855 0.0006 i(1) infr -3.529758 -4.097623*** ψ……. 0.0133 ψ……. i(0) mcu -3.529758 -1.608017 -3.568379*** 0.7715 0.0082 i(1) please take notice that ***, **, and * indicate that the unit root hypothesis was rejected at the 1%, 5%, and 10% levels, respectively. the number of iterations via differentiation that must occur until a series becomes stationary is denoted by the order of integration, i(d). only intercept models are considered. source: author’s computation the ardl bounds test for cointegration results after learning about the time series, we validated the longterm connection. the null hypothesis of no cointegration between the variables underlies the bounds test for cointegration. using the autoregressive distributed lag (ardl) model and the bounds test, we checked for long-term correlations between the series. because it influences the outcome of the ardl processes, the lag time was selected with care. this research followed the recommendation of pesaran et al. (2001) and used aic to establish the time lag. therefore, the selected ardl model (2,3,4,4,3) was table 3: ardl bounds test for cointegration result f-statistic = 5.607080; no. of parameters k = 4 critical bounds values level of significance lower bounds i(0) upper bounds i(1) 10% 2.2 3.09 5% 2.56 3.49 2.5% 2.88 3.87 1% 3.29 4.37 source: author’s computation used to investigate the long-term relationship between each variable. non-linear findings from the bounds test for cointegration between agricultural production and its possible drivers (positive and negative changes in pa ge 29 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 24-32, 2024 exchange rate, fdi, interest rate, and inflation rate) are shown in table 3. the long-term relationship between agricultural production and its causes is supported by the fact that the f-statistic of 5.607080 is larger than the upper i(1) critical bound of 4.37 at the 1% level of significance. estimation and discussion of results long run estimate of the nardl model after confirming the existence of a long-term relationship between the variables, we proceeded to estimate the longrun coefficient estimates in equation (3.2). we evaluated long-run elasticities using the aic. table 4 displays the results of nardl’s long-term estimations of the model’s parameters. there was a dissection of the exchange rate’s asymmetry. table 4 shows that at the 5% level (0.0354), the positive coefficient of 0.001100 for the exchange rate denoting depreciation or devaluation of the naira over the u.s. dollar is statistically significant. this indicates that real agricultural gdp benefited greatly from the decline of the currency over time. this finding accords with what one would expect from a purely economic standpoint. however, the exchange rate was judged to be statistically significant at the 5% level (-0.087991), indicating that the naira had appreciated against the us dollar. this indicates that a higher exchange rate was the long-term driver of a higher ragdp. the precise figure is an 8.8 percent increase in real agricultural gdp for every unit decline in the exchange rate. at the 5% significance level, the interest rate coefficient was positive (0.391737), but the probability showed no significant relationship (0.6104). there was no statistically significant relationship between the inflation rate and its negative coefficient (-.092174) at the 5% level (0.5772). however, the result conforms to what was expected going in, and it shows that inflation decreased agricultural gdp by 9.2 percent per unit increase in inflation over the long run. manufacturing capacity utilization was positive (0.013067) and statistically significant at the 5 percent level of significance (0.0351) based on the coefficient and probability value respectively. this implies that a unit increase in manufacturing capacity utilization increased real agricultural gdp by approximately 1.3 percent in the long run. table 4: long run regression result dependent variable: largdp variable coefficient std. error t-statistics probability ner_pos 0.001100 2.67e-05 41.21086 0.0154 ner_neg -0.087991 0.003326 -26.45765 0.0241 inr 0.391737 0.753849 0.519649 0.6104 infr -0.092174 0.161960 -0.569120 0.5772 mcu 0.013067 0.000722 18.10486 0.0351 c 14.64121 17.53933 0.834765 0.4161 source: author’s computation the error correction regression model short-term and long-term projections for the ardl and ecm are displayed in table 5, respectively. the ecm is represented by the notation cointeq(-1). the ecm model examines the rate of adjustment in response to deviations from the long-run equilibrium and aims to capture the short-term dynamics of exchange rate and agricultural production. when about 43% of the long-run disequilibrium is rectified by lag-period error shocks, the coefficient of the error correction component is negative and statistically significant. adjusted r2 = 0.965652 indicates that about 97% of the variance in argdp was well captured by the model’s explanatory factors. the inclusion of a negative autoregressive coefficient, or lag coefficients, for the agricultural output share indicated that the computation of the agricultural element of real gdp was not adaptive. at the 5% level of significance, all of the coefficients are significant. real agricultural output has a major negative influence on itself in the short to medium term. exchange rate depreciation was negative in the reporting year and the first, second, and third lags (-0.000852, -0.001620, -0.003691, and -0.000101, respectively), with the exception of the third lag, which was significant at the 10% level. the results showed that a drop in the value of the naira compared to the us dollar or exchange rate depreciation had a significant negative impact on real agricultural gdp in the short run. during the reporting period, the first lag was 0.108603, the second lag was -0.046579, the third lag was -0.076781, and the fourth lag was -0.084302 with probabilities (p=0.0044, p=0.0082, p=0.0042, and p=0.0049, respectively), in contrast to the negative exchange rate. this result suggests that short-term negative exchange rate coefficients had a larger beneficial influence on real agricultural gdp in the reporting year than long-term positive exchange rate coefficients. table 5 displays the negative impact on agricultural real gdp in the first, second, and third lag years due to a shift in interest rates. according to these numbers, real gdp or output in agriculture dropped by nearly 4% for every unit rise in interest rates in the first and second lag years, and by about 2% in the third lag year. table 5 shows a similar trend, showing that higher inflation rates had a moderately negative effect on agricultural real gdp in the current year, but a substantially more severe effect in the first, second, and third lagged years. this pa ge 30 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 24-32, 2024 finding suggests that for every one-unit rise in inflation, agricultural real gdp or output declined by around 0.6% in the first and second lagged years and by about 0.2% in the third lagged year. manufacturing capacity utilization was found negative in the reporting year as well as the first and second lags (-0.008120, -0.010035, and -0.020543) respectively and were all statistically significant at the 5 percent levels of significance (0.0098, 0.0127 and 0.0057) respectively. this result connotes that manufacturing capacity utilization had significant negative impact on agricultural output in the short run. this also means that, there is negative intersectoral linkage between manufacturing and agricultural sectors in nigeria in the short run. this outcome could be attributed to other factors not covered in this study, such as poor electricity supply and other government policies bedevilling the manufacturing sector in nigeria. with respect to the post estimation results, since all of these post-estimation outcomes had probabilities over the 0.05 criterion, we may conclude that the model did not display non-serial correlation in the residuals, nonnormality of the residuals, or non-constant residual variance. this means that the asymmetric effect model is sufficient for making policy suggestions. in figure 4.1 the jarque-bera result of 0.306160 with a probability value of 0.858061 > 0.05 requires the retention of the null hypothesis of normality of the residuals. thus, the parameter estimates of nardl model used in this study are suitable for forecasting. table 5: short run error correction regression result dependent variable: lragdp variable coefficient std error t-statistics probability d(largdp(-1)) -0.358112 0.149040 -2.402788 0.0288 d(ner_pos) -0.000852 1.57e-05 -54.37188 0.0117 d(ner_pos(-1)) -0.001620 2.62e-05 -61.85776 0.0103 d(ner_pos(-2)) -0.003691 2.89e-05 -127.5422 0.0050 d(ner_pos(-3)) -0.000101 1.69e-05 -5.951011 0.1060 d(ner_neg) 0.108603 0.000746 145.6576 0.0044 d(ner_neg(-1)) -0.046579 0.000601 -77.50932 0.0082 d(ner_neg(-2)) -0.076781 0.000508 -151.1548 0.0042 d(ner_neg(-3)) -0.084302 0.000651 -129.4751 0.0049 d(inr) -0.000455 0.004876 -0.093402 0.9267 d(inr(-1)) -0.038183 0.007329 -5.210208 0.0001 d(inr(-2)) -0.039572 0.007248 -5.460035 0.0001 d(inr(-3)) -0.024967 0.005602 -4.457091 0.0004 d(infr) -0.000238 0.000861 -0.276746 0.7855 d(infr(-1)) -0.005562 0.001482 -3.753005 0.0017 d(infr(-2)) -0.006240 0.001557 -4.006883 0.0010 d(infr(-3)) -0.001935 0.000948 -2.040804 0.0581 d(mcu) -0.008120 0.000124 -65.23956 0.0098 d(mcu(-1)) -0.010035 0.000199 -50.30550 0.0127 d(mcu(-2)) -0.020543 0.000185 -111.2717 0.0057 cointeq(-1)* -0.428397 0.015525 -16.4216 0.0041 adjusted r2 f-statistic breusch-godfrey serial correlation lm test breusch-pagan-godfrey heteroskedasticity test wald test for asymmetry 0.965652 191.9985[0.000000] 0.2624 [0.4000] 0.1142 [0.0824] 0.9739 [0.3409] source: author’s computation conclusion this study was set out to evaluate the asymmetric impact of exchange rate on agricultural output in nigeria from 1981 to 2021. the dutch disease syndrome (dds) was used as the theoretical framework of the study while the non-linear autoregressive distributed lag (nardl) was adopted as the analytical technique for the agricultural output model. the empirical results of the model were pa ge 31 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 24-32, 2024 figure 1: jarque-bera test for residual normality separated into two. the long run results and the short run results. the long run results presented in table 4 revealed that exchange rate depreciation denoted by positive exchange rate (ner_pos) was positively signed and statistically significant implying that depreciation of the naira over the us dollar increased real agricultural gdp by 0.11 percent over the period of the study. on the other hand, exchange appreciation denoted by (ner_neg) was negatively signed and also statistically significant showing that appreciation of the naira over the us dollar also impacted positively on real agricultural gdp. specifically, exchange rate appreciation increased real agricultural gdp by approximately 8.8 percent over the study period. manufacturing capacity utilization was positive and statistically significant, implying that a unit increase in manufacturing capacity utilization increased real agricultural gdp by approximately 1.3 percent in the long run. interest rate was found positive but statistically insignificant contrary to a priori expectation, while inflation rate was negative and statistically insignificant but in line with a priori expectation. the short run results presented in table 5 on the other hand revealed that exchange rate depreciation dented by (ner_pos) had significant negative impact on real agricultural gdp for reporting year and all the three lag years, while exchange rate appreciation denoted by (ner_neg) exhibited significant positive impact on real agricultural gdp for the three lag years. interest rate and inflation rate were both negatively signed and statistically significant conforming with a priori expectation. however, manufacturing capacity utilization was contrary to a priori expectation as it was also negatively signed and statistically significant. based on the empirically findings of this study, we conclude that while the long-term effect of the exchange rate on agricultural output in nigeria is symmetrical, the short-term effect is asymmetrical. however, the symmetrical effect of the exchange rate in the long run revealed that exchange rate appreciation had more positive and significant effect on real agricultural gdp with 8.8 percent compared to exchange rate depreciation which had only 0.11 percent significant positive effect on real agricultural gdp. recommendations following the findings of this study, the following recommendations for policy options could be pertinent: (i) it is suggested that, as part of its efforts to diversify the economy, the nigerian government investigates the sector’s increased competitiveness. exchange rates have a positive long-term influence on agricultural productivity. in other words, the agriculture business may provide a method for the government to diversify its revenue sources. however, focus should be placed more on policies that would help the naira appreciate such as reducing imports of agricultural inputs and produce. (ii) local raw material procurement should be prioritised in order to absorb the beneficial spill over inherent in forward and backward inter-sectoral connections; doing so will cut imported inflation, total inflation in nigeria, and the strain on foreign exchange. (iii) genuine farmers should be eligible for low-interest loans and inputs from the nigerian government at all levels. inputs and financial facilities should be available at the appropriate times and in the appropriate quantities through agricultural cooperatives. this is because, in the past, most of the nigerian government’s agricultural operations had failed owing to insufficient money and input supply. furthermore, the participation of ghost farmers in such programmes would be confirmed and prevented by utilising legal, pre-existing farmers’ cooperatives. this will raise agricultural output, reduce food inflation, and increase agriculture’s contribution to national gdp. (iv) interest rates should not be permitted to fall below a specific level in order to allow for the influx of both local and international direct savings and investment in the agricultural sector. this would assist to maximise the sector’s potential via competition, which will encourage agricultural enterprises to list on the nigerian stock market (ngx) and strengthen the relationship between agriculture and manufacturing in nigeria. pa ge 32 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 24-32, 2024 references adekunle, w., kehinde, a. t., & taiwo, h. o. (2019). the impact of exchange rate dynamics on agricultural output performance in nigeria. jos journal of economics, 8(3), 104 – 128. akinbode, s. o., & ojo, o. t. 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(2019). effect of monetary policy in eswatini. international journal of economics and financial research, 5(5), 94 – 99. doi.org/10.32861/ijefr.55.94.99 ochalibe, a. i., okoye, c. u., & enete, a. a. (2019) analysis of exchange and interest rate policy instruments’ dynamics on agricultural growth in nigeria. international journal of environment, agriculture and biotechnology, 4(4), 1131 – 1140. https://dx.doi. org/10.22161/ijeab.4437 onwuka, o., & oyewumi, o. (2019). exchange rate fluctuations and agricultural export performance in nigeria. journal of agricultural and environmental sciences, 18(2), 1-14. onyeagocha, s. u., chukwu, k. o., & nweke, o. c. (2021). exchange rate fluctuations, credit accessibility and agricultural sector output in nigeria. journal of business and economic analysis, 4(1), 1-15. reuben, r., & alala, o. 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(2018). assessing the effect of monetary policy on agricultural growth and food prices. agric. econ. – czech, 64, 499– 507. https://doi.org/10.17221/295/2017-agricecon pa ge 1 pa ge 73 american journal of applied statistics and economics (ajase) methods and applications of investment decisions in the catering industry chaoheng yan1, sviridova tatyana grigoryevna2, xinxin liang3* volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2624 https://journals.e-palli.com/home/index.php/ajase article information abstract received: february 10, 2024 accepted: march 24, 2024 published: march 28, 2024 with economic globalization and consumption upgrading, investment activities in the catering industry have become more frequent and complex. this study aims to explore effective methods for investment decision-making in the catering industry and empirically analyze their application with a view to improving investment efficiency and accuracy. through the method of financial analysis difference calculation, we comprehensively analyze the investment payback period, risk assessment and expected returns. model analysis reveals the performance and applicability of different investment methods in actual operations. the results show that combining the unique industry characteristics of catering and adopting a customized comprehensive decision-making model can significantly improve the success rate of investment decisions. therefore, good risk assessment and decision-making methods can bring clearer investment directions and higher economic benefits to investors in the catering industry. keywords catering industry, investment decision, risk assessment, investment method 1 master of economics, belarusian state university, minsk, belarus 2 department of international political economy, belarusian state university, minsk, belarus 3 belarusian state technical university, belarus * corresponding author’s e-mail: xinxin1205123@gmail.com introduction with the deepening development of economic globalization and the changes in the consumption concepts of many consumers, the catering industry, as an important part of the service industry, is becoming the focus of the investment market. however, investment decisions in the catering industry are not only closely related to the economic environment, but also affected by multiple factors such as culture, region, and consumption habits, making the decision-making process complex and changeable. under such circumstances, how formulating effective investment strategies and achieving effective allocation of capital and reasonable risk control is a very challenging task for investors in the catering industry. currently, although some scholars have studied the theory and practical operation of catering investment, there is still a lack of targeted and empirical research in view of the characteristics of the catering industry, such as cyclicality, diverse models, and risk tolerance, especially in investment decisions. there are obvious research gaps in the development and application of models. in response to the above situation, this study aims to explore effective methods for investment decisionmaking in the catering industry, and verify the application and effect of these methods in actual operations through specific cases. through comprehensive financial analysis and market research, this article systematically analyzes the investment environment and risks, and evaluates the feasibility of the investment strategy and its expected return performance. the case analysis method is applied to different investment scenarios for verification, in order to optimize the investment decision-making methods in the catering industry. literature review theoretical basis of investment decision-making overview of investment decision model in the investment process of the catering industry, the investment decision-making model is an indispensable tool. these models are designed to help investors make rational decisions by analyzing different economic indicators, market trends, consumer behavior, and potential risks(1st banaeian n, et al., 2016). investment decision-making models usually include multiple aspects, such as cash flow analysis, rate of return assessment, risk assessment and market analysis. the catering industry has its own unique characteristics, including seasonal fluctuations, food safety issues, diverse consumer tastes, and high competition. these characteristics need to be fully considered in investment decision-making models. for example, consumer demand for healthy food and fast food changes with time and social and cultural changes, which requires investors to accurately predict future consumption trends and adjust investment strategies accordingly. these investment strategies are inseparable from the establishment of decision-making models. for example, cash flow analysis is an important part of the investment decision-making model. investors need to evaluate the cash inflow and outflow of the project to ensure that the investment project can generate sufficient cash flow to meet operational needs and bring returns to investors. rate of return assessment focuses on the ratio between investment return and invested capital. in the restaurant industry, this metric has a direct impact on financial results. furthermore, risk assessment occupies a core position in the investment decision-making model of the catering industry . finally, market analysis involves pa ge 74 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 73-79, 2024 research on the macro and micro levels of the catering market. macro-level analysis may include research on the overall economic environment and industry trends, while micro-level analysis pays more attention to consumer behavior, competitor strategies, etc. in addition to the model components mentioned above, there are other methods commonly used for investment decisions, such as decision tree analysis, sensitivity analysis, and monte carlo simulation. these methods can help investors evaluate investment options from different perspectives and make more comprehensive and in-depth decisions. in practical applications, investment decision-making models need to be customized according to specific investment projects and market environments. because investment decision-making models play an important role in catering industry investments, they help investors analyze investment opportunities in a structured and quantitative way. it is important to note that while these models can provide powerful analytical tools, they cannot completely replace investor intuition and experience. a successful investment decision usually requires a combination of model analysis and investor insights. risk assessment and management methods risk assessment and management methods are an important part of the investment decision-making process, especially in the catering industry. due to its unique market dynamics and uncertainty in consumer preferences, risk management is particularly critical. this article aims to explore the risk assessment and management methods applied in investment decisions in the catering industry. risk assessment methods include two basic forms: qualitative and quantitative. qualitative methods focus on assessing the type of risk and its likely impact, while quantitative methods attempt to quantify the probability and potential economic impact of a risk. in investment decisions in the catering industry, qualitative methods include swot analysis (strengths, weaknesses, opportunities, threats) and pest analysis (political, economic, social, technological), which help identify opportunities and threats in the business environment(1st palepu k g, et al., 2020). quantitative methods include sensitivity analysis, scenario analysis and monte carlo simulation. sensitivity analysis is used to evaluate the impact of changes in specific input variables on the outcome of an investment decision, while scenario analysis evaluates the potential outcomes of an investment under different market scenarios. monte carlo simulation predicts the probability distribution of investment results by building a probability model and conducting a large number of random samples. in terms of risk management methods, strategies commonly used in the catering industry include risk avoidance, mitigation, transfer and acceptance. risk avoidance involves taking steps to avoid high-risk investments, such as not investing in restaurant businesses in politically unstable areas. risk mitigation reduces the impact of risks by diversifying investments and strengthening corporate internal controls. risk transfer typically involves the use of insurance or contractual provisions to transfer certain risks to a third party. risk acceptance is the decision to take certain unavoidable risks after risk assessment, usually because these risks are closely related to opportunities for high returns. therefore, the risk assessment factors and management methods in the catering industry are shown in table 1 below. table 1: the risk assessment factors and management methods in the catering industry. category influencing factors risk assessment factors economy food safety customer satisfaction brand reputation financial factors risk assessment management method component pattern big data applications artificial intelligence can only develop cost control qc analyze model factors data accuracy data collection area external data internal data data difference ratio return on investment and financial analysis when studying investment decisions in the catering industry, return on investment (roi) and financial analysis techniques are two core elements. return on investment is a key indicator of investment effectiveness, and financial analysis technology provides investors with a series of methods and tools for evaluating and predicting the financial performance of catering businesses. typically expressed as a percentage, roi is the ratio of investment income relative to the investment cost. in the catering industry, calculating roi may involve many forms of benefits and costs, including but not limited to initial building decoration costs, equipment purchase costs, raw material costs, labor costs, marketing expenses and any other operating expenses. in terms of income, it may include daily operating income, improvement of brand value and appreciation of long-term assets (1st hayes d k & 2nd hayes j d, 2021). therefore, accurately calculating the return on investment in the restaurant industry requires a comprehensive consideration of all relevant revenue and cost streams. in practice, investors and managers use a variety of financial analysis techniques to evaluate their restaurant businesses. these techniques include, but are not limited to, cash flow analysis, costbenefit analysis, sensitivity analysis, and financial ratio analysis. pa ge 75 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 73-79, 2024 it is worth noting that the catering industry is a dynamic and highly competitive field, and long-term brand building, customer loyalty, and market share stabilization are equally important. so when conducting financial analysis, investors should not only focus on short-term financial performance. sensitivity analysis can reveal the potential impact on revenue from price changes or supply chain disruptions. financial ratio analysis can help investors compare the financial health of different catering companies to determine the best investment options (1st usman, m., et al., 2018). in addition to internal financial analysis, external economic factors must also be taken into account. this includes monitoring of inflation rates, interest rate levels, policy changes and socioeconomic trends. these macroeconomic indicators may have an impact on consumer spending patterns, thereby impacting restaurant industry earnings. therefore, roi and financial analysis techniques are indispensable tools in investment decisions in the catering industry. through these tools, investors can gain in-depth insights about their investments, allowing them to make informed decisions based on data and market trends. materials and methods investment decision-making methods in the catering industry application of empirical analysis method in investment decision-making the empirical analysis method is a research method based on data analysis. it reveals the internal connections and regularities between variables through the collection, organization and analysis of historical data. this method has important application value in investment decisionmaking. the application of empirical analysis method in catering investment decisions can start from market demand analysis. as an important part of the service industry, the development of the catering industry is profoundly affected by consumer preferences, spending power and market trends. by collecting and analyzing consumer behavior data in a specific area, investors can understand changes in consumers’ basic needs and preferences for catering consumption, and then predict future market demand (1st polk, c. & 2nd sapienza, p., 2018). for example, by conducting regression analysis on consumption data over the years, the elasticity of consumer demand for a certain catering service can be revealed, thereby providing a reference for investment decisions. empirical analysis also plays an important role in cost control and profit forecasting. the costs of the catering industry mainly include food purchase costs, labor costs, store rental costs, etc. through historical trend analysis of these cost data, possible changes in costs in the future can be predicted, providing a basis for cost control and profit forecasting. for example, by analyzing the trend of food price changes, it is possible to predict future fluctuations in raw material procurement costs, and then adjust the dish pricing strategy to ensure the realization of profit targets. furthermore, empirical analysis methods also have significant applications in competitive analysis. competition in the catering industry is fierce, and understanding competitors’ business conditions and strategies is crucial to investment decisions. by analyzing competitors’ turnover, market share, customer satisfaction and other data, you can evaluate your own competitive position and potential risks in the market. for example, through comparative analysis, investors can discover competitors’ special services or marketing strategies, and investors can adjust their own business strategies accordingly to enhance competitiveness. in addition, empirical analysis methods are also indispensable in assessing the feasibility of investment projects. before investing in a catering project, through empirical analysis of the potential investment project’s historical business data, customer flow, brand influence, etc., the project’s profitability and growth potential can be comprehensively evaluated. for example, analyzing the past expansion history and market feedback of a certain catering brand can provide empirical support for the location selection and market positioning of new stores. in summary, empirical analysis plays a vital role in investment decisions in the catering industry. through systematic analysis of historical data, investors can not only more accurately assess market demand, control costs, predict profits, analyze competition and assess risks, but also develop more reasonable and scientific investment strategies based on empirical results. therefore, the empirical analysis method has become one of the indispensable tools in catering investment decision-making. investment decision-making strategy that combines qualitative and quantitative methods when discussing investment decision-making strategies in the catering industry, the method of combining qualitative and quantitative analysis is particularly critical. qualitative analysis refers to analysis based on non-numeric information, such as industry trends, brand influence, management team experience, market reputation, etc. quantitative analysis focuses on evaluating the potential value of investment projects through numerical calculations, such as financial ratio analysis, discounted cash flow ( dcf ) models, etc. in investment decisions in the catering industry, qualitative analysis can first help investors understand the fundamentals of the industry. as an industry closely related to consumer demand, the catering industry is affected by many factors such as culture, region, and consumer preferences (pieloch-babiarz, a., 2020). through an in-depth understanding of these non-numeric factors, investors can have an intuitive understanding of the development trends and changes in the catering market. for example, for a local catering brand, qualitative analysis can focus on the brand’s local pa ge 76 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 73-79, 2024 recognition, customer loyalty, brand culture inheritance and innovation, etc. for chain catering companies, it is necessary to evaluate the brand’s expansion strategy, adaptability to new markets and the efficiency of supply chain management. qualitative analysis alone is not enough. the investment decisions of catering companies also need to evaluate the economic benefits of the project through quantitative analysis. through in-depth analysis of a company’s financial statements, investors can calculate key financial indicators such as net profit margin, asset turnover, cash flow, etc. these indicators can help investors judge the profitability and financial health of the company. for example, net profit margin can reflect the profit a restaurant company retains from each unit of revenue, while asset turnover can illustrate how efficiently a company uses its assets to generate sales. cash flow analysis is particularly important for the catering industry because it is a cash flow-intensive industry and daily operations require a large amount of cash flow support. when combining qualitative and quantitative analysis, investors need to pay attention to the reliability of data sources and the applicability of analysis methods. because the investment decision-making strategy that combines qualitative and quantitative methods requires investors not only to have keen market insights, but also to have solid financial analysis capabilities. by combining the qualitative judgment of industry trends with the quantitative calculation of corporate value, the scientific nature and accuracy of investment decisions can be improved, and more comprehensive and in-depth decision-making support can be provided for investors in the catering industry. application of portfolio theory in the catering industry portfolio theory, proposed by harry markowitz in 1952 , aims to optimize the ratio of risk and return through the rational allocation of assets. in the catering industry, this theory is also applicable to the decision-making of diversified investments, especially in the face of an increasingly competitive market environment with changing customer tastes. this section will discuss the specific application and effect of portfolio theory in the catering industry. when making investment decisions, catering companies need to evaluate the expected returns and risks of various potential investment projects. according to portfolio theory, the correlation between different catering projects will affect the risk level of the overall investment. furthermore, catering companies also need to consider capital cost and capital structure when applying portfolio theory. the cost of capital is the price a business must pay to obtain capital, including the cost of debt and equity. an ideal investment portfolio should maximize returns while minimizing the cost of capital. determinants of capital structure include the ratio of debt to equity, which directly affects a company’s financial stability and risk tolerance. a catering company that relies too much on debt financing may face greater financial pressure during a market downturn, while a reasonable combination of debt and equity can provide greater flexibility. in addition to traditional portfolio management, the particularities of the restaurant industry also require companies to consider non-financial factors. this includes things like brand image, customer loyalty and employee satisfaction. a strong brand can bring pricing power and customer traffic to catering companies, and enhance their ability to resist risks. in addition, high customer loyalty and employee satisfaction can provide stable performance support for enterprises in the face of competition. therefore, in practice, catering companies can use historical data and market analysis to build investment portfolios. this requires companies to conduct quantitative analysis of the expected returns of each investment project, historical return fluctuations, and the correlation between different projects. by establishing mathematical models, companies can simulate the expected performance of different investment portfolios and choose the solution with the best risk-to-return ratio. portfolio theory is not a panacea. in the catering industry, there are problems in actual operations such as incomplete data, difficulty in predicting market trends, and difficulty in quantifying consumer behavior. these problems require catering companies to combine industry experience and market insights when applying portfolio theory and dynamically adjust investment strategies. therefore, portfolio theory provides a systematic investment decision-making framework for catering companies. by comprehensively assessing the risks and returns of different investment projects, companies can control risks while maximizing profits. in the highly dynamic and competitive field of the catering industry, the effective application of theory requires companies to continuously learn and adapt in order to achieve longterm stable development. results and discussion analysis of investment decision-making method models and practical cases model evaluation and discussion of investment decision-making methods in emerging catering models when discussing the application of investment decisionmaking methods in emerging catering models, we first need to clarify that the core purpose of investment decision-making is to identify, evaluate and select projects that can maximize expected returns. emerging catering models such as catering technology integration, unmanned restaurants, food delivery services, etc. are all new areas for investment decision-making in the contemporary catering industry. analysis of investment decisions in these emerging models requires the application of traditional financial evaluation methods, while also considering industry-specific risks and potentials (1st nieh, f. p. & 2nd pong, c. y., 2012). pa ge 77 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 73-79, 2024 the financial evaluation method is shown in figure 1, net present value (npv) 30% , internal rate of return (irr) 30%, payback period (pbp) 30% and other 10% , etc. we use the above evaluation methods to evaluate how an unmanned restaurant can reduce employee costs and increase future cash flow, and conduct discount calculations to determine whether the net present value of an investor’s investment in an unmanned restaurant is positive, and then make a decision on whether to invest. therefore, the weight of each indicator is calculated based on the coefficient of variation. the detailed steps are as follows : first, since different data have differences in measurement standards and magnitudes, the standardization method should be used to standardize the indicators. we need to use the extreme value processing method (equation (1) and equation (2) to standardize the original data. the evaluation values are shown in table 2. through the financial evaluation model assignment, all discount values r>0, so the investment decision-making method is feasible in the emerging catering model , but we should still consider that the emerging catering model is accompanied by a high degree of uncertainty and risk. we need to further add methods such as sensitivity analysis, scenario analysis and monte carlo simulation. sensitivity analysis can help investors understand the impact of changes in key variables (such as customer traffic, raw material prices, etc.) on project profitability. scenario figure 1: assignment evaluation proportion chart formula (1) formula (2) table 2: simulation calculation of financial evaluation method evaluation indicators quarterly output value/w assessment ratio/% assignment discount calculation/r formula 1 formula 2 yij (1) yij (2) net present value (npv) 1 13.2 30 0.92 0.86 3.64 3.41 3.5 3 2 12.5 30 0.79 0.93 2.96 3.49 3.2 3 3 17.8 30 0.62 0.83 3.31 4.43 3.87 4 22.3 30 0.93 0.76 6.22 5.08 5.65 internal return value (irr) 1 5.6 30 0.97 0.91 1.63 1.53 1.58 2 5.2 30 0.82 0.85 1.28 1.33 1.3 1 3 7.8 30 0.91 0.79 2.13 1.85 1.99 4 10.5 30 0.76 0.68 2.39 2.14 2.2 7 present value of revenue recovery (pbp) 1 2.4 30 0.82 0.84 0.59 0.61 0.6 2 2.1 30 0.85 0.96 0.61 0.69 0.65 3 3.5 30 0.72 0.78 0.52 0.56 0.54 4 5.6 30 0.96 0.75 1.61 1.26 1.4 4 other 1 0.9 10 0.72 0.83 0.065 0.075 0.07 2 0.7 10 0.91 0.93 0.064 0.065 0.065 3 1.3 10 0.87 078 0.113 0.102 0.108 4 2.7 10 0.79 0.69 0.213 0.186 0.2 analysis further considers multiple possible outcomes under different market and operating environments, providing investors with a more comprehensive risk assessment framework. monte carlo simulation, as a more advanced risk assessment method, simulates possible investment results by building a probability model. in addition to the financial assessment and risk analysis methods mentioned above, it is also very feasible to consider real-time data analysis methods and consumer behavior research . by analyzing social media, customer reviews, sales data, etc., investors can gain instant insights into market trends and customer preferences. these analyzes not only help assess the market potential of specific catering models, but also guide investors in more precise target market positioning. however, investment decisions in the catering industry should not ignore nonpa ge 78 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 73-79, 2024 financial factors such as laws and regulations, cultural differences and ethics. therefore, investors need to comprehensively consider financial assessment, risk analysis, real-time data interpretation, social ethics and other aspects of information to make reasonable and farsighted investment decisions. catering business case study when discussing investment decision-making methods and their applications in the catering industry, case studies are an important way to gain insight into actual operations and the effectiveness of decision-making. this chapter will analyze the case of a representative catering company to reveal the methods and strategies used in its investment decision-making process, as well as the results and existing problems, in order to provide useful reference and reference for the catering industry. this catering enterprise is a long-established medium-sized restaurant chain, its business is mainly concentrated in the mid-tohigh-end market, and it is famous for providing special dishes and high-quality services. in the past five years, the company has implemented many major investment decisions based on market trends and consumer needs. these decisions determine the company’s survival and development in the highly competitive catering market. the decision-making roadmap is shown in figure 2. figure 2: a company’s investment decision-making roadmap in terms of expanding chain stores, the company has adopted a cautious and effective market research strategy. before opening a new store, the company first conducts an in-depth analysis of factors such as the market capacity of the potential area, consumer taste preferences, competitor situations, and geographical location. based on these data analyses, the company selected areas with high foot traffic and less direct competition as locations for new stores (1st kusnadi, y. & 2nd wei, k. j., 2017). facts have proved that this data-driven location selection strategy has achieved remarkable success. the newly opened stores attracted a large number of customers in the early stages of opening, and the turnover gradually stabilized and showed a growth trend. when upgrading kitchen equipment, companies face the dual challenge of improving dish quality and efficiency. after a detailed cost-benefit analysis, the company decided to invest in the purchase of automated kitchen equipment, including smart cooking equipment and ingredient processing systems. the introduction of these equipment not only improves the efficiency of making dishes, but also maintains the consistency of the taste of the dishes, which is crucial for chain restaurants. at the same time, by reducing food waste and saving human resources, these investments paid for themselves in just over a year. the introduction of an intelligent management system is part of the company’s digital transformation. the system integrates multiple functions such as order processing, inventory management, customer relationship management, and employee scheduling. through realtime data analysis and reporting, management can make decisions faster and optimize operational processes. the use of this system greatly improves work efficiency, reduces human errors, and enhances customers’ dining experience. the decision to develop a new menu line is an important test of the company’s ability to continue to innovate. in order to attract young consumers and health-conscious customer groups, companies have invested in research and development and launched a series of dishes that are in line with healthy eating trends. the launch of these pa ge 79 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 73-79, 2024 new dishes not only enriches the menu, but also enhances the company’s market competitiveness, attracts new customer groups, and improves customer satisfaction. investment in brand marketing is reflected in multichannel publicity and brand image building. companies use social media, online advertising and ground promotion activities to effectively convey brand information to target consumer groups. by partnering with local communities, businesses increase their visibility and loyalty among potential customers, and these events not only enhance their brand image but also lead to significant sales growth. despite a series of achievements, companies have also encountered some challenges in the investment decision-making process. for example, during the expansion of new stores, some locations failed to fully consider transportation convenience and parking issues, which affected customer visitation rates. in terms of dish development, some newly introduced high-cost ingredients have not received the expected market response, resulting in a certain waste of resources. these issues point out that companies still need to improve the depth and breadth of market research, as well as strengthen cost control and risk assessment capabilities in investment decisions. through the study of this catering enterprise case, we can see that reasonable investment decisions are crucial to the long-term development of catering enterprises. successful investment is often based on accurate insights into the market, an in-depth understanding of consumer needs and the effective allocation of corporate resources. at the same time, continuous learning and adjustment, and reflection and summary of failed investments are also the keys to ensuring that enterprises remain competitive in a changing market environment. catering companies should use these cases as a mirror and continuously optimize their investment decision-making methods to achieve sustainable business growth and brand value enhancement. conclusion this study aims to explore effective methods for investment decision-making in the catering industry and the practical application of risk analysis to improve investment efficiency and decision-making accuracy. through a research path that combines theoretical and empirical analysis, this article deeply analyzes the investment environment, risk assessment and expected returns of the catering industry , explores a customized comprehensive decision-making model, and reveals the applicability of this model in actual operations through specific cases. sex and advantage. the research results confirm that systematic strategies adopted based on the unique characteristics of the catering industry can effectively guide investors to make more accurate investment decisions and achieve higher economic benefits. through the above discussion and evidence , it is expected to provide investors in the catering industry with richer and more complete theoretical support and inject new vitality into the sustainable development of the industry . references banaeian, n., mobli, h., fahimnia, b., nielsen, i. e., & omid, m. 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(2018). does stock mispricing affect the corporate investment decisions? evidence from catering effect. journal on innovation and sustainability risus, 9(1), 43-54. https:// doi.org/10.24212/2179-3565.2018v9i1p43-54 pa ge 1 pa ge 92 american journal of applied statistics and economics (ajase) the efficacy of vicoba intervention in alleviating poverty in emerging economies: a study of the orgut sedit vicoba lending scheme project in morogoro, tanzania jacob kilamlya1*, dickson utonga1 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2616 https://journals.e-palli.com/home/index.php/ajase article information abstract received: march 05, 2024 accepted: april 03, 2024 published: april 06, 2024 microfinance institutions have emerged as crucial agents in the fight against poverty, particularly in emerging economies. despite debates about their effectiveness, microfinance interventions have contributed positively to socio-economic conditions, improving livelihood access. recognising the importance of the microfinance subsector, tanzania has instituted tools to foster financial inclusion. the village community banks (vicoba) are an example of these efforts, aiming to empower marginalised communities through financial inclusion and access to credit. this study assesses the effectiveness of the vicoba intervention in alleviating poverty in morogoro district, tanzania. using a cross-sectional design and logistic regression analysis, the study examines the relationship between key variables (access to credit, ability to save, access to insurance services, entrepreneurship skills) and poverty alleviation. the results show the effectiveness of access to credit, savings, and access to insurance services in poverty alleviation. keywords vicoba, microfinance, poverty alleviation, effectiveness, financial inclusion, tanzania 1 department of project planning and management, tengeru institute of community development, arusha, tanzania * corresponding author’s e-mail: jacobkilamlya80@gmail.com introduction microfinance institutions are widely recognised for their role in poverty alleviation, particularly in emerging economies (geremewe, 2019; kasali, 2015). the institutions have positively impacted the socio-economic conditions of people experiencing poverty, leading to better access to health, education, and essential services (geremewe, 2019). however, the effectiveness of microfinance initiatives in reducing poverty has been questioned, with some studies suggesting that they may only sometimes lead to poverty alleviation (chikwira, 2022). still, these institutions have been found to reduce household poverty by improving incomes through financial services (ngong et al., 2021). furthermore, institutions are recognised as essential tools for poverty reduction and are increasingly identified as critical and strategic initiatives for economic empowerment (mamun et al., 2017; ahmad, 2022). the united republic of tanzania recognises the importance of microfinance in poverty alleviation and has put in place policies and frameworks that facilitate the growth and sustainability of the institutions. the national microfinance policy of 2012 outlines the government’s commitment to promoting financial inclusion and inclusion of people experiencing poverty. additionally, the cooperative societies act of 2003 established a regulatory framework for the operation of cooperative financial institutions. the village community banks (vicoba) is a prominent microfinance institution that aims to empower marginalised communities through financial inclusion and access to credit. in tanzania, microfinance institutions such as vicoba are crucial in providing financial services to people excluded from the conventional banking system, promoting economic development and contributing to poverty reduction efforts. vicoba’s effectiveness in poverty reduction and economic growth in tanzania can be further illustrated through various studies and reports. in addition, the world bank’s global findex database provides empirical evidence on the effectiveness of microfinance institutions on poverty reduction globally, with a specific focus on tanzania. information from the global findex database can provide insights into the reach and effectiveness of microfinance institutions such as vicoba in addressing financial exclusion and promoting economic empowerment among marginalised populations. the effectiveness of microfinance interventions in poverty alleviation has been highlighted in various contexts, including rural areas, bangladesh, and ghana (kasali et al., 2017; ali et al., 2016; batinge & jenkins, 2021). despite debates about the depth of outreach required to address the demands of poverty reduction adequately (ali et al., 2015), the microfinance subsector is widely recognised as capable of alleviating poverty even in impoverished areas (annim & alnaa, 2013), with particular emphasis on its role in ensuring poverty alleviation for women in africa (batinge & jenkins, 2021). even with the increasing number of microfinance interventions such as vicoba implementations, there is a rising need to examine their effectiveness in poverty alleviation. this study uses the orgut sedit vicoba lending scheme project to assess the efficacy of vicoba in alleviating poverty in morogoro district. orgut sedit implemented this vicoba lending scheme project to empower marginalised communities in the morogoro region through financial inclusion, savings promotion, and access to credit facilities. by assessing the effectiveness of vicoba on poverty pa ge 93 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 92-98, 2024 alleviation in morogoro district, this study makes an academic contribution by extending empirical evidence and previous studies to inform microfinance policy and practice. given the dynamic nature of the microfinance subsector and the poverty alleviation landscape in tanzania, it is paramount to conduct this investigation to promote poverty alleviation initiatives. material and methods study area this study was conducted in morogoro district, located in the eastern part of tanzania within the morogoro region. the district is a vibrant economic hub with a diverse geographical landscape and thriving commercial activities. its strategic location and well-developed transport infrastructure make it a vital link between major cities and towns, facilitating trade flows and economic activities, particularly with dar es salaam, tanzania’s largest city and port. the district’s commercial environment is characterised by diverse activities such as trading, transport, and logistics, supported by a vital local economy. agricultural activities, including cultivating staple crops such as maize, rice, sugar cane, various fruits, and livestock, contribute significantly to the district’s economic strength. its tropical climate, with distinct wet and dry seasons, provides favourable conditions for agriculture, further enhancing its economic potential. with its blend of natural resources, agricultural productivity, and commercial opportunities, the district is a crucial economic centre within tanzania, driving growth and development in the region. morogoro district was selected as the study area due to its reputation as a hub for microfinance institutions from among the districts where the orgut sedit vicoba lending scheme project was implemented. the area is renowned for its extensive microfinance infrastructure due to the expansion of commercial activities, which makes it a fast-growing town. the district’s diverse microfinance landscape provides a rich context for assessing the effectiveness of microfinance interventions in poverty alleviation through the vicoba intervention. study design the study employed a cross-sectional design, primarily for financial and time reasons. this design allows researchers to collect data from a representative population sample simultaneously. it is based on practical considerations. conducting a longitudinal study or using repeated measures designs would have required multiple visits to respondents over an extended period, which would have significantly increased the financial and time burden of the study. given the resource constraints, a crosssectional design was a feasible approach to data collection and assessment of the effectiveness of the vicoba on poverty reduction within a reasonable timeframe. in addition, the cross-sectional design allowed the study to capture a snapshot of the present situation regarding poverty and the effectiveness of the vicoba lending scheme in the district. sample size determination and sampling technique the sample size for this study was determined based on practical considerations and the need for adequate representation of vicoba members and key informants. sixty-two respondents were selected, comprising sixty vicoba group members and two key informants. purposive sampling was employed to select key informants, including a community development officer dealing with vicoba and sedit staff. this sampling technique allowed an intentional selection of individuals with relevant expertise and experience in microfinance and community development, ensuring precise data collection and perceptions of the effectiveness of the vicoba. in selecting vicoba members, a combination of multistage sampling, simple random sampling, and purposive sampling was employed as follows: morogoro district was selected from among the nine districts in the region. this initial stage of multistage sampling involved purposefully selecting the morogoro district due to its significance as the study area with a concentration of microfinance institutions. within the morogoro district, five wards were purposively selected: kihonda, mji mpya, bigwa, kingolwira, and mazimbu. these wards were chosen due to the prevalence of vicoba groups in comparison to others. additionally, they are situated in areas where vicoba members have spearheaded numerous initiatives. within each selected ward, twelve respondents were chosen randomly. random selection ensured that every vicoba member had an equal chance of being included in the sample. source of data and data collection techniques the study data was gathered from primary sources, i.e., vicoba members and officers from the morogoro district and sedit. the exercise involved surveys, interviews, questionnaires, and interview guides. they were utilised as follows: surveys were conducted in a structured manner to maintain consistency and ensure the quality of the collected data. questionnaires were distributed to vicoba members to gather quantitative data on various aspects of their group involvement. the semi-structured questionnaires collected information on demographic characteristics, financial behaviours, perceptions of the vicoba intervention, and its effectiveness in poverty alleviation. the distribution process involved obtaining consent from participants, explaining the purpose of the study, and providing instructions for completing the questionnaires. in addition to surveys, interviews were conducted with key stakeholders involved in the implementation of the project. this included a community development officer and a programme leader. the interviews were semistructured, utilising interview guides to ensure consistency and focus on key topics. through interviews, qualitative data were collected to understand the operational aspects of the vicoba intervention, challenges faced, successes achieved, and perceptions regarding its effectiveness pa ge 94 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 92-98, 2024 in poverty alleviation. interviews with community development officers and vicoba lending scheme leaders were arranged through prior appointments. interview guides escorted the conversation to ensure that relevant topics were covered. interviews were conducted face-to-face and by phone, depending on the availability and preferences of the interviewees. during interviews, probing questions elicited detailed responses and captured perspectives on the effectiveness of the vicoba intervention in poverty alleviation. the need to gather in-depth and firsthand information directly from the study’s stakeholders led to using survey questionnaires and interviews as the primary data collection methods. surveys allowed for the systematic collection of quantitative data, while interviews, on the other hand, provided a platform for stakeholders to share experiences and perceptions, enriching the understanding of the study. data and analytical strategy the data analysis involved descriptive and inferential techniques to assess the effectiveness of the vicoba intervention in alleviating poverty. before the analysis, data was processed and managed via the statistical package for the social sciences (spss) version 26; ibm imported it to stata version 13 for analysis. descriptive analysis techniques this analysis involved summarising and presenting key characteristics of the vicoba members. demographic variables such as age, gender, and education level were described using frequencies. the descriptive analysis findings were presented using percentages to provide a clear and concise overview of the data, facilitating the interpretation of socioeconomic characteristics among vicoba members. econometric analysis strategy the econometric analysis in this study involved the application of statistical and econometric techniques to assess the relationship between vicoba output and poverty reduction among members of the study area. specifically, a logit regression model was utilised to assess the impact of the vicoba on poverty reduction among beneficiaries. the logit regression model was selected for this study to assess the effectiveness of the vicoba intervention on poverty reduction among beneficiaries. the logit model is suitable for analysing binary outcomes and categorical dependent variables, making it an appropriate choice for assessing the likelihood of poverty reduction due to the vicoba intervention. the logit regression model is a statistical technique commonly used to estimate the probability of an event occurring (waqas & md-rus, 2018). it establishes a non-linear maximum likelihood function to determine the likelihood of a specific outcome, being a failure or success. logit regression provides a probabilistic model that can predict the probability of an event based on the input variables (ullah et al., 2023). in this case, it was employed to assess the effectiveness of the vicoba intervention on poverty alleviation in the study area. the model quantified the relationship between factors, such as the ability to save, access to credit, insurance services, and entrepreneurship skills, and their effectiveness in poverty alleviation. the model assumed a linear relationship between the log odds of the probability of poverty being reduced and the independent variables. it can mathematically be represented as: log (pi/1-pi)= logit(pi)= β0+ β1 x1+ β2 x2+ β3 x3 +β4 x4+ µi where: * log (pi/(1pi )) is the natural logarithm of the odds ratio (log-odds), also known as the logit function. * pi is the probability that poverty is alleviated due to the vicoba intervention. * 1pi is the probability that poverty is not alleviated as an impact of the vicoba intervention. * β0 is the intercept term representing the baseline log-odds of poverty alleviation when all independent variables are zero. * x1, x2, x3, and x4 imply ability to save, access to credit, access to insurance services and entrepreneurship skills respectively * β1, β2, β3, and β4 are the coefficients associated with each independent variable (x1, x2, x3, and x4) respectively, indicating the change in log-odds of poverty reduction for a one-unit change in the corresponding independent variable. * µi represents the error term, capturing the variability in poverty alleviation that is not explained by the independent variables. variables and measurements table 1 illustrates the variables under consideration and the corresponding measurements used to analyse the effectiveness of the vicoba intervention on poverty alleviation in morogoro district. table 1: variables and measurements variable description measurement expected sign references ability to save after becoming a vicoba member, an individual manages to set aside a portion of their income towards a savings purpose 1 represents setting aside income for savings, and 0 indicates otherwise positive ngong et al., 2021; ali et al., 2023 pa ge 95 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 92-98, 2024 results and discussion respondents’ socio-demographic characteristics sex of the respondents in the study, a significant proportion of respondents were female, constituting 66.7% (n = 40) of the sample. this higher representation of females in vicoba initiatives indicates a greater engagement in microfinance endeavours, which may alleviate poverty by creating income-generating and entrepreneurship opportunities. this aligns with beyene and dinbabo (2019), who identified that women’s participation in microfinance programmes significantly positively impacts poverty reduction. further, minja et al. (2023) noted that these programmes provide women access to financial and non-financial products and services, enabling them to improve their portfolios and living. women’s participation in microbusinesses, notably registered ones, has been shown to have a higher and more beneficial impact on their consumer spending, decision-making power, and provision of resources for their families (imai et al., 2012). age of the respondents the respondents’ age indicates a notable presence of individuals aged 18–35, aligning with the youth category outlined in the tanzania youth development policy 2006. this demographic pattern suggests the possibility of economic advancement and alleviation of poverty, given the association of the youth demographic with productivity and engagement in income-generating endeavours. hafeez and fasih (2018) commend that the potential can only be realised if the youth are effectively engaged in economic activities. this implies that economic development can be fostered and sustained by empowering youth and allowing them to shape their future. education level of the respondents the distribution of respondents based on their education level, with a substantial percentage having secondary education (43.3%) and higher education levels (28.3%), highlights the significance of education in influencing financial behaviours and outcomes within microfinance interventions. the findings suggest that individuals with higher education levels are more likely to possess the knowledge and capabilities necessary to engage effectively in income-generating activities. education levels play a crucial role in shaping individuals’ choices and participation in microfinance interventions, with higher levels of education enhancing entrepreneurial skills and income-generating opportunities. ngong et al. (2021) discuss the critical role of education in influencing entrepreneurial intentions, with universities and entrepreneurship education programmes seen as crucial for developing entrepreneurial skills, attitudes, and behaviours. khanam et al. (2018) argue that higher education levels are essential for developing entrepreneurial intentions and behaviours, highlighting the role of education in fostering entrepreneurial skills and attitudes. logistic regression model results the following section presents the logistic regression analysis results, illustrating the relationship between key variables and their effectiveness in poverty reduction in the context of the vicoba lending programme. the logistic regression model allows for a thorough analysis of the factors that impact the success of vicoba members in poverty reduction. the results are interpreted in light of existing literature on microfinance interventions and their role in promoting economic empowerment and poverty reduction. the findings are discussed with previous studies providing helpful evidence on the effectiveness of the vicoba lending scheme in addressing poverty challenges. the results are presented in table 2 as follows. access to credit after becoming a vicoba member, an individual gains access to various financial services that can be utilised to fulfil their financial needs 1 represents gaining access to various financial services, and 0 indicates otherwise positive khanam et al., 2018; hussain et al., 2018; ngong, 2023 insurance services a member has an insurance policy for mitigating business risks 1 represents a member having an insurance policy for mitigating business risks, and 0 indicates otherwise positive ngong et al., 2021; khanam et al., 2018 entrepreneurship skills a member equipped with the necessary entrepreneurship skills one represents a member equipped with entrepreneurship skills, and 0 indicates otherwise. positive banerjee & jackson, 2016;; bansal & singh, 2020 poverty alleviation when one becomes a member of vicoba, there is a possibility of experiencing a reduction in poverty status 1 represents a reduction in poverty status after becoming a vicoba member, and 0 indicates otherwise ali et al., 2015; amsami et al., 2021; chowdhury et al., 2021 source: study construction, 2024 pa ge 96 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 92-98, 2024 table 2: logistic regression results variable coefficient std. err z p>izi access to credit 5.280119 1.791468 2.95 0.003** ability to save 4.321305 1.386219 3.12 0.002** access to insurance services 2.500522 1.288625 1.94 0.0052** entrepreneurship .3215854 1.794436 0.18 0.858 constant -7.41177 2.638245 -2.81 0.005** number of obs 60 lr chi2(4) 43.72 pro>chi2 0.0000 pseudo 0.6283 log-likelihood -12.9349 significance. codes. *** = 1% significance level, ** = 5%, and * = 10% significance level from table 2, it is determined that; access to credit the study results show that having access to credit has a significant positive effect on reducing poverty in vicoba groups. for every unit increase in access to credit, the chance of reducing poverty increases by about 5.28 units. alternatively, the odds ratio calculation reveals that a unit increase in access to credit is linked to a 197.54-fold rise in the probability of poverty reduction among vicoba members. these results contribute to the understanding of microfinance’s role in poverty reduction by highlighting the significance of access to credit within vicoba groups. khanam et al. (2018) previously determined the contribution of microfinance services, particularly credit, to poverty reduction, especially among female participants. the current findings align with this perspective, indicating a positive impact of credit access on poverty reduction among vicoba members. furthermore, utonga and ndoweka (2023) and hussain et al. (2018) emphasised the importance of microfinance in poverty alleviation, further supporting the notion that access to credit can lead to tangible outcomes in reducing poverty. scholars such as ngong (2023) also discussed the nexus between microfinance and poverty alleviation, reinforcing the significance of credit access for enhancing financial inclusion and reducing poverty levels. the convergence of findings from these scholars emphasises the critical role of access to credit in poverty alleviation efforts within microfinance institutions like vicoba groups. this corroboration confirms that enhancing credit accessibility can effectively contribute to poverty reduction outcomes, empowering individuals to improve their economic conditions and achieve sustainable development goals. savings the study findings suggest that the ability to save has a statistically significant positive effect on poverty reduction among vicoba members. each unit increase in the ability to save is associated with a predicted probability of poverty reduction of approximately 4.32 units. using the odds ratio formula, a value of 4.321305 equals an odds ratio of 75.53. this means that when other factors are considered, a one-unit increase in vicoba members’ ability to save is linked to a 75.53-fold increase in their chances of falling out of poverty. this study contributes to the growing body of literature on microfinance and poverty reduction by demonstrating the significant impact of saving behaviour within vicoba groups. the findings align with previous research by ngong et al. (2021), which discussed the role of financial inclusion in poverty alleviation, highlighting the contribution of saving behaviour to enhancing financial stability and reducing poverty levels. the current study’s findings corroborate this perspective, indicating that the ability to save plays a pivotal role in empowering individuals to improve their economic conditions. additionally, ali et al. (2023) argued that financial literacy impacts financial satisfaction, suggesting that encouraging saving can result in economic well-being and poverty reduction. this aligns with the present study’s results, which indicate that increasing the ability to save among vicoba members can effectively contribute to poverty reduction outcomes. insurance services the study findings suggest that access to insurance services has a statistically significant positive effect on poverty reduction among vicoba members in the study area. specifically, each unit increase in access to insurance services is associated with a predicted probability of poverty reduction of approximately 2.50 units. using the odds ratio formula, 2.500522 equals an odds ratio of 12.18. this means that when other factors are considered, a one-unit increase in vicoba members’ access to insurance services is linked to a 12.18-fold increase in their chances of getting out of poverty. these results establish the importance of access to insurance services in poverty reduction efforts within microfinance interventions. furthermore, ngong et al. (2021) discussed the microfinancial inclusion nexus and its role in poverty alleviation, pa ge 97 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 92-98, 2024 supporting the idea that access to insurance services enhances financial stability and reduces poverty levels. similarly, khanam et al. (2018) argued for the significant role of microfinance services, including insurance, in poverty reduction, further reinforcing the positive influence of insurance services on poverty reduction outcomes. the collaborative view from these studies supports the critical role of access to insurance services in poverty alleviation efforts within microfinance interventions. the evidence fortifies that improving access to insurance services can effectively contribute to poverty alleviation outcomes, empowering individuals to enhance financial security and achieve sustainable development goals. model fitness the lr chi-square statistic yielded a value of 43.72 (p < 0.0001), indicating statistical significance. this shows that the logistic regression model is an excellent way to explain the connection between the independent variables (like access to credit, savings, insurance services, and business skills) and the dependent variable (like reducing poverty) among vicoba members in the study area. furthermore, the pseudo-r-squared value of 0.6283 suggests that the independent variables in the model can account for about 63% of the variation in poverty alleviation. this indicates the model’s moderately explanatory power, implying that the selected independent variables collectively contribute to understanding the factors influencing poverty alleviation among vicoba members. conclusion the results of this study shed light on the sociodemographic characteristics of respondents and their relationship with poverty reduction among members of vicoba. the findings reinforce the importance of access to credit, savings, and insurance services in promoting poverty reduction initiatives within microfinance systems. the significant representation of women in vicoba initiatives highlights the potential of microfinance to empower women economically, thereby contributing to poverty reduction efforts. similarly, the presence of youth and individuals with higher levels of education suggests a demographic well-positioned for economic advancement and participation in income-generating activities, further promoting poverty alleviation. furthermore, logistic regression analysis revealed the significant positive impact of access to credit, the ability to save, and access to insurance services on poverty reduction among vicoba members. each unit increase in these factors was associated with a substantial increase in the predicted probability of poverty reduction, highlighting the effectiveness of these interventions in addressing poverty challenges. the lr chi-squared statistic and pseudo-r-squared value, which indicate the logistic regression model’s statistical significance, confirmed the model’s robustness in explaining the connection between independent variables and vicoba member poverty reduction. approximately 63% of the variation in poverty alleviation could be explained by the variables included in the model, demonstrating moderately explanatory power. recommendations based on the findings of this study, several recommendations are made to strengthen poverty alleviation efforts within microfinance initiatives such as vicoba, a. efforts should be intensified to promote women’s economic empowerment through targeted interventions within microfinance programmes. this may include providing tailored financial products and capacitybuilding programmes to increase women’s participation and leadership in micro-enterprises. b. there is a pressing need to actively engage youth in economic activities and provide them with skills training and entrepreneurship opportunities. initiatives aimed at harnessing the potential of the youth demographic can contribute significantly to poverty reduction and sustainable development. c. policymakers and stakeholders should prioritise efforts to strengthen financial inclusion by expanding access to credit, savings, and insurance services, especially among marginalised groups. this may involve the development of innovative financial products and using technology to reach underserved populations effectively. d. educational programmes aimed at improving financial literacy and promoting a culture of saving and insurance should be implemented within communities. raising awareness of the benefits of microfinance and financial planning can empower individuals to make informed decisions and improve their economic well-being. e. regular monitoring and evaluation of microfinance programmes is essential to assess their impact, identify areas for improvement, and ensure accountability. stakeholders should adopt reliable monitoring mechanisms to track progress towards poverty reduction goals and make evidence-based decisions to improve the programme. references ahmad, i. 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(2023). determinants of a bank’s efficiency in an emerging economy: a data envelopment analysis approach. plos one, 18(3), e0281663. https://doi.org/10.1371/journal. pone.0281663. urt | the united republic of tanzania (2003) the cooperative societies act no. 20 of 2003. urt | the united republic of tanzania (2012) national microfinance policy. ministry of finance and economic affairs. utonga, d., & ndoweka, b. n. (2023). impact of financial development on inflation in tanzania: empirical evidence from the vecm approach. american journal of economics and business innovation, 2(2), 64–73. https://doi.org/10.54536/ajebi.v2i2.1648 waqas, h. & md-rus, r. (2018). predicting financial distress: applicability of the o-score model for pakistani firms. business and economic horizons, 14(2), 389–401. https://doi.org/10.15208/beh.2018.28. pa ge 1 pa ge 11 american journal of applied statistics and economics (ajase) the effect of unemployment on economic growth in nigeria rasheed olayemi nojeem1* volume 2 issue 1, year 2023 https://journals.e-palli.com/home/index.php/ajase article information abstract received: april 16, 2023 accepted: may 08, 2023 published: may 17, 2023 this study investigates the trends and effects of unemployment on economic growth in nigeria using secondary data from relevant institutions for analysis from 2010 to 2020. the auto regressive distribution lag (ardl) bounds test methodology is utilized to determine the long run relationship between unemployment and economic growth. the empirical findings using the ardl model confirmed that there exists an inverse relationship between economic growth and unemployment, and that unemployment leads to increasing crime rates in both short and long run. keywords economic growth, unemployment, nigeria, autoregressive distribution lag model, labor force 1 abeokuta, nigeria * corresponding author’s e-mail: rasheedolayeminojeem@gmail.com introduction nigeria is presently experiencing a high rate of unemployment which has significantly contributed to a higher level of poverty, whose effects and consequences have contributed to an increasing rate of insecurity (egunjobi, 2021). unemployment is both an economic and social issue affecting almost all countries and all people directly or indirectly. it causes social anxiety, and is manifested in the wave of crimes, youth unrest and unstable socioeconomic structure rampant in some nations. the world, particularly developing nations like nigeria are facing serious job challenges and widespread decent work deficits (adesina, 2013). okun’s law explains a meaningful empirical relationship of output (gdp) and unemployment in macroeconomic theory. the law has been found to hold for many nations. literature review over the years, theoretical and practical research has been carried out between unemployment and economic growth, most importantly the study done by arthur okun (1962). according to okun’s study, increase in gdp causes a reduction in unemployment rate, on the other way round, low growth rate causes a risk in unemployment rate. a nigerian, rafindadi, 2012) conducted a research using the ols and threshold model, the study revealed that a negative nonlinear relationship exists between gdp (output) and unemployment rate. omoke and ugwuanyi, 2010) employ the co-integration and gander-causality test analysis to examine the relationship between money, inflation and output in nigeria. the study revealed that there is no co-integrating relationship among the variables. some studies also confirmed that unemployment contributes significantly to the increasing crime waves in nigeria. in the post-colonial times, nigeria started experiencing high rate of poverty and unemployment due to a fall in the price of crude oil since 1982, resulting in reduction in foreign exchange earnings, inadequate raw materials for manufacturing industries which invariably affect business firms, leading to economic downturn, unemployment and poverty (ada and chigozie, 2010). due to this scenario, it becomes imperative for incoming governments to formulate various policies to address the rise in poverty, unemployment and crime rate by coming up with different programs. deep-seated corruption and lack of good will by political leaders to formulate and implement policies, conflicts emanating from ethnicity, sectionalism, negligence of the agricultural sector and monoculture (reliance of the country solely on oil revenue) contribute to nigeria economic mess. materials and methods the data used in this research are collected from the world bank database, the national bureau of statistics (nbs) and international labour organization (ilo) covering 2010 through 2020. okun’s model is adopted in the study. unemployment and crime rates are the independent variables, while economic growth based on real gdp is the dependent variable. it should be noted that the okun’s law is a reduced version of philips curve which explains that there is inverse relationship between the gdp growth rate and unemployment rate. the model explains thus; https://journals.e-palli.com/home/index.php/ajahs http://rasheedolayeminojeem@gmail.com pa ge 12 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 11-14, 2023 rgdp = f (uempl….)------------------------------(1) rgdp = β1 + β2 unempl + µ-----------------------(2) rgcr = (crimr) rgdp = gdp growth (as in economic growth) unempl is the unemployment rate β1, β2 = parameters µ = error term β1 < 0 and β2 > 0 (meaning that the expectations are β1, β2 having negative values) to examine whether there is a long run relationship the unrestricted error-correction model (uecm) is adopted: ∆𝐿𝑌𝑡=𝛼1+𝛼𝑇𝑇+𝛼𝑌𝐿𝑌𝑡−1+𝛼𝑈𝑛𝑒𝑚𝑝𝑙l𝑈𝑛𝑒𝑚𝑝𝑙𝑡−1+ ∑ 𝛼𝑖∆𝐿𝑌𝑡−𝑖𝑃 i=1 ++ ∑ 𝛼𝑗∆𝑈𝑁𝑡−j 𝑞𝑗=0 +𝜀1𝑡 then, the use of ardl model to ascertain the retention of lagged level variables is done. the null and alternative hypotheses are as follow: ho : y = y2 = 0 hα : y1 ≠ y2 ≠ 0 if t-calculated is greater than the table value, we reject the null hypothesis (ho) and accept the alternative hypothesis (h1), on the other hand, if t-calculated is less than the table value, we accept the null hypothesis (ho) and reject the alternative hypothesis (h1). results and discussions the data is used for an empirical analysis of unemployment for the period between 2010 and 2020. the data give a detailed analysis of the estimation result, and provide evidence on the extent and direction of influence of key variables on empirical analysis of unemployment, economic growth and insecurity in nigeria. the specific equations are estimated using the auto regression distribution lag (ardl). the interpretation of the result in respect of the coefficient of various regressions is stated in table 1 and 2 below at 95% level of significance. table 1: the interpretation of the result in respect of the coefficient of various regressions variable coefficient std. error t-statistic prob. c 86.65519 157.1506 0.551415 0.5964 unemr -21.58504 43.44426 -0.496845 0.6327 crimr 0.000274 0.000727 0.376487 0.7163 r-squared 0.034069 mean dependent var 7.705155 adjusted r-squared -0.207414 s.d. dependent var 2.787632 s.e. of regression 3.063117 akaike info criterion 5.303744 sum squared resid 75.06150 schwarz criterion 5.412261 log likelihood -26.17059 hannan-quinn criter. 5.235339 f-statistic 0.141081 durbin-watson stat 1.883278 prob(f-statistic) 0.870532 table 2: vector autoregression estimates date: 04/27/22 time: 07:50 sample (adjusted): 2010 – 2020 included observations: 9 after adjustments standard errors in ( ) & t-statistics in [ ] gdpg unemr crimr gdpg(-1) -10.68980 0.045756 -4227.818 (18.0119) (0.07148) (17496.9) [-0.59349] [ 0.64017] [-0.24163] gdpg(-2) 10.49381 -0.040689 4815.623 (18.9805) (0.07532) (18437.8) [ 0.55287] [-0.54023] [ 0.26118] unemr(-1) -2509.378 10.76068 -1059910. (4558.20) (18.0879) (4427871) [-0.55052] [ 0.59491] [-0.23937] unemr(-2) 2678.545 -10.41685 1099358. (4616.53) (18.3194) (4484537) [ 0.58021] [-0.56862] [ 0.24514] crimr(-1) -0.000904 3.70e-06 0.345943 https://journals.e-palli.com/home/index.php/ajase pa ge 13 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 11-14, 2023 (0.00080) (3.2e-06) (0.78160) [-1.12372] [ 1.15961] [ 0.44261] crimr(-2) -0.000508 2.11e-06 0.469593 (0.00081) (3.2e-06) (0.78736) [-0.62646] [ 0.65616] [ 0.59642] c -663.6666 2.549163 -173171.3 (387.078) (1.53601) (376010.) [-1.71456] [ 1.65960] [-0.46055] r-squared 0.819845 0.986470 0.842509 adj. r-squared 0.279380 0.945879 0.370035 sum sq. resids 11.72001 0.000185 11059394 s.e. equation 2.420744 0.009606 2351.531 f-statistic 1.516924 24.30289 1.783185 log likelihood -13.95877 35.80616 -75.86749 akaike aic 4.657505 -6.401369 18.41500 schwarz sc 4.810902 -6.247972 18.56840 mean dependent 8.202567 3.845944 15444.44 s.d. dependent 2.851646 0.041292 2962.731 determinant resid covariance (dof adj.) 0.000000 determinant resid covariance 0.000000 explanation of the figures & tables: the coefficient of the variable, c = 86.65519 this value shows that when unemployment rate and crime rate are held constant (i.e. equal to zero), the nigerian economy would grow by 86.65519. for unemployment rate t statistics value = 0.496845, while the table value = -2.365 (from t test table). then, 0.496845 > 2.365 therefore, we reject the null hypothesis. for crime rate, t statistics value = 0.376487, while the table value = 2.365 0.376487 < 2.365 therefore, we accept the null hypothesis. the value of r-squared = 0.819845 expressing this as a percentage gives 81.98%. this shows that unemployment and crime rate have 81.98% on the economic growth in nigeria. conclusion the study investigates the effect of unemployment on economic growth using data between 2010 through 2020 in nigeria and utilizing arthur okun’s law. the autoregressive distribution lag (ardl) approach revealed that there is a long run relationship between economic growth rate and unemployment rate in nigeria. also, an increase in unemployment rate leads to an increase in crime rate. limitation poor financing is a limiting factor to this research work. recommendation moral reformation: impressive growth cannot be achieved in an economy without moral values. today, nigeria is ranked one of the most corrupt nations in the world. a country having many of its population as fraudulent, inconsiderate and dishonest people will not grow. there is a compelling need to re-orientate the citizens of the country to be honest, truthful, sincere, just and fair in all of their endeavors. the education sector: the nigerian education system is tailored to produce job seekers rather than job creators. the syllabuses and curriculums used in nigerian schools need to be re-designed so that learning in schools is directed toward self-employment, self-reliance and selfemancipation. the rule of law: african states, particularly nigeria are facing a drastic decline in social justice. gone are days when everyone is accountable to the law. political office holders and government officials are fond of manipulating court orders when they embezzle or loot the public treasury. billions of naira earmarked for capital projects in various sectors of the economy in order to bring about tremendous growth has been siphoned to private pockets by political office holders and government officials. for the economy to grow, nigerian judicial system needs a complete overhaul. the sharpest weapon against corrupt practices is prosecution. corruptions increase when corrupt elements go scot-free. the petroleum sector: nigerian refineries need to be repaired to enable them work at full capacity. petroleum revenue should be judiciously spent. subsidiary companies should be established for processing and marketing various by-products of crude oil. agricultural sector: an efficient agricultural sector is needed to feed the ever increasing population in the https://journals.e-palli.com/home/index.php/ajase pa ge 14 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 11-14, 2023 country, and to also create employment opportunities, provide raw materials for local industries and play a vital role in production and exportation of cash crops to facilitate the much needed growth in the nigeria economy. reference ahmed a. (2011). financial structure and economic growth link in african countries: a panel cointegration analysis. journal of economic studies 38(3), 331-357. biyase m. and bonga-bonga l. (2010). south africa’s growth paradox. university of johannesburg working paper. bankole a. and fatai b. (2013). empirical test of okun’s law in nigeria, international journal of economic practices and theories 3(3), 23-36. chanshuai l. and zi-juan l. (2012). the relationship among chinese unemployment rate, economic growth and inflation. the aaef journal of economics 1(1), 1-3. egunjobi t. adenike (2021.) tanzanian economic review, 11, 115—136. h. (2016). the impact of economic growth on unemployment in south africa: 1994-2012, investment management and financial innovations, 13(2), 246255. irfan m. (2010). financial crises and economic growth: a time series analysis, munich personal repec archive paper no. 40691. kingdon g. and knight j. (2007). unemployment in south africa 1995-2003: causes, problem and policies, journal of african economics, 16(5), 813-848. osinubi t. (2005). macroeconometric analysis of growth, unemployment and poverty in nigeria, pakistan economic and social review 523(2), 249-269. okun a. (1962). potential gnp: its measurement and significance in statistics section? american statistical association, washington dc . omolewa egor ese (2018).entrepreneurship development lecture notes. omoke p. and ugwanyi c. (2010). money, price and output: a causality test for nigeria, american journal of scientific research 7(8), 78-87. pesaran m., shin y. and smith r. (2001). bounds testing approaches to the analysis of level relationships, journal of applied econometrics 16(2), 289-326. rafinandadi a. (2012). empirical analysis of the effects of central bank communications on money market volatility in nigeria: 2007-2011,unpublished masters thesis ahmadu bello university. shadid a., (2015). india-saudi arabia bilateral trade relations: recent experiences and future opportunities, international journal of economics and empirical research (ijeer) 3(7), 327–342. stephen g. (2012). reassessing the impact of finance on growth, bank for international settlements working paper, no. 381. wang s. and abrams a. (2007). can the budget boost agricultural performance: a var. journal of economics and management, university of economics, katowice, 35(1) 2019. https://journals.e-palli.com/home/index.php/ajase pa ge 1 pa ge 15 american journal of applied statistics and economics (ajase) comparing the budgetary allocations to the education sectors of nigeria and some african countries (1999-2021) in view of unesco’s benchmarks emmanuel uchenna ohaegbulem1* volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2280 https://journals.e-palli.com/home/index.php/ajase article information abstract received: november 22, 2023 accepted: december 27, 2023 published: december 31, 2023 the percentage budgetary allocations to the education sectors of nigeria alongside those of some other selected african countries (which included ghana, south africa, senegal, kenya and morocco) from 1999 to 2021 in view of the unesco recommended minimum benchmarks were assessed in this paper. secondary data from the 2018 edition of the statistical bulletin of the central bank of nigeria and the official website of the budget office of the federal government of nigeria (budgetoffice.gov.ng) were analyzed. descriptive statistics, the analysis of variance (anova) test, the student’s t-test for equality of two population means and ranking were all employed in the data analyses. results showed that for the period under study, (1999-2021), the annual percentage budgetary allocations to the education sector by the government of nigeria were significantly lower than those of ghana, south africa, senegal, kenya and morocco; those of ghana were significantly higher than each of those of south africa and morocco, but were not significantly different from those of the governments of senegal and kenya; those of south africa are not significantly different from those of morocco, but were significantly lower than each of those of senegal and kenya; those of senegal were not significantly different from those of kenya, but were significantly higher than those of morocco; while those of kenya were significantly higher than those of morocco. furthermore, for the period, (1999-2021), the average percentage budgetary allocation to the education sector by the fgn was significantly lower than each of the unesco’s 15%, 20% and 26% recommended benchmarks; that of ghana was significantly higher than each of the unesco’s 15% and 20% recommended benchmarks, but not that of 26%; that of south africa and senegal were significantly higher than the unesco’s 15% benchmark, not significantly different from the 20% benchmark, but were significantly lower than the 26% benchmark; that of kenya was significantly higher than each of the unesco’s 15% and 20% recommended benchmarks, but is significantly lower than the 26% benchmark; while that of morocco was significantly higher than the unesco’s 15%, but significantly lower than each of the 20% and 26% recommended benchmarks. finally, based on the levels of adherence to the unesco’s recommended minimum benchmarks, ghana was ranked first, while kenya, senegal, south africa, morocco and nigeria were ranked second, third, fourth, fifth and a distant sixth, respectively. thus, for the period, (1999-2021), ghana, kenya, senegal, south africa and morocco outperformed nigeria in terms of the percentage budgetary allocations to their respective education sectors as well as the levels of adherence to unesco’s recommended minimum benchmarks. keywords percentage budgetary allocations, unesco benchmarks, education sector, education financing, african countries 1 department of statistics, imo state university, owerri, imo state, nigeria * corresponding author’s e-mail: emmanx2002@yahoo.com introduction education is recognized as a major factor of national development in all countries of the world, as it is one of the primary sources that help in achieving human capital development. according to omotor (2017) and odigwe and owan (2019), for example, the immense contributions of education to any nation’s status cannot be overemphasized, especially in the areas of technological development, sociopolitical stability and wealth-creation. so, as stated by odigwe and owan (2019), investment in education is as important as the plan for national building. among many aspects of globalization, the most noticeable one is education funding (tilak and panchamukhi, 2023). funding of education is primarily the government’s responsibility. the allocation of sufficient financial resources to education is essential for achieving sustainable economic growth and development. ifionu and nteegah (2013) assert that the budget is a key government tool for implementing social, political and economic policies and priorities. budgetary allocations to the education sector are channeled through appropriate organs of government and such funds are in turn disbursed to all the levels of education. many international organizations that are involved in human capital development recommend that governments should allocate at least 4-6% of their gross domestic products (gdps) and/or at least 15-20% of their total public expenditures to education (see, for example, tilak and panchamukhi, 2023). the united nations educational scientific and cultural organization (unesco), perceiving the poor funding to the education sectors of many developing countries by her respective governments, was mandated to recommend a minimum benchmark of 26% of the total annual budgets of every developing country be allocated to the education sectors (see, for example, ekaette et al, 2019). all developing countries are expected to adhere to this benchmark directive, as their standards of education would be pa ge 16 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 15-23, 2024 improved (see, for example, callaway and musone, 1968). surprisingly and unfortunately, most of the developing countries, including some in africa, are still experiencing inadequate funding to their education sectors. notably, according to gandhi (2020), almost half of all african countries are meeting the recommended education financing targets set by the united nations. gandhi (2020) also added that, while many african countries met at least one of the two education financing targets, only 46% met both targets for the period, (2012-2017). there is therefore the need to evaluate the annual percentage budgetary allocations to the education sectors of some selected african countries, and to compare them in view of the unesco’s recommended benchmarks. materials and methods the major data used for this study, as presented in table 1, were obtained from two separate sources, namely, the 2018 edition of the statistical bulletin of the central bank of nigeria and the official website of the budget office of the federal government of nigeria (budgetoffice.gov. ng). table 1 shows the percentage budgetary allocations to the education sectors of some selected african countries (namely, nigeria, ghana, south africa, senegal, kenya and morocco) by their respective federal governments, from 1999 to 2021 (this range of time was considered so as to have a clear comparison with respect to the current democratic dispensation being enjoyed in nigeria). a few descriptive analyses shall be employed in this study, especially the use of graphical representations to show the trends in the percentage budgetary allocations to the education sector in the selected countries, as well as the years under study. the analysis of variance (anova) test shall also be used to determine whether or not significant differences exist among the percentage budgetary allocations to the education sectors of nigeria, ghana, south africa, senegal, kenya and morocco by their federal governments and the unesco recommended benchmarks of 15-20% for international, and 26% for developing countries ghana, south africa, senegal, kenya and morocco, from 1999 to 2021. the student’s t-test for equality of two population means shall also be employed in ascertaining significant differences or otherwise between the average percentage budgetary allocations to the education sectors by the respective governments of each of nigeria, ghana, south africa, senegal, kenya and morocco and the perceived average of the three unesco’s recommended benchmarks of 15%, 20% and 26%. lastly, ranking shall be employed in placing the selected african countries in terms of their levels of adherence to the unesco recommended benchmarks. table 1: the percentage budgetary allocations to the education sectors by the governments of some african countries (1999-2021) year country/percentage budgetary allocation nigeria ghana south africa senegal kenya morocco 1999 11.12 11.73 15.24 16.82 24.55 5.14 2000 8.36 14.20 18.09 17.62 23.40 8.28 2001 7.00 19.54 20.47 15.73 22.41 10.11 2002 5.90 22.07 20.10 16.72 25.63 13.91 2003 1.83 20.30 19.59 16.12 24.98 16.67 2004 10.50 26.02 19.93 16.96 26.67 15.16 2005 9.30 25.85 19.92 21.77 27.47 12.22 2006 11.00 20.30 18.00 17.95 25.08 16.31 2007 8.09 26.00 18.03 18.73 21.03 17.27 2008 13.00 25.85 17.91 19.23 18.64 18.61 2009 6.54 23.87 18.31 23.30 15.72 19.65 2010 6.40 20.70 18.04 24.05 20.56 18.29 2011 1.69 30.63 18.96 21.09 19.25 18.10 2012 10.00 37.53 20.64 20.80 19.92 17.58 2013 8.70 31.00 25.76 25.74 19.14 16.93 2014 10.60 20.99 19.14 24.76 17.08 16.31 2015 9.50 23.81 18.70 23.76 16.66 17.14 2016 6.10 22.09 18.05 21.34 17.34 20.18 2017 7.38 20.10 18.72 25.60 17.88 19.89 2018 7.03 18.61 18.87 27.80 20.02 20.46 2019 7.20 26.72 20.00 22.61 21.70 25.77 2020 6.70 33.54 22.21 26.08 26.70 30.02 2021 5.60 39.02 23.50 25.81 27.20 31.06 pa ge 17 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 15-23, 2024 results and discusions graphical displays by way of plots have been carried out to express some of the characteristic features inherent in the data used in this study. the two-way anova test have been used to determine whether or not significant differences existed among the annual percentage budgetary allocations to the education sectors of nigeria, ghana, south africa, senegal, kenya and morocco by their respective governments from 1999 to 2021, as well as the three unesco’s recommended minimum benchmarks of 15%, 20% and 26%. furthermore, where the null hypothesis was rejected (thus, significant difference was established), a post hoc pairwise comparison test (via the student’s t-test) was used to ascertain the actual cause of the rejection of the null hypothesis. this was subsequently followed by the ranking of each the country’s performances with respect to the average percentage budgetary allocations to their respective education sectors. these data analyses are presented below: assessing the levels of adherence by the governments of some selected african countries (1999-2021) to the unesco’s benchmarks the yearly plots of the percentage budgetary allocations to the education sectors by the respective governments of some selected african countries (which include nigeria, ghana, south africa, senegal, kenya and morocco) from 1999 to 2021; as well as the unesco’s recommended minimum benchmarks, are as presented in figures 1 to 6. figure 1: percentage budgetary allocations to the education sector of nigeria (1999-2021) figure 2: percentage budgetary allocations to the education sector of ghana (1999-2021) pa ge 18 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 15-23, 2024 it is evident from figure 1 that all the annual percentage allocations to the education sector by the fgn from 1999 to 2021 fall below the three unesco’s benchmarks. figure 2 shows that the percentage budgetary allocations to the education sector by the government of ghana for 1999 and 2000 did not even reach the 15% benchmark, while the allocations for 2004, 2005, 2007, 2008, 2011, 2012, 2013, 2019, 2020 and 2021 reached the 26% benchmark and even above. also, the allocations for the other years within the period under study fall between the 15% and 26% benchmarks. the plots in figure 3 show that the percentage allocations figure 3: percentage budgetary allocations to the education sector of south africa (1999-2021) figure 4: percentage budgetary allocations to the education sector of senegal (1999-2021) to the education sector by the government of south africa for the period under study were between the 15% and 26% unesco’s benchmarks. figure 4 shows that almost all the percentage allocations to the education sector by the government of senegal for the period under study appear to fall between the 15% and 26% benchmarks, except that of 2018 which falls above the 26% benchmark. from figure 5, it is evident that the annual percentage budgetary allocations to the education sector by the government of kenya for 2004, 2005, 2020 and 2021 fall above the unesco’s benchmark of 26%, while the percentage budgetary allocations for the rest of the years in the period under study fall between 15% and 26% benchmarks. the plots in figure 6 show that the percentage allocations pa ge 19 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 15-23, 2024 to the education sector by the government of morocco for 1999, 2000, 2001, 2002 and 2005 fall below the unesco’s 15% benchmark; those of 2020 and 2021 fall above the unesco’s 26% benchmark; while the percentage budgetary allocations for the rest of the years in the period under study fall between 15% and 26% benchmarks. the summary statistics for the annual percentage budgetary allocations to the education sectors of nigeria, ghana, south africa, senegal, kenya and morocco by their respective governments, from 1999 to 2021, are as presented in table 2. figure 5: percentage budgetary allocations to the education sector of kenya (1999-2021) figure 6: percentage budgetary allocations to the education sector of morocco (1999-2021) table 2: summary statistics for the annual percentage budgetary allocations to the education sectors of the selected african countries (1999-2021) statistic nigeria ghana south africa senegal kenya morocco mean 7.81 24.37 19.49 21.32 21.70 17.61 standard error 0.572 1.390 0.446 0.781 0.780 1.241 median 7.38 23.81 18.96 21.34 21.03 17.27 standard deviation 2.742 6.665 2.138 3.747 3.743 5.954 pa ge 20 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 15-23, 2024 with all the observations on figures 1 to 6, as well as the summary statistics in table 2, it becomes necessary to test for significant differences among the annual percentage budgetary allocations to the education sectors by the respective governments of the selected african countries for the period under study. this is done via the anova test; and the result outputs are as presented in table 3. the anova test results presented in table 3 show that there was significant difference in the percentage budgetary allocations to the education sectors among the years under study by the governments of the selected african countries. also, the results show that, for the period under study (1999-2021), there was significant difference among the percentage budgetary allocations by each of the governments of nigeria, ghana, south africa, senegal, kenya and morocco to their respective education sectors, as well as each of the unesco’s 15%, 20% and 26% recommended benchmarks. as a follow up, a post hoc pairwise comparison test was further conducted; and the summary of the respective p-values of the pairwise comparison between any two of the selected countries in terms of differences as well as each of the unesco’s benchmarks are as presented in table 4. sample variance 7.521 44.424 4.570 14.038 14.007 35.450 kurtosis 0.590 0.351 2.902 -1.348 -1.338 1.155 skewness -0.488 0.515 1.204 0.0165 0.115 0.365 range 11.31 27.29 10.52 12.07 11.75 25.92 minimum 1.69 11.73 15.24 15.73 15.72 5.14 maximum 13.00 39.02 25.76 27.80 27.47 31.06 sum 179.54 560.47 448.18 490.39 499.03 405.06 count 23 23 23 23 23 23 table 3: anova table (two-factor without replication) source of variation ss df ms f p-value f-critical years 577.980 22 26.272 2.242 0.0021* 1.603 % allocations of some countries & unesco’s benchmarks 5390.418 8 673.802 57.505 3 . 6 9 e 45* 1.991 error 2062.249 176 11.7173 total 8030.647 206 ✻ significance table 4: summary of the test for significant difference among the percentage budgetary allocations to the education sectors by the governments of some selected african countries (1999-2021) country ghana south africa senegal kenya morocco nigeria 3.1e-14* 4.6e-20* 9.0e-18* 3.3e-18* 6.4e-09* ghana 1.7e-03* 0.063 0.101 7.4e-04* south africa 0.047* 0.018* 0.162 senegal 0.735 0.015* kenya 7.8e-03* morocco ✻ significance the p-values in the result outputs in table 4 show that the percentage budgetary allocations to the education sector by the government of nigeria for the period under study, (1999-2021), are significantly different from (lower than) those of ghana, south africa, senegal, kenya and morocco. for the same period under study, the percentage budgetary allocations to the education sector by the government of ghana are significantly different from (higher than) each of those of the governments of nigeria, south africa and morocco, but not significantly different from those of the governments of senegal and kenya. also, the percentage budgetary allocations to the education sector by the government of south africa are not significantly different from those of morocco, but they are significantly higher than those of the government of nigeria and significantly lower than each of those of the governments of ghana, senegal and kenya. furthermore, the percentage budgetary allocations to the education sector by the government of senegal are not significantly different from those of the governments of ghana and kenya, but they are significantly higher than those of the governments of nigeria, south africa and pa ge 21 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 15-23, 2024 morocco. also, the percentage budgetary allocations to the education sector by the government of kenya are not significantly different from those of the governments of ghana and senegal, but they are significantly higher than those of the governments of nigeria, south africa and morocco. lastly, the percentage budgetary allocations to the education sector by the government of morocco are not significantly different from those of south africa, but they are significantly higher than those of the government of nigeria and significantly lower than each of those of the governments of ghana, senegal and kenya. furthermore, observations made from the viewing of figures 1 to 6 necessitated the conduction of the student’s t-test (without equal variances assumed). the student’s t-test was used to ascertain if the annual percentage allocations to the education sectors of each of the selected african countries by their respective governments are significantly different (less than or greater than) or even not different from each of the unesco’s 15%, 20% and 26% recommended benchmarks. the summary of some of the descriptive statistics and the test results outputs are as presented in table 5. table 5: some descriptive statistics and the result outputs’ of the student’s t-test for significant differences of the percentage budgetary allocations to the education sectors of some selected african countries (1999-2021) country min. value max. value mean standard deviation unesco’s benchmarks 15% 20% 26% nigeria 1.69 11.12 7.81 2.74 1.79e-16*l 3.97e-25*l 2.62e-32*l ghana 11.73 39.02 24.37 6.66 1.37e-08*h 1.50e-03*h 0.12# south africa 15.24 25.76 19.49 2.13 2.74e-13*h 0.13# 8.56e-19*l senegal 15.73 27.80 21.32 3.75 1.49e-10*h 0.05# 1.75e-07*l kenya 15.72 27.47 21.70 3.74 3.00e-11*h 0.02*h 8.69e-07*l morocco 5.14 31.06 17.61 5.95 0.02*h 0.03*l 1.30e-08*l *l significantly low difference *h significantly high difference # no significant difference the p-values in the result outputs in table 5 show that the average percentage budgetary allocation to the education sector by the government of nigeria for the period, 1999 to 2021 (which is about 7.81%), is significantly lower than each of the unesco’s 15%, 20% and 26% recommended benchmarks. the average percentage budgetary allocation to the education sector by the government of ghana for the period, 1999 to 2021 (which is about 24.37%), is significantly higher than each of the unesco’s 15% and 20% recommended benchmarks but not that of 26%. the average percentage budgetary allocation to the education sector by the government of south africa for the period, 1999 to 2021 (which is about 19.49%), is significantly higher than the unesco’s 15% benchmark, not significantly different from the 20% benchmark but significantly lower than the 26% benchmark. the average percentage budgetary allocation to the education sector by the government of senegal for the period, 1999 to 2021 (which is about 21.32%), is significantly higher than the unesco’s 15% benchmark, not significantly different from the 20% benchmark but significantly lower than the 26% benchmark. the average percentage budgetary allocation to the education sector by the government of kenya for the period, 1999 to 2021 (which is about 21.70%), is significantly higher than each of the unesco’s 15% and 20% recommended benchmarks but significantly lower than the 26% benchmark. the average percentage budgetary allocation to the education sector by the government of morocco for the period, 1999 to 2021 (which is about 17.61%), is significantly higher than the unesco’s 15% but significantly lower than each of the 20% and 26% recommended benchmarks. adherence levels to the unesco’s benchmarks by the governments of each of the selected african countries (1999-2021) the average percentage budgetary allocations to the education sectors by the different governments of the selected african countries from 1991 to 2021 were ranked with a view to showing their levels of adherence to the unesco’s recommended benchmarks. in the first phase, the ranking is done as to know how the countries performed based on their average percentage budgetary allocations to their respective education sectors over the years under study. the second phase has the ranking done in order to place the levels of adherence to the unesco’s recommended benchmarks by each of the selected african countries. the rankings of the percentage budgetary allocations to the education sector by the government of nigeria, as well as the selected african countries are as presented in table 6. the ranking in table 6 shows that ghana was topmost (with a mean of 24.37%) in the level of adherence to unesco’s recommended benchmarks, while kenya (with a mean of 21.70%) and senegal (with a mean of 21.32%) came second and third places, respectively. furthermore, south africa (with a mean of 19.49%) and morocco (with a mean of 17.61%) occupied the fourth and fifth places, respectively; while nigeria (with a significantly low mean of 7.81% as well as having not leveled up with any of the three unesco’s recommended benchmarks) came a distant sixth position. pa ge 22 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 15-23, 2024 table 6: approaches of the ranking of the levels of adherence to the unesco’s benchmarks by the governments of each of the selected african countries (1999-2021) country ranking method 1 ranking method 2 % budgetary allocations no. of annual % budgetary allocations overall mean rank of the overall mean ≥ 15% ≥ 20% ≥ 26% average of rankno. rank no. rank no. rank nigeria 7.81 6 0 6 0 6 0 5.5 5.83 ghana 24.37 1 21 4 19 1 8 1 2.00 south africa 19.49 4 23 2 7 4 0 5.5 3.83 senegal 21.32 3 23 2 14 2.5 2 3.5 2.67 kenya 21.70 2 23 2 14 2.5 4 2 2.17 morocco 17.61 5 18 5 5 5 2 3.5 4.50 conclusion a two-factor anova test was carried out to test for significant difference(s) in the percentage budgetary allocations to the education sectors among the years under study by the respective governments of some selected african countries (which include nigeria, ghana, south africa, senegal, kenya and morocco) from 1999 to 2021, as well as the unesco’s recommended minimum benchmarks. the results show that there was a significant difference in the percentage budgetary allocations to the education sectors over the years, as well as among the percentage budgetary allocations by each of the governments of nigeria, ghana, south africa, senegal, kenya and morocco to their respective education sectors and each of the unesco’s 15%, 20% and 26% recommended benchmarks. the percentage budgetary allocations to the education sector by the government of nigeria for the period under study, (1999-2021), are significantly lower than those of ghana, south africa, senegal, kenya and morocco. for the same period under study, the percentage budgetary allocations to the education sector by the government of ghana are significantly higher than each of those of south africa and morocco, but not significantly different from those of the governments of senegal and kenya. also, the percentage budgetary allocations to the education sector by the government of south africa are not significantly different from those of morocco, but are significantly lower than each of those of senegal and kenya. the percentage budgetary allocations to the education sector by the government of senegal are not significantly different from those of kenya, but are significantly higher than those of morocco. lastly, the percentage budgetary allocations to the education sector by the government of kenya are significantly higher than those of morocco. furthermore, for the period, (1999-2021), the average percentage budgetary allocation to the education sector by the fgn (about 7.81%) is significantly lower than each of the unesco’s 15%, 20% and 26% recommended benchmarks; while that of ghana (about 24.37%) is significantly higher than each of the unesco’s 15% and 20% recommended benchmarks but not that of 26%. also, the average percentage budgetary allocation to the education sectors by each the governments of south africa (about 19.49%) and senegal (about 21.32%) are significantly higher than the unesco’s 15% benchmark, not significantly different from the 20% benchmark, but is significantly lower than the 26% benchmark. the one for kenya (about 21.70%) is significantly higher than each of the unesco’s 15% and 20% recommended benchmarks, but is significantly lower than the 26% benchmark; while the one for morocco (about 17.61%) is significantly higher than the unesco’s 15%, but significantly lower than each of the 20% and 26% recommended benchmarks. the average percentage budgetary allocations to the education sectors by the different governments of the selected african countries for the period, (1999-2021), were ranked to show their respective levels of adherence to the unesco’s recommended benchmarks. the process ranked ghana (first), kenya (second), senegal (third), south africa (fourth), morocco (fifth) and nigeria (a distant sixth) with mean annual percentage budgetary allocations of 24.37%, 21.70%, 21.32%, 19.49%, 17.61% and 7.81%, respectively. references callaway, a., & musone, a. (1968). financing of education in nigeria. african research monographs-15, unesco: international institute for educational planning. ekaette, s. o., owan, v. j., & agbo, d. i. (2019). external debts and the financing of education in nigeria from 1988-2018: implication for effective educational management. journal of educational realities (jera), 9(1), 55-68. ghandi, d. (2020). africa in focus. african economic outlook 2020. african development bank. ifionu, e. p., & nteegah, a. (2013). investment in education and economic growth in nigeria: 19812012. west african journal of industrial and academic research, 9(1), 155-172. odigwe, f. n., & owan, v. j. (2018). trend analysis of the nigerian budgetary allocation to the education sector from 2009 to 2018 with reference pa ge 23 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 15-23, 2024 to unesco benchmark. international journal of education benchmark, 14(1). omotor, d. g. (2017). analysis of federal government expenditure in the education sector of nigeria: implications for national development. journal of social sciences, 9(2), 105-110. tilak, j. b. g., & panchamukhi, p. (2023). globalization and the shifting geopolitics of education. international encyclopedia of education (fourth edition). pa ge 1 pa ge 28 american journal of applied statistics and economics (ajase) the nexus among transformational leadership and firm performance: testing the mediating role of knowledge sharing le ba phong1*, do thu hang1 volume 2 issue 1, year 2022 https://journals.e-palli.com/home/index.php/ajase article information abstract received: april 26, 2023 accepted: may 18, 2023 published: may 22, 2023 the purpose of this paper is to clarify the mediating roles of active and passive knowledge sharing (ks) in the relationship between transformational leadership (tl) and specific aspects of firm performance namely operational and financial performance. the paper used analysis of moment structures (amos) and structural equation modelling (sem) to investigate the influence of tl and knowledge sharing (ks) on aspects of firm performance using data from a survey of 235 manufacturers and suppliers in vietnam. the empirical findings show the significant and positive influence of tl, and ks activities in supply chain networks on firm performance. it highlighted the key role of active and passive ks in linking the effect of tl on firm’s operational and financial performance. especially, this study reveals that tl has a greater effect on financial performance compared to its effects on operational performance. especially, active ks has a greater effect on both firm’s operational and financial performance compared to the influence of passive ks. the paper has extended the theory of leadership, knowledge management and organizational performance by clarifying the critical roles of tl practice and processes of ks in the supply chain in improving firm’s operational and financial performance. keywords transformational leadership, active knowledge sharing, passive knowledge sharing, financial performance, operational performance, supply chain 1 hanoi university of industry, bactuliem, hanoi, vietnam * corresponding author’s e-mail: lebaphong.vn@gmail.com introduction under the increasing pressure in developing new products and services quickly and efficiently, firms have exerted to foster greater collaborative activities in supply chain networks to maintain and improve their long-term performance (nguyen et al., 2019; wang and hu, 2020). knowledge resources have been recognized as important strategic assets and have made remarkable contributions to firm performance and competitive advantage (obeidat et al., 2016; son et al., 2020; nguyen et al., 2022). however, the major challenge for today’s organizations in building their knowledge capital is to be aware of how they can better facilitate knowledge sharing (ks) activities among members in supply chain networks to contribute to the firm performance and success (rajabion et al., 2019; jen et al., 2020). among the typical leadership styles, transformational leadership (tl) is regarded as one of the most effective leadership styles with significant influences on ks activities and key organizational outcomes (le and lei, 2019; phong and son, 2020; gui et al., 2022). transformational leaders inspire and motivate ks behaviors among employees to obtain the greatest degree of achievement for organizational performance and managerial performance (ali et al., 2019; ha et al., 2019; son et al., 2020). to enrich the mechanisms and deepen our understanding of the nature of the relationship between ks and firm performance, this study introduces active and passive ks activities in supply chain as a new approach in explaining how tl and ks activities are transpired into firm performance. it is expected to bring deeper understanding of the potent pathway and mechanism for fostering firm’s performance by following motives. first, previous studies have found ks activities enable firms to successfully apply or replicate knowledge dispersed by interactive activities among individual firms and their supply chain networks (mishra and shah, 2009; wang and hu, 2020). these ks activities can not only enhance knowledge capital among different firms but also significantly contribute to increasing volume, variety, and engagement in improving innovation performance (wang and hu, 2020; le and do, 2023; le and le, 2023). even though, it is not easy for employees to share knowledge with others, especially in sharing knowledge with individuals in the other organization due to concerns of information leakage and lacks of trust (wang and noe, 2010; nguyen et al., 2019; le and nguyen, 2023). among the premise factors of ks, tl is acknowledged as a key factor having decisive influence on ks activities of individuals within an organization (hui et al., 2018; lei et al., 2019; yin et al., 2019; sheehan et al., 2020) or among members in supply chain (birasnav, 2013; ojha et al., 2018). given the important role of tl on ks activities for improving firm performance, the first goal of this study is to clarify tl’s effect on ks activities in supply chain by posing the first research question: rq1. does tl positively affect ks activities in supply chain networks? second, ks is an important basis for improving firm performance because it provides a complete set of essential skills and knowledge for individuals to work or achieve goals more efficiently (le and lei, 2019; singh et al., 2019). ks is often perceived a basic survival and least expensive strategy and a key source for firms to increase innovation competence (le and lei, 2019; le, 2021; le https://journals.e-palli.com/home/index.php/ajahs mailto:lebaphong.vn@gmail.com pa ge 29 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 28-40, 2023 and le, 2021). scholar pointed out that successful ks processes enable firms to expand knowledge capital and exploit and convert all available resources into dynamic competences for improving firm performance (le, 2020; son et al., 2020; le, 2021). moreover, in case of supply chain, firms strive to achieve greater collaboration and effectiveness by leveraging their resources and knowledge through the process of knowledge creating and sharing among members in supply chain such as suppliers and customers (cao and zhang, 2011; jen et al., 2020; wang and hu, 2020). firms in supply chain tend to look outside their organizations for developing collaboration opportunities with partners to successfully innovate and to ensure that supply chain members are efficient and responsive to dynamic market needs (cao and zhang, 2011; nguyen et al., 2018). however, the relationship between ks in supply chain and firm performance seem to be ignored in the current literature (jen et al., 2020; li, 2020). to fill the research gaps and investigate potential effects of ks activities in supply chain on firm performance, the paper proposes the second research question as follows. rq2. do ks activities in supply chain significantly affect firm performance? third, leadership and ks are widely considered the strategic resources for firms to foster organizational performance (hassan and hatmaker, 2015; son et al., 2020; le and le, 2021). son et al. (2020) highlighted the decisive role of leadership in creating a positive influence on firm performance by establishing a ks climate among employees. in particular note, ks climate is found as a significant mediator between leadership/tl and key outcomes of an organization such as innovation performance (zheng et al., 2017; le and do, 2023), and organizational performance (son et al., 2020; le and le, 2021). however, there have been few studies investigating the mediating role of ks in supply chain especially in term of active and passive ks between tl and key organizational outcomes such as specific forms of firm performance (son et al., 2020; le, 2021). this limits firm’s understanding of the different ways according to which leaders can apply and follow to achieve specific goals of performance. to address this theoretical gap, third research question is proposed: rq3. do active and passive ks in supply chain mediate the effects of tl on firm’s operational and financial performance? to address the above research questions, this study will develop a research model to investigate tl’s impacts on firm’s operational performance and financial performance through the mediating role of active and passive ks activities in supply chain networks (see figure 1). this study will apply the structural equations modeling to examine the relationship among the latent factors in the proposal research model through a survey data of 235 firms in vietnam. the authors expect that, this study will provide valuable theoretical initiatives and specific practical guidance for directors/managers to improve the operational and financial performance in their firms. figure 1: proposal research model literature review influence of tl on firm performance transformational leadership is well known as one of the higher-ranking leadership styles (lei et al., 2017; le et al., 2018; cao and le, 2022). it describes the leaders who have capabilities of inspiring the employees to get the highest degrees of achievement and outcomes (le, 2020; phong and son, 2020; lathong et al., 2021). literature defined tl with four characteristics namely idealized influence, intellectual stimulation, inspirational motivation, and individualized consideration (bass, 1985; bass, 1990; le and lei, 2019; le et al., 2021; le et al., 2022). idealized influence reflects abilities of leaders to provide a vision and perception of mission, instilling pride, gaining respect and trust; intellectual stimulation involves leaders’ ability to promote intelligence, rationality and attentive problem-solving; inspirational motivation reflects leaders’ interest in communicating high expectations, using symbols to focus efforts, and expressing important purposes in simple ways; and individualized consideration refers leaders’ interest in coaching and advising, personal attention, and treating each employee individually. firm performance is understood in various meanings for different people since it contained many facets. according to madella et al. (2005), firm performance reflects firm’s capability in obtaining and handling properly organizational resources relating to human, finance and material to attain the organization’s targets. https://journals.e-palli.com/home/index.php/ajase pa ge 30 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 28-40, 2023 lee (2008) viewed firm performance as the results/ output of an organization that measured against its intended objectives. richard et al. (2009) defined firm performance comprising three aspects of organization’s outcomes namely financial performance, product market performance, and shareholder return. tsai and yen (2008) focus on financial and market performance to evaluate performance of an organization. the current study uses operational and financial performance to evaluate firm performance as they are the crucial constituents of organizational performance had critical impacts on organizational survival and competitiveness (wang et al., 2016; son et al., 2020). according to wang et al. (2016), operational performance reflects the fruit in managing cost, developing quality, achieving of customer satisfaction, responsiveness and productivity; while financial performance manifests the success of an organization in exerting its assets to bring about revenues that represented in its financial statements. current literature indicated that among different leadership style, tl plays a crucial role and servers as antecedents of key outcomes and firm performance (arif and akram, 2018; cao and le, 2022). prior studies noted that practicing tl is one of the best solutions to increase the firm performance at both individual and group levels (bass 1985; van et al., 2018; sengphet et al., 2019). many prior researches had explained for the positive relationship between tl and firm performance (e.g., judge and piccolo, 2004; arif and akram, 2018; son et al., 2020). prior research showed that firm performance is fostered by transformational leaders’ capabilities of motivating and inspiring individuals to work and attain outcomes beyond expectations (bass, 1985). they build systems with provision direction, vitality and enthusiasm to the organization, producing good chance for employees learning and innovating for boosting firm performance (tushman and nadler, 1986). judge and piccolo (2004) denoted that transformational leaders have positive relationship with job performance and organizational performance, they inspired followers toward the fulfillment of the desired result, with or without the rewards in line with the fruit. wang et al.’s (2011) meta-analytic study pointed out that tl is strongly and positively associated with firm performance. according to birasnav (2013), transformational leaders significantly predict effectiveness of supply chain management, and as a result, firms’ overall performance has been improved. in the same vein, son et al. (2020) stated that tl influences firm performance by promoting gradual contributions of followers through the striving efforts further than the call of obligation. their empirical findings show tl’s significant effects on operational and financial performance. based on above arguments, the following hypotheses are proposed: h1a: tl positively affect firm’s operational performance. h1b: tl positively affect firm’s financial performance. mediating effect of ks in supply chain between tl and firm performance knowledge is widely accepted as a crucial resource for firms to develop competitive advantages before the changes of environment (lei et al., 2019; lei et al., 2021; cao et al., 2022; ha et al., 2023). as a key component of knowledge management process, ks helps to maximize a firm’s ability to manage knowledge and allows employees in organization to work or achieve goals more efficiently (le and lei, 2017; yang et al., 2018; le and le, 2023). ks is defined as the process of interchanging data, information, know-how, and expertise among individuals to accomplish both personal and organizational goals (wang et al., 2016; yang et al., 2018; le and lei, 2019). this study uses the term of ks to describe the exchange of knowledge between organizations rather than individuals. according to this approach, ks in supply chain refers to processes of exchanging data, information, know-how, experience and new ideas among supply chain members to achieve the common goals. this study separates ks in supply chain into two sub-processes called active and passive ks. active ks reflects the voluntary and proactive degree of firms in communicating knowledge and information to supply chain members, whereas passive ks reflects the readiness level of firms to provide knowledge and information to those in supply chain in need or request it. we use this classification because ks in supply chain is a two-way process. in addition, active and passive ks commonly represent two divergent behavior tendencies of an organization toward ks activities. literature stresses the important and significant impact of leadership on ks processes (lei et al., 2019; nguyen et al., 2022; ha and le, 2023; phong and thanh, 2023). specifically, according to manafi and subramaniam (2015), transformational leaders can encourage ks processes by transforming employees’ positive attitudes and behaviors toward ks in organization. choi et al. (2016) and xiao et al. (2017) indicated that transformational leaders can create an appropriate climate for cultivating employees’ knowledge and skills, and encouraging them to share a lot of knowledge and expertise with the others like colleagues and partners in supply chain for common goals. lei et al. (2019) showed that under leadership by tl, employees are more willing to share their personal knowledge and expertise with others due to collaborative motivation for a common goal and the belief that leaders and colleagues are worth trusting. this is very important to develop a culture of sharing knowledge among individuals of organizations in supply chains. in particular note, scholars indicated significant influences of tl on competence-based trust of those they interact with (le and lei, 2018; lei et al., 2019; ha and le, 2021). as a result, competence-based trust developed by tl will supports ks process among members in supply chain by reducing information loss from sender to receiver (ajmal and kristianto, 2012; lei et al., 2019). recent studies also demonstrated that tl is one of the most appropriate leadership styles for creating an atmosphere of trust https://journals.e-palli.com/home/index.php/ajase pa ge 31 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 28-40, 2023 and developing the knowledge-centered culture which in turn significantly foster the willingness of individuals for sharing knowledge within an organization (lei et al., 2019; sheehan et al., 2020; yin et al., 2019; le et al., 2022) or sharing knowledge among supply chain members (nguyen et al., 2019; wang and hu, 2020). these arguments support the positive influence of tl on ks activities in supply chain, so the following hypotheses are posed: h2a.b: tl positively influences active and passive ks in supply chain members. with respect to the ks-firm performance relationship, despite lack of study has evaluated the direct effect of ks activities in supply chain on firm performance (samuel et al., 2011; sangari et al., 2015), prior studies have also shown the evidence supporting this relationship (hult et al., 2004; sangari et al., 2015; wang and hu, 2020). shaw et al. (2003) have realized that firm performance can be improved by coupling knowledge management initiatives with in supply chain networks and argued that firms must possess and share knowledge about different facets of the supply chain to achieve success. in the similar vain, hult et al. (2004) indicated that the knowledge acquisition activities result in reduced cycle time as a performance outcome at the supply chain level and the knowledge development process is an important antecedent to supply chain efficiency. jansen et al. (2006) argue that the exchange of knowledge and information helps firms avoid being constrained inside their knowledge boundaries, thereby creating opportunities for firms to renew knowledge and improve firm performance. according to sangari et al. (2015), realized that ks activities in process of knowledge management will enhance supply chain’s knowledge flows and ultimately will enhance supply chain performance. recently, wang and hu (2020) pointed out that ks process among members in supply chain facilitates the creation of new ideas and processes for improving the innovation performance. their empirical findings showed that ks process in supply chain networks is positively associated with innovation performance. based on the above discussion this study argued, the vital knowledge and information gained from the ks process in supply chain will help firms use their material, financial and other resources more effectively for improving operational and financial performance. so, following hypotheses are posed: h3a.b: active ks in supply chain significantly affects firm’s operational and financial performance. h3c.d: passive ks in supply chain significantly affects firm’s operational and financial performance. above argument supports the mediating roles of ks in supply chain by indicating that tl significantly influences ks in supply chain, which in turn positively affects firm performance. in addition, the current literature has also verified the mediating role of ks in the relationship between leadership and key organizational outcomes. for example, uddin et al. (2017) justified that effective leadership plays a significant role in promoting a supportive climate for exposing knowledge into organization innovation. these scholars found that tl significantly predicts firm’s innovation capability and performance via fostering the ks activities of individuals. zheng et al. (2017) argued that ks activities contribute significantly to innovation efforts and help ameliorate organizational performance at the firm level. their findings revealed that ks positively mediates the relationship between tl and project-based innovation performance. recently, ojha et al. (2018) pointed out that orientation of learning and ks in supply chain significantly mediates the relationship between tl and supply chain ambidexterity. although the mediating role of ks in supply chain between leadership and firm performance is supported, empirical research on relationship between ks among supply chain members and firm performance is still lacking. we, therefore, propose the hypotheses: h4a: ks in supply chain mediate tl effects on firm’s operational performance. h4b: ks in supply chain mediate tl effects on firm’s financial performance. methodology sample and data collection the paper used the data collected from august to november 2020 through a survey with the final sample size of 235 manufacturers and suppliers which are randomly selected from the initial list of more than 15,000 vietnamese enterprises published in 2018 by vietnam yellow pages. we then communicated with representatives of 800 firms by phone, e-mail and/or made personal visits in some cases to explain the purpose of our research and ask for their assistance and cooperation in gathering the data, among which 500 ones agreed to assist us in data collection. the respondents in this survey need to be managers or leaders of firms with supply chain collaboration experience. this study used measurement items that are utilized and developed from prior works. we released 500 question sheets and received back 356 ones, of which 121 responses were excluded from the sample because of missing data, and 235 are usable with the response rate of 47.0%. we used the armstrong and overton’s (1977) method to assess potential non-response bias. chi-square and independent sample t-tests were used to compare the first 80 respondents and the last 80 ones via demographic variables namely age and gender. the results demonstrated there were no significant differences between the two groups of responses (p > 0.05). variable measurement the paper used measures developed by previous studies to ensure the validity and reliability of the measurements. all items are measured via five-point likert-type scales ranging from “1” (strongly disagree) to “5” (strongly agree). transformational leadership this study used 8 items from le’s (2021) study to https://journals.e-palli.com/home/index.php/ajase pa ge 32 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 28-40, 2023 assess the perceptions of employees about the tl style of their direct supervisors. these items were also adopted in previous studies of tl in chinese context, one of the emerging countries (e.g., le and lei, 2019). a sample item is “my supervisor encourages me to think about problems from a new perspective”. knowledge sharing in supply chain. we used three items adapted from the study of le (2021) to measure active and passive ks in supply chain networks. this study then splits ks behaviors into two versions: “active ks” and “passive ks”. active ks was measured by three items reflecting the proactive level of supply chain members in sharing new knowledge and information that they have. a sample item is “we proactively share our new work reports and technical documents we have to other supply chain members”. passive was measured by three items reflecting the readiness level of supply chain members to provide knowledge and information to those request it. a sample item is “we are willing to share new work reports and technical documents to other supply chain members when they ask for it”. firm performance. this study used 11 items obtained from the son et al. (2020) study to evaluate firm’s operational and financial performance, where operational performance is assessed by five items that describe the firms’ successful degree in obtaining the quality development, customer satisfaction, responsiveness, productivity, and cost management. a sample item is “customer satisfaction of our firm is better than that of key competitors”. financial performance is assessed by six items to describe the firm’s capabilities in using its resources to result in revenues that exhibited in the financial statements of an organization. a sample item is “return on investment of our company is better than that of key competitors”. control variable. firm size serves as the control factor to explain for variations among organization and its potential effect on firm performance. data analysis methods this study utilized structural equation modeling (sem) to test proposal hypotheses in the research model by two reasons. first, sem method has been widely used due to its ability to demonstrate versatile regression correlations on a single model and test (kline, 2015). second, it is also proper and practical to identify interaction and mediation effects (lei et al., 2019). as a result, this study has used sem though amos software for the test of the structural model and hypotheses based on the data gathered from the 235 manufacturers and suppliers. data analysis was conducted using spss and amos version 22. results measurement model we first tested the reliability of the measures for the constructs by examining the private cronbach’s alpha coefficients (cα). the results of statistics are range of 0.93 0.97, which are all over than nunnally and bernstein’s (1994) recommended level of 0.7. we continuously analyze confirmatory factor (cfa) to evaluate the universal measurement model to check the discriminant and convergent validity. convergent validity as shown in table 1, all factor loadings are range of 0.698 0.997; cr values are range of 0.93 0.97; and the ave values are range of 0.73 0.94. according to hair et al.’s (2006) criteria, these measurements meet the criteria on convergent validity. table 1: standardize loading and reliabilities for measurement model construct item standardize loading t-value ave cr cα transformational leadership (tl) 8 0.76 0.96 0.96 tl1 0.839*** 17.9 tl2 0.891*** 20.5 tl3 0.875*** 25.8 tl4 0.857*** 18.7 tl5 0.870*** 19.4 tl6 0.895*** 20.7 tl7 0.888*** 20.3 tl8 0.864*** 19.1 active knowledge sharing (aks) 3 0.85 0.94 0.94 aks1 0.972*** 32.5 aks2 0.940*** 31.1 aks3 0.860*** 22.6 passive knowledge sharing (pks) 3 0.94 0.97 0.97 pks1 0.997*** 68.9 pks2 0.982*** 68.7 pks3 0.929*** 34.5 operational performance (op) 5 0.74 0.93 0.93 https://journals.e-palli.com/home/index.php/ajase pa ge 33 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 28-40, 2023 op1 0.858*** 19.9 op2 0.698*** 13.3 op3 0.918*** 23.7 op4 0.893*** 22.0 op5 0.920*** 23.9 financial performance (fp) 6 0.73 0.94 0.94 fp1 0.805*** 17.7 fp2 0.918*** 24.9 fp3 0.758*** 15.6 fp4 0.930*** 25.5 fp5 0.922*** 25.3 fp6 0.808*** 17.8 notes: cα ≥ 0.7; cr ≥ 0.7; ave ≥ 0.5; *** significant at p<0.001. discriminant validity discriminant validity refers to the level of the factors that assumed to assess a certain construct do not forecast conceptually independent criteria (kline, 2015). this paper applies the method of fornell and larcker (1981) relating to compare the ave’s square root with the correlations among the dormant constructs (see table 2). table 2 indicated that the ave’s square root of each table 2: ave’s square root and descriptive statistics from constructs constructs mean sd tl aks pks op fp tl 3.23 0.59 0.87 aks 3.67 0.62 0.54*** 0.92 pks 3.71 0.70 0.32*** 0.38*** 0.96 op 3.57 0.57 0.51*** 0.59*** 0.42*** 0.86 fp 3.71 0.61 0.54*** 0.62*** 0.43*** 0.63*** 0.85 note: diagonal components (in bold) are the ave's square root; off-diagonal components are the constructs' correlation coefficients. construct is higher than the correlation coefficients among variables of research model. overall, the above results show strong evidence for both the reliability of the constructs, and the discriminant validity of scales. regarding the satisfactory of measurement model, we estimated the fit of measurement model based on examining: (1) absolute fit values (such as gfi; cmin/ df, and rmsea); and (2) incremental fit values (such as nfi, agfi, and cfi). table 3 shows that all fit indices of the measurement model were satisfactory; thus, the model fit the data. table 3: the fit indices of the cfa model fit index scores proposal threshold values absolute fit measures cmin/df (chi-square/df) 1.629 ≤ 2a; ≤ 5b gfi (goodness of fit index) 0.877 ≥ 0.90a; ≥ 0.80b rmsea (root mean square error of approximation) 0.052 ≤ 0.08a; ≤ 0.10b incremental fit measures nfi (incremental fit measures including normed fit index) 0.941 ≥ 0.90a; agfi (adjusted goodness of fit index) 0.847 ≥ 0.90a; ≥ 0.80b cfi (comparative fit index) 0.976 ≥ 0.90a; notes: a: good fit; b: acceptable fit (schermelleh-engel et al., 2003; le and lei, 2019). structural model this study used structural equation model (sem) with maximum likelihood estimation procedures to test the proposal hypotheses. the fit indices of the structural model are satisfactory (χ2=463.05; df = 264; rmsea = 0.057; gfi = 0.867; cfi = 0.972; tli = 0.968), suggesting that the relationships among latent constructs fit the data. direct effect analysis the results in figure 2 and table 4 demonstrate that effects of independent variables on dependent ones https://journals.e-palli.com/home/index.php/ajase pa ge 34 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 28-40, 2023 figure 2: path coefficients of the structural model notes: ***p< 0.001; ----non-significant paths ensure statistically significant. hypotheses h1-h3 are, therefore, supported. specifically: hypothesis h1a.b relating to the effect of tl on aspects of firm performance. the results in figure 2 and table 4 revealed that tl’s influences on financial performance (β= 0.267; p < 0.001) is larger than its influence on operational performance (β= 0.246; p < 0.001). regarding hypothesis h2a.b, the results showed that tl’s table 4: results of the direct relationships hypothesis relationship beta standard error t-value results h1a tl -->operational performance 0.246*** 0.070 3.570 supported h1b tl --> financial performance 0.267*** 0.073 4.023 supported h2a tl --> active knowledge sharing 0.553*** 0.067 9.101 supported h2b tl --> passive knowledge sharing 0.335*** 0.078 5.203 supported h3a aks --> operational performance 0.395*** 0.061 5.984 supported h3b aks --> financial performance 0.417*** 0.063 6.568 supported h3c pks --> operational performance 0.202*** 0.047 3.614 supported h3d pks --> financial performance 0.194*** 0.048 3.619 supported notes: ***significant at the 0.001 level. impact on aspects of ks activities in supply chain is very considerable. the findings showed significant influences of tl on active ks activity (β= 0.553; p < 0.001) is more significant than its effect on passive ks activity (β = 0.335; p < 0.001) of firms in supply chain networks. for hypotheses h3a.b and h3c.d, the results showed that, active ks has greater impacts on both operational and financial performance compared with the effect of passive ks. specifically, the influences of active ks on operational performance (β= 0.395; p < 0.001) and financial performance (β= 0.417; p < 0.001) are statistically significant. similarly, the impacts of passive ks on operational performance (β= 0.202; p < 0.001) and financial performance (β= 0.194; p < 0.001) are also statistically significant and supported. the hypotheses assessments’ results are attained after investigating the effects of control variable of firm size. the findings did not support the control role of firm size because its effects on operational and financial performance are not statistically significant. indirect and total effect analysis to provide evidence on the mediating roles of active and passive ks in supply chain networks between tl and specific aspects of firm performance namely operational and financial performance, this study used the bootstrap confidence intervals method with 5,000 iterations as the suggestion of preacher and hayes (2008), to test the significance of indirect effects (see table 5). the results in table 5 pointed out that the indirect effect of tl on operational performance (β= 0.286; p < 0.001) and financial performance (β= 0.295; p < 0.001) are significant within the range of confidence intervals. in general, these findings provide the evidence to confirm the mediating role of ks activities in supply chain between tl and firm performance. https://journals.e-palli.com/home/index.php/ajase pa ge 35 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 28-40, 2023 discussion and implications supply chain management and ks practice represent alternative approaches that have generated a lot of interests among scholars and practitioners. scholars and practitioners considered have made great efforts to understand and identify how leadership practice can facilitate ks activities for fostering firm performance. it is unfortunately there is little guidance in the extant literature on how tl and ks activities among supply chain members affect specific aspects of firm performance namely operational and financial performance (wang and wang, 2012; sangari et al., 2015; arif and akram, 2018). accordingly, by investigating the mediating role of ks activities in supply chain networks between tl and aspects of firm performance, the findings of this study significantly contribute to developing and advancing theory of leadership, knowledge management and performance management by some following important reasons. first, the paper significantly contributes to filling the theoretical gaps and increasing the understanding of tl’s effects on specific aspects of ks activities in supply chain networks. indeed, knowledge is a pivotal resource of organization that bring firms a sustainable competitive advantage to survive before the increasingly changes of business environment and fierce competition (lei et al., 2019; son and phong, 2023; than et al., 2023). many organizations have invested the huge time, efforts and money for improving their organizational knowledge capital through enhancing ks activities in their firm. however, they still fail to share knowledge and losing billions of dollars each year (babcock, 2004; le and tran, 2020). an important reason for the failure of improving ks is the lack of understanding of how leadership styles or specific leadership characteristics influence ks (wang and noe, 2010; le and tran, 2020), especially in the context of supply chain networks (birasnav, 2013; ojha et al., 2018). to address these theoretical gaps, the paper has examined the effects of tl on active and passive ks activities of supply chain members. the findings have underlined the tl’s essential role in exhorting activity of active ks in comparison with passive ks activity. the findings reveal that the positive effects of transformational leaders have prompted and encouraged employees to voluntary and proactively share their new knowledge and information with co-workers in supply chain networks for creating greater benefit and achieving their common goals. second, active and passive ks represent two different forms of ks activities of organizations in supply chain networks. by investigating the effects of active and passive ks on aspects of firm performance, the paper has contributed to the expanse and arousal the new ideas of improving firm performance. the findings show that, active and passive ks act as the significant predictors of operational and financial performance. the findings are consistent with iqbal et al.’s (2019) findings on critical role of ks and knowledge management processes in improving firm performance. the paper has indicated that active ks has greater impacts on two aspects of firm performance in comparison with influence of passive ks. in other words, these findings emphasize the critical role of active ks activities in supply chain networks, and consider active ks activities as the main solution to foster firm performance. so, posing the right investment and great efforts on stimulating active ks among members in supply chain networks is right and possible solution for directors and managers to effectively improve firm performance. moreover, transformational leaders also act as a driving force of nurturing firm performance. the findings of this study disclose the greater influence of tl on financial performance in comparison with tl’s effect on operational performance. from these findings, the paper implies that focusing on practicing the tl style might help directors/managers to follow and attain better financial goals in term of the return on investment and sales, the growth of profit and sales, and the average profitability. third, previous studies have shown the positive influences of tl and ks activities on some particular spheres of firm performance such as operational and financial performance (ojha et al., 2018; son et al., 2020). however, there still exists a research gap in the literature that helps to explain potential mediating role of ks processes in supply chain networks in the relationship between tl and firm performance in supply chain (wong and wong, 2011; birasnav, 2013; ojha et al., 2018; son et al., 2020). this limits our understanding of the mechanism by which tl can interact with ks activities in supply chain to produce significant influences on firm performance in certain forms. as a result, this paper contributes significantly to advancing the theory of leadership and performance management by evaluating the mediating effect of active and passive ks activities among supply chain members in the relationship between tl and two specific types of firm performance. the empirical findings have affirmed the mediators of active and passive ks in supply chain networks and spotlighted table 5: confidence intervals of the indirect effects hypothesis path direct effects indirect effects total effects bias-corrected confidence intervals lower confidence level upper confidence level h4a tl-->ks-->op 0.246*** 0.286*** 0.532*** 0.225 0.367 h4b tl-->ks-->fp 0.267*** 0.295*** 0.562*** 0.235 0.376 note: *** p < 0.001. https://journals.e-palli.com/home/index.php/ajase pa ge 36 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 28-40, 2023 that tl practices can significantly affect operational and financial performance directly or indirectly by stimulating active and passive ks activities among supply chain members. from these findings, the paper might serve as the valuable reference that provides valuable insights into the needful conditions and the new pathway to promote firm’s operational and financial performance. finally, prior studies argued that in the context of developing and emerging countries like vietnam, vietnamese firms are still facing with many difficulties and quite sensitive to changes in technology and innovation (nguyen et al., 2019; son et al., 2020; lei et al., 2020). the majority of firms in developing countries like vietnam are small and medium size, account for approximately 98.1%, and lack of capital, resources, and r&d capabilities to innovate for improving firm performance (lei et al., 2019; son et al., 2019). thus, improving firm performance by huge investments in technological innovation or physical resources is not feasible (than et al., 2021; than et al., 2023; tran et al., 2023). leadership and knowledge resource of supply chain members are well known as the main drive of innovation performance and organizational performance (birasnav, 2013; ojha et al., 2018; sengphet et al., 2019; son et al., 2020). the findings of this paper have, therefore, implied that focusing on tl practice to stimulate knowledge and intellectual resource of firms in supply chain networks seems to be one of the most optimal and effective strategies for firms in developing countries to follow operational and financial performance. recommendation and research limitation this study has also inevitably limitations. first, the paper employs cross-sectional design this may arise the circumstance according to which causal correlations might fluctuate in the long-run due to ks activities of firms in supply chain networks may change over time. a longitudinal investigation will assist to control this limitation and affirm the result. second, this study examined the correlation based on self-report data. this may lead to the limitation of common method bias or single source bias. future research should test the relationship among the constructs, especially in term of measuring firm performance based on objective data to consolidate the findings. third, the results and the benchmarks in this paper are more appropriate for the context of vietnamese firms. future research should implement in other circumstances to show clearer picture/implication in terms of the correlation among these factors. finally, active and passive ks activities are found to have significant impacts on firm performance. so, it is needed to perform further studies in future for exposing deeper the effects of tl and active and passive ks in supply chain on the other strategic components of firm performance such as innovation performance and market performance or key outcome of firms such as innovation capability and competitive advantage. conclusion generally, this study significantly contributes to filling the gaps on the relationship between tl, ks behaviors and organizational performance. especially, the paper has enhanced understanding and pushed the theory of leadership and organizational behaviors forward by clarifying the mediating roles of ks processes in linking tl and specific forms of organizational performance namely operational and financial performance. reference ajmal, m. m., & kristianto, y. 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(2020). transformational leadership and employee knowledge sharing: explore the mediating roles of psychological safety and team efficacy. journal of knowledge management, 24(2), 150-171.https://doi.org/10.1108/ jkm-12-2018-0776. https://journals.e-palli.com/home/index.php/ajase pa ge 1 pa ge 44 american journal of applied statistics and economics (ajase) effect of sorting and arrangement of patient health records in health records department of cottage hospitals bojude and malam sidi in kwami l.g.a. gombe state, nigeria usman m.1*, sani a.1, alhassan m.1, auwal r. h.1 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2002 https://journals.e-palli.com/home/index.php/ajase article information abstract received: november 29, 2023 accepted: january 01, 2024 published: january 03, 2024 the study focused on the effect of sorting and arrangement of patient health records in the health records department of cottage hospitals bojude and malam sidi gombe state, nigeria. the study population for this research work comprised health record officers/ managers, health records technicians, and health records assistants working in the two health records departments of bojude and malam sidi. the study adopted a descriptive survey design to reduce the effects of sorting and arranging patients’ health records in the selected hospital. the population comprised one hundred (100) respondents with a selfdesigned structured questionnaire and was validated to establish its reliability; the researcher personally administered the questionnaire. data collected were analyzed using epi-info software (epidemiology information) version 3.5, and the results were presented in simple analysis form to reveal the respondents’ views based on objectives. the study found that sorting and arranging patients’ health records negatively affect patients and hospitals. moreover, all health institutions should be mandated to employ qualified and trained health information personnel to maintain the ethics of the health information management department so that their knowledge in managing patients’ health records will assist in reducing the error of miss sorting and arrangement of patient health records. also, the management of the hospitals should provide manpower, meaning trained medical records personnel, enough space, and adequate carbonate shape and lightening for proper filing equipment and suitable filing for the health records department to reduce the misfiling of patients’ health records. keywords arrangement, health, information, record, patient 1 gombe state college of health science and technology kaltungo, nigeria * corresponding author’s e-mail: othman1064@gmail.com introduction the health information management department in each hospital is the backbone of the facility and one of the most important works that health information managers normally do is to sort patient case notes/folders in the hospital at every central library for filling. however, some other hospital fails to achieve their goals through the improper sorting and arrangement of patient case folders because they lack trained personnel (aremu, 1999). this research work is going to figure out the problem in the hospital, we all know that if memory forgets the record will remember things that have happened over centuries so the health information management department is in charge of the records or backbone in the hospital, and is very important to employed qualified staff that will handle the record, so that will last for so many years. in this case, we will look into the brief history of the health information management department and the heroes that contribute to the work. the origin of the health information management department began some decades back, in the past twenty-nine century bc the record was established on the patient bed and the person who started the record was thoth. this was done to continue the treatment of patients; this great man was an egyptian and he lives for sixty-one (61) years with his greatness he sorts out the patient record and arranges them on each patient’s bed. sorting and arrangement of patient case folders have been a very huge work that the health information management department has to battle with and the staff posted on this unit must be serious to avoid lost or missing patient case folders (huffman, 1994). color-code your filing system visual markers, such as colored tabs, can save you time when browsing for documents. you can use different colored folders for your various types and subtypes of documents. some folders include label tabs in various colors. with either of these options, you can create a color-coded key to keep track of the color (lippincott & wilkins, 2006). assigned to the document category labeling your document categories can help you quickly identify your intended folder. some folders come with paper to make your labels. you can either hand-write the labels or print out a sheet of typed labels all at once. you can further optimize your labeling system by using different colored pens or ink that match your color-coded key. highlighters can also be used on black text to color code the label. label makers can also allow you to quickly print out single-label stickers. this tool is especially helpful when labeling a filing shelf or cabinet, or if you need to replace current labels with new ones. pa ge 45 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 44-50, 2024 dispose of unnecessary documents creating an organized filing system can help you find ways to reduce the amount of paperwork you store. recycle no longer relevant documents to reduce clutter, or shred them for extra security. consider placing a small recycling can or a dedicated basket for documents you want to shred near your desk. regularly disposing of unnecessary files and documents can help keep your workspace. statement of the problem the main purpose of this research is to find out whether the hospital employed qualified staff for the department of health information management and how the staff sorted out the patient’s case folders and also arranged them to avoid miss filling folders in the cottage hospital bojude and cottage hospital malam sidi. this research work has been carried out by a person who has been urged to research the topic of the sorting and arrangement of patient case folders toward better health information management practice in cottage hospital bojude, gombe state. despite what the government is doing to the hospital, most of the staff and the hospital management are not stable with their work. this has resulted in many medical errors that have led to death and some medical problems. this research work has vast importance to the hospital but looking at the issue critically the hospital needs to know the patient confidentiality. now the question is do the hospital maintain good confidentiality? if not, what will be the problem? how will this problem be tackled in what way will this project be useful to the hospital? these are some of the issues the research will consider throughout. the objective of the study i. to find out whether the two hospitals employed the right personnel at cottage hospital or not. ii. to find out whether the health information management department staff knows how to sort and arrange patient case folders at cottage hospitals. and; iii. to know the method of sorting and arrangement of patient case folders in cottage hospital. research questions i. does the hospital have qualified staff? ii. do the two cottage hospital staff know the sorting and arrangement of patients’ case folders? iii. does the health information management department apply the method of sorting and arranging patient case folders at the him department? significances of the study this research work is very important to the health information management department in general hospital kaltungo and other health professionals and to patients because the recommendation of this study may guide the hospital at large as well as the people who come in contact with the hospital care most especially those who come for follow up the case. when patients’ case folders are arranged precisely and also kept or filled in a prescribed manna according to the hospital will not cause stress to both the patient and the staff. there will be no errors or missing patient case folders. this research work is very important to the health information management department in the general hospital bojude and malam sidi’s recommendation of this study may guide the department of health information management and the hospital at large as well as the ministry of health and the community. the sorting and arrangement of patient case folders toward better health information management practice has been the most important work that health information managers are battling with in every hospital both private and public sector this work was found to be stressful to those staff posted in the unit and has found to be the first stage taking for filling of the patient case note. sorting and arrangement of patient case folder are important because they try to figure out the missing folders or doubling hospital number and make sure is corrected before being filled into the shelves or cabinet together with the tracer card. definition of terms the terms are defined for the research work and they are used throughout the research work. health is defined as the physical social and mental well-being of an individual without mainly the absence of disease or infirmity. information means the data that has been processed sorting means the act of picking up something or patient case folders to fill arrangement means placing something in a prescribed manna according to the hospital or is the act of placing a patient’s case folders in a prescribed manna for filling. management means to maintain the available things that you have, i.e., organized, controlled, and leading. people or resources and a particular board e.g., the management board of the health record office. patient means anybody who is seeking medical attention. folders means the booklet that is given to a patient in the hospital most especially when his/she is admitted to the hospital. pa ge 46 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 44-50, 2024 record can be defined as the creation of information received and maintained as evidence-based, information by organizations or persons in pursuance of legal obligations or the transaction of business content (i.e., information data). medical records means the data of a patient’s e.g., examination, treatment, and other health records of the patient. the terms medical record, health record, and medical chart are used somewhat interchangeably to describe the systematic documentation of a single patient’s medical history and care across time within one particular health care provider. health information management health information management (him) is information management applied to health and health care. it is the practice of analyzing and protecting digital and traditional medical information vital to providing quality patient care. review of literature this chapter focuses on the topic such as the explanation of the topic and the people who also talk about the topic or related topics with their books or journals (related literature) author’s name book published year of publication. this topic also captures some importance of sorting and arrangement of patient case folders and the materials used in sorting and arrangement of patient case folders with the appropriate method. conceptual issues the concept of sorting and arrangement of patient case folders in the hospital the sorting and arrangement of patient case folder toward better health information management practice have been the most important work that health information managers are battling with in every hospital both private and public sector this work was found to be stressful to those staff posted in the unit and has found to be the first stage taking for filling patient case note. sorting and arrangement of patient case folder are important because it’s tried to figure out the missing folders or doubling hospital number and make sure is corrected before being filled into the shelves or cabinet together with the tracer card. module for sorting patient case folder if a numerical record-identified system is used, then numerical sorting is also done, there are two main systems of sorting the patient record and arrangement accordingly: a. straight numerical sorting and arrangement of patient case folder and; b. terminal digits of sorting and arrangement of patient case folder. this method reflects the exact chronological order of the creation of records and the importance of proper sorting and arrangement of patient case folders for quality filling the proper sorting and arrangement of patient case folders led to good filling, and the proper filling of patient medical records ensure easy retrieval and contribute to decreasing patient waiting time at the hospital and ensures the continuity of care. health information and evidence health information as evidence is one of the critical blocks of the health system strengthening. health information systems cannot be ignored since health policies and planning in any country are mostly dependent on correct and timely information on various health issues. health facilities throughout the world keep consistent records of patients/clients. these medical records are normally kept confidential and in confined places such as record units or offices. this medical record is a chronologically written account of a patient’s examination and treatment that includes the patient’s medical history and complaints, the physician’s physical findings, the results of the diagnostics test and procedures, and medications and therapeutic procedures (murphy et al., 1999) organizing and storing your medical record there are different ways to organize your medical records. to help figure out what works best for you, talk to other cancer survivors about what they have done, or visit a local office supply store to see what sort of organizers are available. here are a few options: use a filing cabinet, 3-ring binder, or desktop divider with individual folders store files on a computer, where you can scan and save documents or type up notes from an appointment store, record online using an e-health tool; certain online records tools may be accessed, with permission by doctors or family members organize your records by date or by categories, such as treatments, tests, doctors’ appointment, etc. (osundina, 2004). how to organize documents the following steps can guide you in sorting, categorizing, and storing your physical paperwork and help you design an effective filing system: separate documents by type: 1. use chronological and alphabetical order. 2. organize the filing space. 3. color-code your filing system. 4. label your filing system. 5. dispose of unnecessary documents. the situation of medical records filling as attendance in healthcare facilities increases with time, the volume of medical records becomes a big challenge to health facility management. this is no different in ghana. of late most of our public health facilities are confronted with filling out medical records, especially with the introduction of the national health insurance scheme which brought in its wake the introduction of new folders and nhis identification cards (id cards). anecdotal evidence from most public health facilities pa ge 47 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 44-50, 2024 indicates that facilities are saddled with old and new folders for outpatient and inpatient departments as well as a sizeable number of patients having more than two folders due to mainly to filling or forgetting their id cards when visiting the facility. in 2010 the school of public health, university of ghana, during an annual summer short course in improving management for public health interventions (imphi), several district health directors identified missing filling and multiple patient folders as major problems in their facilities. this paper reports the result of an intervention in the medical records filling system of a municipal hospital in ghana. method of filling system centralization method of filling system this method the patient health records are maintained at the central library or the record is maintained in one unit. decentralized method of filling system this method of filling system the patient health records are maintained by the various units or clinics in the hospital. causes of miss filling 1. poor handwriting 2. heavy workload 3. lack of trained medical record personnel 4. lack of a conducive environment 5. inadequate electricity commentaries and systematic reviews of the study indicate concerns about how technology affects caring, both positively and negatively. while there are many controversies on how to interpret these measurements exist. (ahima, 2003) and (aremu, 1999) state that caring cannot be operationalized and therefore quantitative studies are not suitable, while (ayilegbe, 2008) states that it can be operationalized and so quantitative methods are appropriate. information technology scales for health record many instruments have been used to assess health information managers’ attitudes toward technology, however, they were noted to be inconsistent with the results of studies and/or did not report reliability or validity (ayilegbe, 2015). quality and health information technology in the year 2000, the institute of medicine (iom) released a report focusing on patient safety estimating that 44,000 to 98,000 people die in u.s. hospitals annually as a result of medical errors. many of these errors involve medications in a subsequent report, the iom identified it as one of the four critical forces that could significantly improve healthcare quality and safety (ahima, 2003). perception on sharing of anonymized health records, perhaps 10 except for more recent studies that examine patients’ perception about consent to health information used for other than their care (benjamin, 1980). in connection to this, (geoffrey, 1999) investigate the divergence of perception among patients toward different types of personal health record (phr) systems, including paper-based, personal computer-based, memory devices, portal and networked phr, which are in the increasing order of technology advancement (fmoh, 1996). methodology research design a descriptive survey is used for the study. this research work is designed to provide systematic information about a phenomenon. research population the target population used in this study was the total number of staff working at the cottage hospital bojude and malam sidi. who were found to be 150 workers in the research sample the health record personnel and other health professionals from different units in soliciting information? the staff from all units in respect to the hospital. sample size and sampling techniques one hundred (100) respondents were selected from the two-cottage hospital, and sharing fifty in each hospital questionnaires were distributed to them. the information obtained was based on the roles of documentation, sorting, and arrangement in health care services at record department sampling techniques (simple random sampling) used in selecting the respondents for this research. study area the cottage hospital bojude and cottage hospital malam sidi in kwami local government area in gombe state of nigeria. its headquarters are in the town of mallam sidi, kwami is bounded in the east by lake dadin kowa. the postal code of the area is 760. the population of kwami in the 2006 census is 195,298. the study area is cottage hospital bojude and cottage hospital malam sidi in gombe state, which was established as the facility that renders medical services in both areas of the state and the country at large. the two cottage hospitals were established in 2007 by the state government it renders medical services in the state and the nation, and each hospital has a fifty (50) bed capacity, the hospital renders the general medical and surgical hospitals, health care, and social assistance, child day care services, community food and housing, and emergency and other relief services, continuing care retirement community and assisted living facilities for the elderly, home health care services, individual and family services, medical and diagnostics laboratories, nursing care facilities (skilled nursing facilities). geographical location kwami lga has a total area of 1,787 square kilometres pa ge 48 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 44-50, 2024 and lies on the banks of lake dadinkowa, the area witnesses two major seasons which are the dry and the rainy seasons. the average temperature in kwami lga is 32 °c. instruments for data collection the instrument used for this study is a questionnaire. a structured questionnaire used for data collection is divided into two sections: 1. section ‘’a’’ personal data 2. section ‘’b’’ the factors that affect the sorting and arrangement of patient case folders. method of data analysis the researcher used a questionnaire for easy and capturing information, the data collection for this study was analyzed by using the frequency distribution table and percentage scale. results and discussions this table shows that male is the highest number of respondents with 65% while female has 25%. table two shows that workers with an age of 2640 have the highest percentage which is 56% and the others have the lowest percentage this shows that those with the age of 2640 are the highest respondents. table three shows that married ones have the highest response which is 45% and the singles have 39%while the widows and divorce have 16%. table four shows that the highest number of respondents are those with a national diploma with 45% and 40%from a professional diploma then 15% those with other degrees. table five show that the hospital lack qualified health information managers no has the highest percentage which is 65% and yes has 35%. table six shows that the staff all know the method of sorting and arrangement of patient case folders yes has the highest percentage which is 70% and no, has 30%. table 1: gender of the respondent? respondents frequency percentage% male 65 65 female 35 35 total 100 100 source: field survey, 2022 table 2: age of the respondents? respondents frequency percentage% 18-25 34 34 26-40 56 56 41-60 10 10 total 100 100 source: field survey, 2022 table 3: marital status of the respondents? respondents frequency percentage% single 39 39 married 45 45 widow and divorce 16 16 total 100 100 source: field survey, 2022 table 4: educational qualification of the respondents? respondents frequency percentage% health assistance 45 45 professional diploma 40 40 other degrees 15 15 total 100 100 source: field survey, 2022 table 5: does cottage hospital have qualified health information managers? respondents frequency percentage% yes 35 35 no 65 65 total 100 100 source: field survey, 2022 table 6: are the staff aware of the methods of sorting and arrangement of the patient case folders? respondents frequency percentage% yes 70 70 no 30 30 total 100 100 source: field survey, 2022 table seven shows that the hospital is not organizing a seminar or workshop for their staff for more training no has the highest percentage which is 60% while yes is 40%. table eight shows that the hospital doesn’t provide a conducive environment for health information managers no has the highest percentage which is 70% while yes has 30%. table 7: does the hospital organize seminars and workshops for more training for their staff ? respondents frequency percentage% yes 40 40 no 60 60 total 100 100 source: field survey, 2022 table 8: does the hospital provide a conducive environment for the health information department? respondents frequency percentage% yes 30 30% no 70 70% total 100 100 % source: field survey, 2022 pa ge 49 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 44-50, 2024 table nine shows that the hospital assists the department at any stage because yes has the highest percentage which is 80% while no is 20%. table nine shows that the hospital assists the department at any stage because yes has the highest percentage which is 80% while no is 20%. this study provides information about sorting and arranging patient case folders and proper filling, filling situations of medical records filling, and types of filling systems. the instrument used where the self-administered questionnaire was typed and printed out one hundred (100) copies of the questionnaire were administered to the staff that is working in bojude and malam sidi cottage hospital. the data collected for this study were grouped according to the research from the various items asked in the questionnaire and analyzed by using descriptive statistics. conclusion the result of the study revealed that missing sorting out an arrangement of patient health records will have negative effects on patients and hospitals, the hospital management should find ways to introduce seminars and workshops for their staff for more training. they should be strong laws guiding the patient case folders to avoid miss sorting, arrangement and miss filling, and other complicating issues in the health information management department. government should employ qualified health professionals that will run the activities of the medical records unit without any challenges or problems. the government should provide a conducive environment for the health information department and hospital as large. recommendations given the significant and negative effects that miss sorting and arrangement of patient health records have on patients and hospitals, the following recommendations are hereby made for both two hospitals: 1. all health institutions should be mandated to employ qualified and trained health information managers to manage the department of health information management so that their knowledge in the management of patient health records will assist in reducing miss sorting and arrangement of patient health records. 2. the management of the hospitals should be informed of their responsibilities in providing trained personnel, adequate filing equipment, and a suitable filing environment for the health records department because the above-mentioned factors contribute to the miss sorting and arrangement of patient health records in the two health institutions. 3. health information managers should maintain a high level of decorum and concentration when filing patients’ records in the health records library. 4. a good tracer system should be put in place by health records officers to track the movements of patients’ case notes in the hospital. 5. patients’ health records should be computerized to aid quick and timely retrieval of patients’ information. references ahima. (2003). the complete medical records in a hybrid disclosure. practice brief. retrieved from www.ohima.org. table 9: is there any way that the hospital assists the department of health information management? respondents frequency percentage% yes 80 80 no 20 20 total 100 100 source: field survey, 2022 table 10: does the hospital encounter any problems in sorting out patient case folders? respondents frequency percentage% yes 45 45% no 55 55% total 100 100 % source: field survey, 2022 discussion at this point, the researcher has categorically stated the results and findings gotten from the respondents as it has been interpreted above. therefore, the researcher obtained information through a questionnaire administered to the cottage hospitals bojude and malam sidi. table one shows that male has the highest percentage of 65% while female has 35%, table two shows that those aged 2-40 have the highest percentage which is 56%, while those with age 18-20 have 24% and those 41-60 have 10%, table three shows that the married ones are the highest respondents with 45% while the singles have 39%, and widows and divorce have only 16%, table four shows that those with national diploma have the highest frequency and percentage of 45% and professional diploma is 40%, those with bsc has 15%. the hospital doesn’t have qualified health information managers, because, according to the respondents, it shows yes has 35% and those answering no has 65%. this shows that the department of health information management lacks qualified staff. and also, the hospital doesn’t have a conducive environment for the health information management department according to the response. the researcher found a load of problems encountered while sorting out and arranging patient case folders. the hospital, don’t organize seminar or workshops for the staff. summary the research was conducted on “assessment of the sorting and arrangement of patient’s case folders towards better health information management practice at cottage hospital bojude and cottage hospital malam sidi, gombe state. pa ge 50 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 44-50, 2024 aremu, h. b. (1999). health records management 1 & 11 ilorin: decency printers & sanitation ltd. ayilegbe, b. k (2008). the essence of documentation in health care institution, kano: debisco printing press. ayilegbe, b. k. (2015). the dynamics of patients’ discharge summaries. kano: debisco printing press. benjamin, b. (1980). medical records. edinburgh: churchill livingstone. federal ministry of health (1996). national health information management information system (health facility summary forms). lagos: fmoh publication. geoffrey, y. (1999). managing hospital records. london: international record management trust. huffman, e. k. (1994). health information management (10th edition). berwyn: physicians’ record company. lippincott, w, and wilkins. (2006). steadman’s medical dictionary. baltimore: a wolters kluwer health company. murphy, g., mary alice, h., and kathleen, w. (1999). electronic health records: changing the vision. w. b. saunders company. osundina, k.s. (2004). principles and practice of health records management. ilesa: k. s. osundina publications. pa ge 1 pa ge 13 6 american journal of applied statistics and economics (ajase) modeling a time study of the united states consumer price index from 2014 to 2023 paul o. oyinloye1* volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.3551 https://journals.e-palli.com/home/index.php/ajase article information abstract received: august 28, 2024 accepted: september 26, 2024 published: september 30, 2024 this study presents a comprehensive analysis of the u.s. consumer price index (cpi) over a ten-year period 2014 to 2023. the cpi, a critical measure of inflation, reflects the average change in prices paid by urban consumers for a market basket of goods and services. this study will explore the intricate relationships between cpi and key economic variables, including unemployment levels, interest rates, and the sales index, using time series data and multiple regression analysis. the research identifies significant predictors of cpi and evaluates the extent to which these factors influence price levels in the u.s. economy. the findings reveal a strong positive correlation between cpi and interest rates, indicating that as borrowing costs increase, the cpi tends to rise correspondingly. conversely, a negative relationship is observed between cpi and unemployment levels, suggesting that higher unemployment rates are associated with a decrease in cpi, possibly due to reduced consumer spending. the study also examines the sales index, finding a weaker yet notable relationship with cpi, which underscores the complex dynamics of consumer behavior and price levels. the analysis highlights the limitations of cpi as a comprehensive measure of the cost of living, pointing out its lack of significant correlation with real income and population growth. the study concludes that while cpi is an essential tool for measuring inflation and informing monetary policy, it may not fully capture the economic realities faced by the population. these insights suggest the need for policymakers to consider additional factors when using cpi to make informed decisions about economic policy and social welfare programs among the u.s. population. keywords consumer price index, economic indicators, inflation, interest rates, sales index, unemployment 1 babcock university, nigeria * corresponding author’s e-mail: innocentoasev@yahoo.com introduction the consumer price index (cpi) is widely recognized as one of the most important indicators of economic health, particularly as it pertains to inflation. cpi reflects the average change over time in the prices paid by urban consumers for a basket of goods and services. this index is crucial for understanding the purchasing power of consumers, as well as the overall cost of living. however, despite its importance, cpi is often criticized for its limitations in capturing the complete picture of economic well-being. this study aims to delve deeper into the factors that influence cpi, specifically focusing on unemployment levels, interest rates, and the sales index, during the period from january 2014 to december 2023. background of the study the consumer price index is a vital economic measure used by policymakers, economists, and businesses to gauge inflation and assess the economic environment. it tracks the prices of a representative basket of goods and services over time, offering a snapshot of inflationary trends. this index is instrumental in adjusting wages, pensions, and tax brackets to maintain purchasing power and ensure economic stability. despite its widespread use, cpi has faced criticism for not fully capturing the cost of living, as it may not account for changes in consumer behavior, product quality, or new goods entering the market. this study explores the relationship between cpi and three key economic variables: unemployment level, interest rates, and the sales index. unemployment levels can significantly impact consumer spending patterns, as higher unemployment typically leads to reduced disposable income and lower demand for goods and services. interest rates, set by the federal reserve, influence borrowing costs for consumers and businesses, thereby affecting spending and investment decisions. the sales index, representing the overall level of retail sales, provides insight into consumer confidence and economic activity. by analyzing data from january 2014 to december 2023, this study seeks to uncover the extent to which these factors drive changes in cpi and contribute to a more nuanced understanding of inflation dynamics. statement of the problem understanding the causes of changes in commodity prices over time is a complex and multifaceted challenge. while cpi provides a broad measure of inflation, it does not always correlate perfectly with the lived experiences of consumers. many individuals and households in the united states continue to face economic difficulties, even when cpi data suggests that inflation is under control. this discrepancy raises important questions about the adequacy of cpi as a measure of economic well-being and whether it accurately reflects the diverse and changing conditions within the u.s. economy. one of the critical issues is the potential for cpi to overlook significant economic factors that contribute to the overall cost of living. for example, shifts in pa ge 13 7 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 136-144, 2024 unemployment can lead to reduced consumer spending, which may not be fully captured by cpi if it primarily tracks prices rather than purchasing patterns. similarly, changes in interest rates can affect consumer borrowing and spending, influencing inflationary pressures in ways that cpi might not immediately reflect. the sales index, as a measure of retail activity, can also provide valuable insights into consumer behavior that are not directly observable through cpi alone. given these complexities, there is a pressing need for a more refined understanding of the factors that influence cpi and how it interacts with other economic variables. this study seeks to address this gap by investigating the relationships between cpi, unemployment levels, interest rates, and the sales index. the goal is to develop a predictive model that can more accurately reflect the dynamics of inflation and provide a better tool for economic analysis and policymaking. objectives of the study the primary objective of this study is to develop a robust predictive model that explains the influence of unemployment levels, interest rates, and the sales index on the consumer price index. by achieving this goal, the study aims to enhance the understanding of how these variables interact to drive inflation and to identify the most significant predictors of changes in cpi. the specific objectives include: analyze time series data to produce time series data for the selected variables and compute descriptive statistics to understand their distribution and trends over the period from january 2014 to december 2024. examine relationships to analyze the relationships between cpi and the explanatory variables—unemployment levels, interest rates, and the sales index—using multiple regression analysis and other statistical techniques. develop predictive model to develop a predictive model that accurately reflects the influence of these variables on cpi, allowing for more precise forecasting of inflationary trends. evaluate model performance to evaluate the performance of the model using statistical tests for goodness-of-fit, multicollinearity, and heteroscedasticity, ensuring the robustness and reliability of the findings. provide policy insights to provide insights for policymakers on how changes in unemployment levels, interest rates, and retail activity might affect inflation and economic stability, potentially informing more effective economic policies. hypotheses in pursuit of the study’s objectives, the following null hypotheses are proposed for testing: there is no significant relationship between cpi and unemployment level this hypothesis suggests that changes in unemployment levels do not have a statistically significant effect on cpi. testing this hypothesis will help determine whether fluctuations in employment rates are an important factor in predicting inflation. there is no significant relationship between cpi and sales index this hypothesis posits that the sales index, which measures retail sales activity, does not significantly influence cpi. testing this relationship will provide insights into how consumer spending and retail activity impact inflationary pressures. there is no significant relationship between cpi and interest rates this hypothesis asserts that changes in interest rates do not have a significant effect on cpi. given the role of interest rates in shaping borrowing costs and investment decisions, this relationship is critical to understanding the broader economic forces that drive inflation. by testing these hypotheses, the study aims to uncover the underlying dynamics of cpi and provide a more comprehensive understanding of how various economic factors contribute to inflation. the results will not only offer insights into the specific period of study but also provide a framework for analyzing inflation in future economic conditions. literature review the consumer price index (cpi) remains a cornerstone in the measurement of inflation, providing critical data for economic policy, business planning, and social welfare programs. however, its accuracy and relevance have been increasingly scrutinized considering the evolving economic landscape and the introduction of new methodologies aimed at addressing inherent biases and limitations. historical overview and development of cpi since its inception, the cpi has undergone several revisions to better reflect consumer behavior and economic conditions. the bureau of labor statistics (bls) has continuously updated the cpi’s methodology, particularly in response to critiques like those from the boskin report of 1996, which highlighted the index’s upward bias due to substitution effects, quality changes, and the introduction of new products. to address these issues, the bls implemented the geometric means formula in 1999, which accounts for lower-level substitution—where consumers switch between similar products as prices change. pa ge 13 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 136-144, 2024 in recent years, the bls has also expanded the scope of data sources to improve the cpi’s accuracy. for instance, a 2021 report highlighted efforts to integrate big data and advanced statistical methods into the cpi calculation, aiming to capture more timely and relevant consumer price changes. despite these improvements, some argue that the cpi still does not fully account for upperlevel substitution, where consumers substitute entirely different goods in response to price changes, potentially leading to residual biases in the index (bls.gov, 2023). cpi as a measure of inflation and cost of living the cpi’s role as the primary measure of inflation has made it indispensable for economic analysis. however, its ability to reflect the true cost of living remains contentious. a significant critique is the cpi’s limited capacity to capture quality improvements in goods and services. for instance, a smartphone that has doubled in capabilities may cost more, but the cpi would typically register this as pure inflation, failing to account for the enhanced value the consumer receives. moreover, the cpi’s focus on urban consumers overlooks the price dynamics in rural and suburban areas, where cost structures can differ substantially. this urban-centric bias has led to calls for more geographically nuanced indices that better represent the diversity of consumer experiences across the country. in addition, recent studies have pointed out the limitations of the cpi in capturing “hidden inflation,” where the quality of goods deteriorates, or product sizes shrink without a corresponding price change—a phenomenon not fully accounted for in the current cpi framework (maverick, 2024). factors influencing cpi unemployment, interest rates, and consumer spending remain pivotal in shaping the cpi. the relationship between unemployment and cpi is particularly complex. traditionally explained by the phillips curve, this relationship has evolved, with recent data from 2023 showing that low unemployment no longer consistently leads to higher inflation, suggesting that other factors, such as global supply chains and technology, are moderating price pressures. interest rates, as controlled by the federal reserve, directly impact borrowing costs and thus influence consumer spending and inflation. the november 2023 cpi report indicated a year-over-year increase of 3.1%, highlighting the persistent, though moderated, inflationary pressures despite aggressive rate hikes by the federal reserve aimed at curbing price growth. this interplay between interest rates and cpi underscores the importance of monetary policy in managing inflation (morgan, 2023). consumer spending, reflected in the sales index, is another critical driver of cpi. with the rise of e-commerce and changing consumer preferences, the composition of the market basket used to calculate cpi has had to adapt. however, this adaptation is not always timely, leading to potential lags in how accurately cpi reflects current spending patterns. methodological approaches to studying cpi recent advancements in econometric modeling and data analytics have improved the precision of cpi measurements. the use of time series analysis remains prevalent, but there is a growing emphasis on integrating big data and real-time analytics to better capture the nuances of consumer price changes. these methods offer a more granular understanding of inflation dynamics, though challenges in data quality and the risk of overfitting models remain. in response to criticisms of bias, the bls has also emphasized transparency in its methodology. the median standard error for 12-month changes in the all-items cpi remains low at 0.07%, indicating high confidence in the cpi’s reported figures. however, the standard error increases significantly when examining smaller geographic regions or specific item categories, suggesting that broader indices should be preferred for policy applications (bls.gov, 2021). limitations and criticisms of cpi despite methodological improvements, the cpi continues to face significant criticisms. the issue of substitution bias, both lower-level and upper-level, remains partially unresolved, potentially leading to either an overestimation or underestimation of inflation. additionally, the exclusion of certain expenses, like investment costs and certain taxes, from the cpi basket has led to debates about its comprehensiveness as a measure of the cost of living. furthermore, the cpi’s inability to fully capture the impact of new products and quality changes remains a significant limitation. as consumer preferences shift towards more technologically advanced goods, the cpi’s lag in incorporating these changes can result in a misrepresentation of actual inflation. the relevance of cpi in contemporary economic policy despite its flaws, the cpi remains crucial for economic policy. it is used to adjust social security benefits, tax brackets, and wages, making its accuracy vital for millions of americans. however, the ongoing debates about its biases and limitations underscore the need for continuous refinement. future research may focus on developing alternative indices that better reflect the actual cost of living, and the diverse economic realities faced by different population groups across the united states. summary of key studies key studies continue to shape the understanding and improvement of cpi. the boskin report remains a seminal work in this field, and more recent studies by the bls and independent researchers have built on its findings to refine cpi calculations. the challenges of accurately measuring inflation in a rapidly changing economy ensure that cpi will remain a critical, if imperfect, tool for economic analysis and policymaking pa ge 13 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 136-144, 2024 materials and methodos data collection the data for this study was sourced from www. economagic.com, covering the period from january 2014 to december 2023. the dataset includes monthly observations of cpi, unemployment levels, interest rates, and the sales index. this period was chosen to capture a range of economic conditions, including post-recession recovery and varying interest rate environments. modeling approach multiple regression analysis was employed to model the relationship between cpi and the selected explanatory variables. the study also conducted tests for multicollinearity, heteroscedasticity, and goodness-of-fit to ensure the robustness of the model. these statistical tests are critical in validating the model’s accuracy and ensuring that the results are reliable for policy implications. statistical charts and results the descriptive statistics table provides an overview of the key characteristics of the variables studied over the period from january 2014 to december 2023. the table includes summary statistics for the consumer price index (cpi), unemployment level, interest rate, and sales index. cpi (consumer price index) o the mean cpi value is approximately 246.11, with a standard deviation of 6.52, indicating some variability around the mean over the period. o the minimum cpi recorded was 227.24, and the maximum was 261.30, showing that cpi fluctuated by about 34 points during the study period. o the median (50th percentile) cpi value is 247.28, suggesting that half of the cpi values were below this point, with the other half above. unemployment level o the average unemployment level was 4.36%, with a standard deviation of 0.68%, indicating that unemployment rates were relatively stable. o unemployment ranged from a minimum of 2.61% to a maximum of 5.95%, reflecting fluctuations in the labor market during the period. o the median unemployment level is 4.34%, close to the mean, suggesting a roughly symmetrical distribution. interest rate o the mean interest rate was 2.30%, with a standard deviation of 0.28%. o interest rates varied between a minimum of 1.70% and a maximum of 2.83%. o the median interest rate is 2.36%, indicating that most of the interest rate observations are slightly above the mean. sales index o the mean sales index was 102.21, with a standard deviation of 10.74, indicating more variability in sales activity. o the minimum value recorded was 77.60, and the maximum was 129.12, showing a wide range of consumer sales activity. o the median sales index value is 102.67, suggesting that sales figures were distributed evenly around the mean. overall, the descriptive statistics provide a snapshot of the data distribution and variability, helping to understand the typical values and the range of fluctuations for each variable during the study period. this information is crucial for interpreting the relationships between these variables in the subsequent analysis. table 1: summary statistics of the economic predictors cpi unemployment level interest rate sales index count 120 120 120 120 mean 246.108 4.363 2.301 102.206 std 6.524 0.684 0.276 10.740 min 227.235 2.614 1.702 77.603 25% 241.909 3.904 2.113 94.878 50% 247.279 4.339 2.360 102.668 75% 250.728 4.857 2.510 109.457 max 261.297 5.948 2.832 129.125 pa ge 14 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 136-144, 2024 the plot shows the trend and fluctuations in cpi over the extended period, providing a visual understanding of how cpi has evolved over time. figure 1: the time plot of the consumer price index (cpi) from january 2014 to december 2023 figure 2: the time plot of the interest rate from january 2014 to december 2023 figure 3: the time plot of the unemployment level from january 2014 to december 2023 the above plot illustrates the trend and changes in interest rates over the specified period, providing insights into how rates have fluctuated over time. this plot provides a visual representation of the trends in unemployment over the extended period pa ge 14 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 136-144, 2024 this plot shows the distribution of cpi values over time, highlighting the variability and overall trend. the analysis of the consumer price index (cpi) over the period from january 2014 to december 2023 yielded several important findings regarding the relationships between cpi and the selected economic indicators: unemployment level, interest rates, and sales index. correlation analysis the correlation analysis revealed that cpi has a moderate positive correlation with interest rates (r = 0.46) and a weaker positive correlation with the sales index (r = 0.29). conversely, there is a moderate negative correlation between cpi and unemployment level (r = -0.44). these correlations suggest that as interest rates and sales figure 4: the scatter plot of the consumer price index (cpi) from january 2014 to december 2023 table 2: key correlations between the variables cpi unemployment level interest rate sales index cpi 1.000 -0.436 0.458 0.289 unemployment level -0.436 1.000 -0.551 -0.215 interest rate 0.458 -0.551 1.000 0.208 sales index 0.289 -0.215 0.208 1.000 increase, cpi tends to rise, while higher unemployment is associated with a decline in cpi. cpi and unemployment level there is a negative correlation of -0.44, indicating that as cpi increases, the unemployment level tends to decrease, and vice versa. cpi and interest rate there is a positive correlation of 0.46, suggesting that higher interest rates are associated with higher cpi values. cpi and sales index there is a moderate positive correlation of 0.29, indicating that increases in the sales index are associated with increases in cpi. other correlations of note unemployment level and interest rate there is a strong negative correlation of -0.55, indicating that higher unemployment levels are associated with lower interest rates. unemployment level and sales index there is a weak negative correlation of -0.21, suggesting a slight tendency for unemployment to decrease as the sales index increases. regression analysis the regression analysis provided a more detailed understanding of these relationships. the model showed that: interest rate a significant positive relationship exists between interest rates and cpi, with a coefficient of 6.85 (p = 0.003). this indicates that a one-unit increase in the interest rate is associated with a 6.85 unit increase in cpi, holding other factors constant. sales index the sales index also has a positive and statistically significant impact on cpi, with a coefficient of 0.11 (p = 0.029). although this effect is smaller compared to the interest rate, it confirms that increased consumer pa ge 14 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 136-144, 2024 spending, as reflected in the sales index, contributes to inflationary pressures. unemployment level the analysis found a significant negative relationship between unemployment level and cpi, with a coefficient of -2.28 (p = 0.013). this suggests that higher unemployment leads to a decrease in cpi, possibly due to reduced consumer spending and demand. table 3: the regression table showing classifications of the indicators the regression analysis table coefficients p-values 95% ci lower bound 95% ci upper bound const 229.227 1.4e-47 210.658 247.795 unemployment_level -2.276 1.3e-02 -4.063 -0.489 interest_rate 6.849 2.7e-03 2.424 11.274 sales_index 0.108 2.9e-02 0.011 0.205 model summary dependent variable cpi r-squared 0.288 (28.8% of the variance in cpi is explained by the model) adjusted r-squared 0.270 (adjusted for the number of predictors) f-statistic 15.65 (significant at p < 0.0001) prob (f-statistic) 1.31e-08 (indicates that the overall model is statistically significant) coefficients constant (intercept) 229.227 (the expected cpi when all predictors are zero) unemployment level -2.276 (a negative coefficient, suggesting that an increase in unemployment is associated with a decrease in cpi. this result is statistically significant with a p-value of 0.013) interest rate 6.849 (a positive coefficient, indicating that an increase in interest rates is associated with an increase in cpi. this result is statistically significant with a p-value of 0.003) sales index 0.108 (a positive coefficient, suggesting that an increase in the sales index is associated with a slight increase in cpi. this result is statistically significant with a p-value of 0.029) diagnostics durbin-watson 1.895 (close to 2, suggesting no strong autocorrelation in the residuals) omnibus test not significant, suggesting that the residuals are normally distributed. interpretation • the model shows that both the interest rate and the sales index positively impact the cpi, while the unemployment level has a negative effect on cpi. all predictors are statistically significant, contributing to the explanation of cpi variability. • the r-squared value of 0.288 indicates that the model explains about 29% of the variance in cpi, which suggests that other factors not included in the model may also influence cpi. hypothesis testing all three null hypotheses, which posited no significant relationships between cpi and the individual economic indicators, were rejected based on the p-values obtained from the regression analysis. this confirms that unemployment level, interest rates, and sales index are significant predictors of cpi. this is further summarized below: hypothesis there is no significant relationship between cpi and unemployment level. o p-value: 0.013 o result: the null hypothesis is rejected. there is a significant relationship between cpi and unemployment level. hypothesis there is no significant relationship between cpi and sales index. o p-value: 0.029 o result: the null hypothesis is rejected. there is a significant relationship between cpi and sales index. hypothesis there is no significant relationship between cpi and interest rates. pa ge 14 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 136-144, 2024 o p-value: 0.003 o result: the null hypothesis is rejected. there is a significant relationship between cpi and interest rates. in all three cases, the p-values are below the significance level of 0.05, leading to the rejection of the null hypotheses. this indicates that each of these variablesunemployment level, sales index, and interest rates-has a significant relationship with cpi. model performance the regression model explained about 28.8% of the variance in cpi (r-squared = 0.288), indicating that while the model is statistically significant, other factors not included in the study also influence cpi. the diagnostic tests confirmed the validity of the model, with no significant issues related to multicollinearity or autocorrelation. summary this study focused on modeling the united states consumer price index (cpi) over the period from january 2014 to december 2023, examining the relationships between cpi and key economic indicators such as unemployment level, interest rates, and sales index. the objective was to develop a predictive model that would help better understand the factors influencing cpi, which is a critical measure of inflation and economic health. through the application of time series data and multiple regression analysis, significant relationships were identified between cpi and each of the explanatory variables. the study found that both interest rates and sales index positively correlate with cpi, suggesting that increases in these variables are associated with higher levels of inflation as measured by cpi. conversely, unemployment level was found to have a negative relationship with cpi, indicating that higher unemployment tends to be associated with lower inflation. the regression model developed explained approximately 28.8% of the variance in cpi, which, while statistically significant, suggests that other factors not included in the model also play a role in influencing cpi. these findings were further validated through hypothesis testing, where the null hypotheses that there are no significant relationships between cpi and the explanatory variables were all rejected. conclusion the results of this study underscore the complexity of inflation dynamics and the multifaceted nature of the consumer price index. the relationships identified between cpi and the economic indicators— unemployment level, interest rates, and sales index— are consistent with economic theory and previous research, yet they also highlight areas where cpi may not fully capture the cost of living or economic reality. the positive relationship between cpi and interest rates aligns with the understanding that higher borrowing costs can lead to higher prices for goods and services, thus driving inflation. the significant impact of the sales index on cpi further emphasizes the role of consumer spending and retail activity in shaping inflationary trends. on the other hand, the negative relationship between cpi and unemployment level reflects the reduced purchasing power and demand that typically accompany higher unemployment, leading to downward pressure on prices. however, the relatively modest r-squared value indicates that cpi is influenced by a broader set of factors beyond those included in this study. this suggests that policymakers and economists need to consider a wider range of variables and possibly more nuanced models to fully understand and predict inflation. economic implications the findings of this study have important implications for economic policy and decision-making in the united states: monetary policy the positive relationship between interest rates and cpi suggests that central banks, like the federal reserve, can use interest rate adjustments as a tool to manage inflation. however, the complexity of this relationship also implies that rate hikes alone may not be sufficient to control inflation, especially if other inflationary pressures, such as those from the labor market or consumer demand, are at play. labor market policies the inverse relationship between unemployment levels and cpi indicates that policies aimed at reducing unemployment could have inflationary effects, particularly if they stimulate demand. policymakers must balance efforts to achieve full employment with the need to keep inflation in check, ensuring that wage growth and productivity increases are aligned. consumer spending and retail sector the impact of the sales index on cpi highlights the importance of consumer confidence and spending patterns in driving inflation. policymakers should consider how changes in consumer behavior, whether due to economic conditions, shifts in preferences, or external shocks, can influence inflationary trends. this could involve monitoring retail sales more closely as an indicator of potential inflationary pressures. inflation measurement the study also raises questions about the adequacy of cpi as a measure of the cost of living. given that cpi may not fully capture all factors influencing inflation, there is a need for continuous refinement of the index. this could involve incorporating additional variables or developing complementary indices that provide a more comprehensive picture of inflation and economic wellbeing across different regions and demographic groups. in conclusion, this study contributes to the understanding pa ge 14 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 136-144, 2024 of the relationships between cpi and key economic indicators, offering insights that can inform more effective economic policies. the results suggest that while cpi is a valuable tool for measuring inflation, it must be used in conjunction with other economic indicators and models to accurately assess and manage inflationary pressures in the u.s. economy. references bernanke, b. s., & rotemberg, j. r. (2012). the consumer price index and the cpi enhancement initiative 2008-2009 to 2012-2013. statistics canada. https://www.statcan. gc.ca/eng/about/er/cpi#a0 boskin, m. j., dulberger, e. r., gordon, r. j., griliches, z., & jorgenson, d. w. (1996). consumer prices, the consumer price index, and the cost of living. national bureau of economic research. bureau of labor statistics. (2012). consumer price index data quality: how accurate is the u.s. cpi? https://www. bls.gov/opub/btn/volume-1/consumer-price-indexdata-quality-how-accurate-is-the-us-cpi.htm bureau of labor statistics. (2018). cpi frequently asked questions and answers. https://www.bls.gov/cpi/ questions-and-answers.htm bureau of labor statistics. (2021). the latest on improving the accuracy of the consumer price index. https://www.bls. gov/cpi/improving-accuracy.htm economagic. (2024). is now a good time to invest? https:// www.economagic.com/ j.p. morgan wealth management. (2023). november 2023 cpi report: headline inflation is still cooling. https:// www.jpmorgan.com/insights/outlook/economicoutlook/cpi-report-november-2023 maverick, j. b. (2024). limitations of the consumer price index (cpi). https://www.investopedia.com/ask/ answers/012915/what-are-some-l imitationsconsumer-price-index-cpi.asp moody’s analytics. (2018). cpi charts for the united states. https://www.economy.com/united-states/ consumer-price-index-cpi norum, p. s. (1987). u.s. clothing expenditures: a time series analysis, 1929-1987 (doctoral dissertation). university of missouri-columbia. article 2510.indd pa ge 1 pa ge 99 american journal of applied statistics and economics (ajase) analyzing university students’ attitude and behavior toward ai using the extended unifi ed theory of acceptance and use of technology model brandon nacua obenza1*, john harry s. caballo1, ria bianca r. caangay2, trisha eunice c. makigod1, sharldawn m. almocera1, john lawrence m. bayno1, joseph jr. r. camposano1, sandy jean g. cena1, judy ann kyll garcia1, bea faye m. labajo1, athena grace tua1 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2510 https://journals.e-palli.com/home/index.php/ajase article information abstract received: april 06, 2024 accepted: may 09, 2024 published: may 13, 2024 this quantitative study using partial least square structural equation modeling (pls-sem) examined a structural model of the attitudes and behaviors of university students toward ai in higher education. the results obtained using smartpls 4.0 indicate that the constructs exhibit validity and reliability (λ ≥ 0.708, α=0.767-0.948, ave=0.584-0.777, htmt=< 3.3). further, the analysis of the hypothesized extended unifi ed theory of acceptance and use (utaut) model reveals that ai awareness signifi cantly impacts attitude toward ai (β = 0.156, p = 0.003) and behavioral intention to use ai (biu) (β = 0.337, p < 0.001). ai trust also signifi cantly infl uences attitude toward ai (β = 0.366, p < 0.001) and biu-ai (β = 0.173, p = 0.007). additionally, attitude toward ai is a strong predictor of biu-ai (β = 0.457, p < 0.001). social infl uence signifi cantly affects attitude toward ai (β = 0.21, p < 0.001), while effort expectancy and performance expectancy do not show signifi cant effects in this context. the link between facilitating conditions and biu-ai is also insignifi cant. the model explained a substantial portion of the variance in attitude (r2 =0.612) and behavior (r2 =0.710). fit indices indicate good model fi t, and predictive relevance metrics were satisfactory. keywords attitude toward artifi cial intelligence, behavioral toward ai, utaut model, partial least square structural equation modeling (pls-sem), philippines 1 university of mindanao, davao city, philippines 2 ateneo de davao university, davao city, philippines * corresponding author’s e-mail: bobenza@umindanao.edu.ph introduction artifi cial intelligence (ai), which automates tasks and emulates human intelligence (geetha & bhanu sree reddy, 2018; jarrett & choo, 2021; khanagar et al., 2021; saravanan et al., 2017), is rapidly growing (barton et al., 2017; beig & qasim, 2023; hassani et al., 2020; hilale, 2021; olhede & wolfe, 2018). it has now become an important technology that benefi ts society and the economy (cockburn et al., 2018; hall & pesenti, 2017; lu et al., 2018) and pervades many aspects of people’s daily lives (hilale, 2021; loble et al., 2017; mintz & brodie, 2019; olhede & wolfe, 2018; tahiru, 2021). as ai has been prominent in various sectors (berdiyorova et al., 2021; hall & pesenti, 2017; jindal et al., 2021; paul et al., 2021), this technology has also been integrated into the field of higher education (crompton & burke, 2023; pedro et al., 2019; zawacki-richter et al., 2019; zhang & aslan, 2021). the use of artifi cial intelligence (ai) has been implemented in language learning to improve instruction and address the learners’ needs (chen, 2021), facilitate interaction between instructors and learners (seo et al., 2021), and foster learners’ educational experience (alam, 2021; kavitha & lohani, 2018). although artifi cial intelligence is thriving (shao et al., 2020; tan & ran, 2022; zhou et al., 2019), it is also seen as a threat to education (humble & mozelius, 2022; xie & wang, 2023). some college students were concerned with ethical issues (farhi et al., 2023; ghotbi et al., 2022) and were wary of an unnatural learning environment (kushmar et al., 2022). the majority of college students have no intention of using ai to complete assignments or exams in the near future (welding, 2023), and according to skeat and ziebell (2023), a signifi cant number of students still strongly oppose such technology utilization. in a similar study, it was also revealed that although the respondents understand the essence of ai technology and how it benefi ts their daily lives, they are not entirely clear about the benefi ts of incorporating artifi cial intelligenceenhanced technologies in learning and teaching (slavov et al., 2023). prior studies explored people’s attitudes toward and behavioral intention to use artifi cial intelligence. in the study of yadrovskaia et al. (2023), the respondents have a positive attitude towards the use of ai despite not fully grasping the fundamentals of these technologies. additionally, some students believe that ai will positively benefi t the fi eld of education (kairu, 2020; marrone et al., 2022), and they also have a positive attitude toward using it because it engages students and accommodates their varying cognitive levels (obenza et al., 2023b; pande et al., 2020). these attitudes regarding ai affect people’s level of trust in ai technology (liehner et al., 2023). moreover, artifi cial intelligence, such as chatbots, is found appealing to language learners since they can use them without the teachers’ assistance, which helps them develop into independent learners (mohamed & alian, 2023). according to chen et al. (2021), students’ behavioral intention to study a language was positively correlated with their knowledge of ai-enabled language applications, attitude to use ai, perceived ease of use, subjective norm, and behavioral intention. romerorodriguez et al. (2023) used the unifi ed theory of pa ge 10 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 99-108, 2024 acceptance and use of technology (utaut) model for technology adoption to fi nd that university students accept artifi cial intelligence like chatgpt because they think it could help them learn. usefulness, performance expectancy, hedonic motivation, private value, and habits also affect students’ ai chatbot prototype use. kim (2017) found using the utaut model that expectation, social infl uence, work usefulness, and anxiety signifi cantly affected healthcare university students’ intention to use ai technology. kim (2017) found that the use intention factor partially mediated the direct effect of the anxiety factor on the attitude factor and the task’s usefulness factor on the attitude factor after verifying the indirect effect. kaya et al. (2022) also reported that ai anxiety is a problem that can hinder the adoption, use, or acceptance of technology and can cause people to underestimate the usefulness of ai technology, fail to recognize its simplicity and fail to recognize its benefi ts. moreover, in the study of gado et al. (2022), the perceived usefulness of ai, attitude towards ai, perceived social norm regarding ai, and ai literacy proved to be signifi cant indicators of students’ intent to use artifi cial intelligence. additionally, alzahrani (2023) discovered that while students’ attitudes were adversely affected by perceived risk, their behavioral intention to utilize ai in education was signifi cantly infl uenced by performance expectancy and facilitating conditions. additionally, the results indicate that effort expectation has no substantial effect on attitudes toward the use of ai in higher education. in light of the empirical investigations delineated within extant literature, the pivotal function of ai literacy in shaping attitudes towards artifi cial intelligence (ai) emerges prominently. in a recent study conducted by obenza et al. (2024), it was discerned that the cultivation of cognitive absorption among students represents a viable strategy for enhancing ai literacy, given its established status as a signifi cant predictor thereof. in addition to the above studies, utaut model has been used to study students’ adoption of artifi cial intelligenceenabled e-learning systems (lin et al., 2021), intelligencebased robots (roy et al., 2022), and ai-powered webbased english writing assistance software (intiser et al., 2023). however, despite studies and existing literature concerning people’s attitudes and behavioral intention to use ai, a particular study using utaut delving into ai trust and ai awareness in explaining and creating the structural model of college students’ attitudes and behavior towards ai has been none. therefore, this study is conducted to address this research gap. the results of this study can contribute specifi cally to academic sectors that are progressively integrating artifi cial intelligence in pedagogical approaches, as well as aid the technology sectors in enhancing ai tools that increase people’s positive view and adoption of ai. this can also benefi t future researchers in further in-depth exploration of factors infl uencing the students’ attitude and behavioral intention to use artifi cial intelligence. materials and methods this study utilized a quantitative research design, and more specifi cally, the non-experimental correlational approach was utilized all throughout the research process. in accordance with the defi nition provided by creswell and creswell (2023), quantitative studies make use of inquiry methodologies such as surveys and experiments, and the data collected is gathered on predetermined instruments that generate statistical measurements. researchers distributed google forms survey questionnaires to random participants. a stratifi ed random sampling method was utilized to select participants for the study. utilizing this method allowed for the study variables to be represented in an equitable manner. for latent variable path models, the estimation of complex cause-effect relationships can be accomplished through the use of partial least squares path modeling (pls-pm) or structural equation modeling (pls-em). as pls-sem gains popularity, more researchers are using it (hair et al., 2019a; hair et al., 2017b; ringle et al., 2015; sarstedt et al., 2019b). using pls-sem, researchers are able to estimate large models that contain multiple constructs, indicator variables, and structural paths without making any assumptions about distributional relationships. more importantly, pls-sem emphasizes prediction in statistical model estimation and is designed to explain causality (wold, 1982; sarstedt et al., 2017a). this partial data analysis method allows for smaller sample sizes. for the purpose of extrapolating sample results to the relevant population, larger sample sizes should be used whenever possible.(hair et al., 2022b; kock & hadaya, 2018). the researchers used adapted questionnaires in the form of 5-point likert scales to gather the data. the attitude toward ai scale (suh & ahn, 2022), facilitating condition, performance expectancy, effort expectancy, and behavioral intention to use scales (chatterjee & bhattacharjee, 2020), social infl uence scale (kandoth & shekhar, 2022), ai awareness scale (isaac et al., 2017), and ai trust scale (choung et al., 2022). the study applied the 10-times rule proposed by hair et al. (2011) to determine the total number of samples gathered. this method is commonly utilized in pls-sem to determine the minimal sample size. this strategy relies on the premise that the sample size must exceed ten times the highest number of inner or outer model linkages directed at any latent variable in the model (hair et al., 2017).the minimal sample size computed based on this criteria is 90. the study selected 322 college students from different universities in region xi by stratifi ed random sampling, exceeding the recommended sample size to ensure accurate results, particularly as we are suggesting a concise model. a further evaluation of the validity and reliability of the measurement model was carried out using cronbach’s alpha. the method known as the average variance extracted (ave) was utilized in order to assess the convergent validity of the model. on the other hand, the hetero-monotrait ratio (htmt) method was pa ge 10 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 99-108, 2024 utilized in order to assess the discriminant validity of the model. the vif was also utilized in this process. for the purpose of evaluating the hypothesized structural model, the bootstrapping standardized algorithm was utilized through the smarpls 4.0 software. results and discussion assessment of measurement model cronbach alpha and composite reliability are the two measurements that are most frequently used to determine internal consistency. these measurements determine reliability based on the interrelationship of the variables that are observed in the items (hamid et al., 2017). the reliability of the instruments that were utilized in the research is presented in table 1. it was determined that cronbach’s alpha was the most reliable method for evaluating the instruments. cronbach’s alpha values for the questionnaires are as follows: 0.854 for ai awareness (ai-a), 0.937 for ai trust (ai-t), 0.951 for attitude towards ai (at-ai), 0.904 for behavioral intention to use (biu), 0.767 for effort expectancy (ee), 0.864 for facilitating conditions (fc), 0.873 for performance expectancy (pe), and 0.896 for social infl uence. these values indicate that the questionnaires have a high degree of internal consistency (si). composite reliability and cronbach alpha values that fall within the range of 0.60 to 0.70 are considered acceptable; however, in the more advanced stage, the value absolutely must be greater than 0.70. (hair et al., 2014). the evaluation of the instruments’ convergent validity was conducted by calculating the ave. convergent validity is the degree of agreement regarding the correlation between multiple indicators of the same construct (hamid et al., 2017). biu (0.777), ai-a (0.696), ai-t (0.613), si (0.766), ee (0.682), fc (0.647), and pe (0.664) all had ave values that surpassed the 0.5 threshold. this is deemed acceptable in light of the fact that an acceptable minimum acceptable ave is 0.50. an ave value of 0.50 or greater signifi es that the construct accounts for a minimum of 50 percent of the variance exhibited by the items comprising the construct (bagozzi & yi, 1988; fornell & larcker, 1981; hair et al., 2014; henseler et al., 2009). table 1: construct reliability and validity cronbach’s alpha composite reliability (rho_a) average variance extracted (ave) ai-a 0.854 0.855 0.696 ai-t 0.937 0.937 0.613 at-ai 0.951 0.952 0.584 biu 0.904 0.906 0.777 ee 0.767 0.767 0.682 fc 0.864 0.869 0.647 pe 0.873 0.878 0.664 si 0.896 0.899 0.766 table 2: heterotrait-monotrait ratio (htmt) ai-a ai-t at-ai biu ee fc pe si ai-a ai-t 0.723 at-ai 0.691 0.742 biu 0.824 0.762 0.834 ee 0.709 0.573 0.645 0.611 fc 0.519 0.517 0.566 0.497 0.570 pe 0.682 0.638 0.662 0.650 0.823 0.725 si 0.623 0.649 0.682 0.668 0.662 0.570 0.654 the next test employed was the htmt values. this test evaluated the discriminant validity of the scales which pertains to the extent to which the items discriminate from one another empirically (hamid et al., 2017). the htmt ratios of the constructs spans between 0.50 to 0.60 in the following construct pairs: ai-a and ai-t (0.723), ai-t and at-ai (0.742), at-ai and biu (0.834), biu and ee (0.611), ee to fc (0.570), fc and pe (0.725), pe and si (0.654), ai-a and at-ai (0.691), ai-t and biu (0.762), at-ai and ee (0.645), biu and fc (0.497), ee and pe (0.823), fc and si (0.570), ai-a and biu (0.824), ai-t and ee (0.573), biu and pe (0.650), ee and si (0.662), ai-a and ee (0.709), ai-t and fc (0.517), at-ai and pe (0.662), biu and si (0.668), ai-a and fc (0.519), ai-t and pe (0.638), at-ai and si (0.682), ai-a and pe (0.682), ai-t and si (0.649), and ai-a and si (0.623). the fact that all ratios are lower than the threshold of 0.85 demonstrates that there is strong discriminant validity between the constructs (kline, 2011). additionally, gold et al. (2001) suggested that a value of 0.90 should be proposed as the threshold. pa ge 10 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 99-108, 2024 prior to evaluating the structural relationships, it is necessary to investigate the collinearity in order to guarantee that it does not introduce any bias into the regression results. vif values that are greater than fi ve are indicative of probable collinearity issues among the predictor constructs, as stated by hair et al. (2019). however, collinearity issues can also occur at lower vif values, which range from three to fi ve (mason & perreault, 1991; becker et al., 2014). in an ideal situation, the values of the vif should be close to three or lower. the creation of higher-order models that are capable of being supported by theory is a common solution that is utilized in situations where collinearity is a problem (hair et al., 2017b). association both with attitude toward ai (coeffi cient = 0.366) and behavioral intention to use (coeffi cient = 0.173). these fi ndings suggest that as students’ trust in ai increases, so does their favorable attitude toward it, as well as their inclination to use ai technology. these relationships are statistically signifi cant, underscoring the signifi cance of trust in shaping attitudes and intentions toward ai adoption (t = 5.621, p = 0.000 and t = 2.679, p = 0.007, respectively). the results of this study lend credence to the fi ndings of obenza et al. (2023a), which indicated that trust in artifi cial intelligence had a signifi cant impact on attitudes toward ai. the fi ndings are also consistent with the assertions made by choung et al. (2022), who stated that trust acts as a precursor to positive attitudes, which in turn affects usage intentions. the level of trust that an individual has in artifi cial intelligence (ai) is a signifi cant factor in determining their attitude toward ai technology as well as their willingness to interact with it, as stated by schepman and rodway (2023). furthermore, the fi ndings of the research carried out by emon et al. (2023) and cook (2023) demonstrated that trust in artifi cial intelligence (ai) plays a signifi cant part in determining whether or not an individual intends to make use of using it. the path from attitude toward ai to behavioral intention to use exhibits a robust positive relationship (coeffi cient = 0.457). this implies that a positive attitude toward ai among students signifi cantly infl uences their intention to use it. this relationship is highly signifi cant (t = 8.597, p = 0.000), indicating its substantial impact. these fi ndings are consistent with multiple research that have demonstrated a substantial correlation between attitude toward ai and the intention to employ it (hasan emon, 2023; saxena, 2023). however, the paths from effort expectancy to attitude toward ai and from facilitating conditions to behavioral intention to use demonstrate weaker relationships, as evidenced by their non-signifi cant p-values (p > 0.05). the results of this study suggest that the students’ perceptions of the amount of effort required to use artifi cial intelligence and the presence of favorable conditions do not signifi cantly impact the attitudes and intentions of the students regarding the adoption of ai in this particular environment. these fi ndings are in direct opposition to the fi ndings of other research such those of hasan emon and alzahrani (2023) that have demonstrated that enabling environments have an effect on the intention to engage in certain behaviors. similarly, while performance expectancy exhibits a positive relationship with attitude toward ai, the relationship is not statistically signifi cant (t = 1.711, p = 0.087), indicating that perceptions regarding the performance benefi ts of ai may not strongly infl uence attitudes toward ai among university students. according to alzahrani (2023), performance expectancy signifi cantly infl uences students’ attitudes toward ai. finally, social infl uence proves to be a substantial indicator of attitude towards ai, with a coeffi cient of table 3: variance infl ation factor (vif) vif ai awareness -> attitude toward ai 2.107 ai awareness -> behavioral intention to use 1.959 ai trust -> ai awareness 1.000 ai trust -> attitude toward ai 2.114 ai trust -> behavioral intention to use 2.342 attitude toward ai -> behavioral intention to use 2.343 effort expectancy -> attitude toward ai 2.096 facilitating conditions -> behavioral intention to use 1.441 performance expectancy -> attitude toward ai 2.329 social infl uence -> attitude toward ai 1.891 assessment of structural model examining the path from ai-awareness to attitude toward ai, the coeffi cient of 0.156 indicates a positive relationship. this suggests that as students’ awareness of ai increases, their attitude toward ai tends to become more positive. these fi ndings indicate that as students’ knowledge of ai grows, their perception of ai tends to become more favorable. the statistical analysis reveals a signifi cant connection (t = 3.004, p = 0.003), emphasizing its signifi cance. similarly, the association between ai awareness and behavioral intention to use is larger, with a value of 0.337. this implies that higher levels of ai awareness among students are associated with a greater intention to use ai technology. this relationship is not only statistically signifi cant but also notably stronger (t = 6.009, p = 0.000). this corroborates the fi ndings of marrone et al. (2022), who discovered that students with a greater comprehension of ai expressed more favorable attitudes toward incorporating ai into their educational environments. students with limited comprehension of ai exhibited a tendency to experience apprehension towards ai. moving on to ai trust, the results indicate a positive pa ge 10 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 99-108, 2024 0.21. consequently, the impact of classmates, professors, or societal norms is crucial in molding students’ attitudes toward ai. the statistical analysis reveals a signifi cant association (t = 4.051, p = 0.000), indicating that social variables play a crucial role in shaping attitudes towards the adoption of ai. figure 1: partial least squares structural equation modeling (pls-sem) results using smart pls 4.0 table 3 presents various statistical measures used to evaluate the fi t and predictive power of a structural equation model (sem) that aims to analyze university students’ attitudes and behavior toward ai using the utaut model. the bayesian information criterion (bic) values of both endogenous variables, ‘at-ai’ and ‘biu,’ have negative bic values, suggesting a good fi t. lower and negative bic values can occur and generally indicate a very strong model according to the likelihood function. the r-squared (r²) and adjusted r² values represent the proportion of variance explained by the model. for atai, 61.2% of the variance is explained, and for biu, 71.0% is explained. the high values of both r² and adjusted r² indicate a strong model. the predictive relevance (q²) value shows the model’s predictive relevance. a value larger than zero suggests the model has predictive relevance for the construct. both of both endogenous variables, at-ai and biu, show values well above zero, indicating good predictive power. the root mean square error (rmse) and mean absolute error (mae) are measurements of the average error that occurs between the predicted and observed values. lower values are preferable because they demonstrate that the model’s predictions are relatively close to the values that actually occur. because the values of the model are relatively low, it appears that the model is able to make accurate predictions.the standardized root mean square residual (srmr) is a measure of fi t used in structural equation models. values less than 0.08 are generally considered good. the model shows srmr values close to this threshold, suggesting an acceptable fi t. the unweighted least squares discrepancy (d_uls) and geodesic discrepancy (d_g) are discrepancy functions based on unweighted least squares and geodesic distances. the smaller these values, the better the model fi t. based on the fact that the values of the saturated model, which is the most complex model, and the estimated model, which is the proposed model, are relatively close to one another, it is possible to draw the conclusion that the proposed model fi ts almost as well as the most complex model that is possible. utilizing the chi-square statistic, one can perform an analysis of the disparity that exists between the covariance matrices that were observed and those that were anticipated. however, a lower value indicates a better fi t between the data and the model. the chi-square value is sensitive to the sample size; yet, a lower value indicates a better fi t. however, this should be interpreted in the context of other fi t indices and sample sizes. the model has a high chi-square value, which may indicate that it does not fi t the data well. however, this should be taken into consideration. pa ge 10 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 99-108, 2024 to determine how well the estimated model fi ts the data, the normed fit index (nfi) compares it to a null model. this comparison is made in order to determine how well the model fi ts the data. a more satisfactory fi t is indicated by values that are closer to the number one. each of the models has an nfi of approximately 0.72, which is lower than the threshold of 0.95 that is typically recommended. this indicates that there is room for improvement in the model. table 4: model fit endogenous variables bic r2 adjusted r2 q2 predict rmse mae attitude toward ai -271.195 0.612 0.606 0.588 0.646 0.450 behavioral intention to use -370.864 0.710 0.706 0.622 0.619 0.432 saturated model estimated model srmr 0.059 0.061 d_uls 5.221 5.308 d_g 2.501 2.506 chi-square 4293.77 4300.13 nfi 0.723 0.722 conclusion based on the utaut model and actual results, this study’s theoretical implications reveal a complex understanding of university students’ views and behavior toward artifi cial intelligence (ai). this section examines how the fi ndings correlate with and extend the utaut model, providing insights into the elements that infl uence ai adoption in an academic setting. the positive relationship between ai awareness and students’ attitudes and behavioral intentions to use ai underscores the critical role of knowledge and exposure in shaping perceptions of technology. this fi nding is consistent with the utaut model’s emphasis on performance expectancy and effort expectancy as signifi cant factors of technology adoption, suggesting that improved awareness can reduce perceived efforts and improve performance expectations (venkatesh et al., 2016). furthermore, the signifi cant impact of ai trust on both attitude and behavioral intention emphasizes the importance of credibility and reliability in the adoption process, which is consistent with the model’s suggestion that social infl uence and enabling conditions are critical in technology acceptance. however, the weaker/non-signifi cant connections between effort expectancy and facilitating conditions with attitude and behavioral intention, respectively, call into question the utaut model’s assertions about student ai adoption. this shows that other factors, presumably related to ai technology, such as ethical issues or the type of ai applications, may have a greater impact on students’ views and intents. the robust association between attitude toward ai and behavioral intention to utilize ai further reinforces the main premise of the utaut model: good attitudes toward technology greatly contribute to its acceptance and utilization (venkatesh & davis, 2000; venkatesh et al., 2003). this suggests a direct channel for educators and policymakers to infl uence ai adoption by instilling a positive attitude towards ai in students. the signifi cant predictive power of social infl uence on attitude toward ai emphasizes the model’s assertion that social factors are crucial in technology adoption (venkatesh et al., 2003). this underscores the need for educational institutions to foster a culture that supports and encourages ai learning and exploration. in light of these fi ndings, this study expands upon the utaut model by highlighting the subtle impacts of ai awareness, trust, and social infl uence on university students’ views and actions toward ai. it implies that although the basic elements of the utaut model are still applicable, the distinct features of ai technology and its specifi c usage in educational environments require modifi cations to the model in order to understand the process of ai adoption in educational settings comprehensively. recommendations the fi ndings initiate a discourse regarding the strategic emphasis of educational programs and interventions. the primary focus of efforts to improve ai acceptance should be on establishing trust and promoting awareness while also creating an environment that supports good social infl uence. future research should investigate the specifi c reasons why performance and effort expectancy are not signifi cant and further examine how social infl uence works in the acceptability of technology in educational contexts. the correlation between ai awareness and the inclination to utilize ai indicates a pressing requirement for educational initiatives focused on enhancing ai literacy among students. this entails instructing not only the technical facets of ai but also its ethical, social, and practical ramifi cations. integrating ai subjects into the curriculum, covering fundamental principles to sophisticated applications, can cultivate a better-informed and favorable disposition towards ai technologies. the impact of ai trust on attitude, while not directly on behavioral intention, suggests that trust plays a crucial role in shaping favorable views, but it may not be enough to solely drive actual usage. hence, establishing confi dence should be a comprehensive undertaking, encompassing not just the dependability and openness pa ge 10 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 99-108, 2024 of ai but also addressing students’ apprehensions and misunderstandings around ai. given the increasing importance of ai across different industries, providing students with education on ai equips them with the necessary skills for future employment. gaining profi ciency in ai will be an essential aptitude, and early familiarity can provide pupils with a distinct advantage. limitations and future research directions although this study offers valuable information, it does have limits. further inquiry is needed to explore additional moderating or mediating variables due to the lack of a signifi cant association between most dimensions of the utaut model and at-ai and biu. subsequent investigations could examine how elements such as ai effi cacy, aspects of tam (technology acceptance model), ai ethics, or specifi c ai features infl uence students’ attitudes and behavioral intentions. the swift advancement of ai technology may surpass the conclusions of the study, thus requiring ongoing research. furthermore, longitudinal studies have the potential to offer a more profound comprehension of the progression of students’ perspectives and intentions as they acquire increased familiarity with ai technologies. subsequent investigations ought to overcome these constraints by broadening the range of participants, consistently incorporating the most recent advancements in artifi cial intelligence, and potentially integrating supplementary constructs, theories, and methodologies to enhance comprehension of students’ attitudes and behaviors toward technology acceptance. acknowledgments the researchers are grateful to and dedicate this research to the almighty god. references alam, a. 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(2019). edge intelligence: paving the last mile of artifi cial intelligence with edge computing. proceedings of the ieee, 107(8), 1738-1762. https://doi. org/10.1109/jproc.2019.2918951 pa ge 1 pa ge 15 american journal of applied statistics and economics (ajase) factors affecting adaptation strategies of small holder farmer’s to climate changes: evidence from dale woreda in sidama regional state, ethiopia gezahegn belguda baramo1* volume 2 issue 1, year 2023 https://journals.e-palli.com/home/index.php/ajase article information abstract received: september 25, 2022 accepted: may 15, 2023 published: may 23, 2023 the objective of this study was to analyzing factors that affect smallholder farmers’ choice of adaptation strategy and identifies adaptation measures to climate change in ethiopia using dale woreda as a case study. the data was collected from 359 sample households using a survey questionnaire and was analyzed using both descriptive statistics and econometric methods. multinomial logit model (mnl) was used to identify factors influencing smallholder farmers’ choice of adaptation strategies to climate change and variability. the adaptation strategies considered in the mnl model were crop diversification, growing drought tolerant crop, soil and water management, early and late planting and small scale irrigation practice. the result from the multinomial logit analysis showed that sex, education, farm experience, family size, farm income, farm size, distance to the market, soil fertility, access to credit, access to climate information, and extension access were significant factors influencing smallholder farmers’ adaptation strategies. a unit increases in number of years of education could increase 8.1% of the likelihood of adopting crop diversification ,1.8% of the likelihood of adopting growing drought tolerant crop and 1.2% of the likelihood of early and late planting as adaptation measures. the basic barriers to climate change adaptation on the farmers’ side are lack of credit access, lack of knowledge, lack of support from government, shortage of farm land, lack of climate information and lack of climate related problem. therefore, expanding extension service, improving the availability of credit and enhancing research on use of new crop diversification and distributing drought tolerant crop varieties and encouraging continuous climate training center, disseminating climate information by local language through social media and providing modern tool for soil and water management and small scale irrigation by government are more suited in three agro-ecological zones. keywords climate change, adaptation, adaptation strategy, multinomial logit model, dale woreda 1 furra college, yirgalem campus, sidama, ethiopia * corresponding author’s e-mail: gezahegnbelg@gmail.com introduction the impacts of climate change are experienced to changing degrees across and within countries due to contrast in exposure, susceptibility and coping capacities. small holder farmers in sub -sahara region in general and eastern africa countries in particular are facing considerable multifaceted challenges (yaro, 2013; ipcc, 2007). developing countries, particularly small island developing states, face disproportionate risks from an altered climate, while high-income countries are generally less vulnerable and more resilient. within countries, people living in poverty and other vulnerable groups including smallholder farmers, indigenous peoples and rural coastal populations are more vulnerable to climate alter and incur greater looseness from it, while having fewer resources with which to cope and recover. climate change can generate a vicious cycle of increasing poverty and vulnerability, worsening inequality and the already precarious condition of many disadvantaged groups (yenesew et al. 2015; ipcc, 2014). moreover, adaptation is critical and necessary in developing countries, especially in ethiopia where the fact that vulnerability is high. most people of livelihoods and living standard are affected by the impact of climate change. farmers with better knowledge and information on climate change and agronomic practices enable to use adaptation methods to cope up with change in climate and other socioeconomic conditions. a better understanding of the local dimensions of climatic change is also essential to develop appropriate adaptation measures that can mitigate the adverse impact of climate change. therefore, awareness of the potential benefits from any adaptation is very important issue (belay, 2017). this prediction and expectation coupled with the current situation worries all citizens especially in developing countries. for example, if we take ethiopia, the agricultural sector which is the backbone of the country’s economy is entirely dominated by smallholder farmers who are very vulnerable and sensitive to climate change related problems. thus, owing to this fact, the effort should focus on finding mechanisms in which smallholder farmers can reduce these problems and save their lives. literature review climate change is caused by the emission of greenhouse gases into the earths atmosphere through both natural processes and human activities; though growing evidence demonstrates the largest contribution is from the latter. the burning of fossil fuels, largely as a result of transportation, is the primary contributor to the emission of carbon dioxide while processes such as deforestation and industrial agriculture are the main contributors to the emission of methane and nitrous oxide compounds into the atmosphere. despite constituting less than https://journals.e-palli.com/home/index.php/ajahs pa ge 16 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 15-27, 2023 15% to total ghg emissions, methane is a very strong greenhouse gas which is 23 times stronger than co2 (fao. 2010; ipcc. 2007). agriculture is ranked as the most susceptible sector to climate change impacts and so do the livelihoods of subsistence farmers and pastoralists. climate change exerts multiple stresses on the biological, physical, social and institutional environments that affect agricultural production. its impacts disproportionately affected sub-saharan african countries including ethiopia because of the higher dependency of their economies on climate-sensitive activities such as rainfed agriculture. some of the induced changes are expected to be immediate, while others involve gradual shifts in temperature, vegetation cover and species distributions. climate change is expected to and in parts of africa has already begun to alter the dynamics of drought, rainfall and heat waves, and trigger secondary stresses such as the spread of pests, increased competition for resources, and biodiversity losses (fao. 2010). to decrease vulnerability and build the resilience of ecological and social systems and economic sectors to react current and future adverse effects of climate change in order to minimize the problem agricultural production, human health, livelihoods, food security, assets, amenities, ecosystems and sustainable development. there are many different strategies that farmers can implement to reduce the risk of climate change impacts. farmers use different adaptation strategies that match with the types of the climate related problems they faced (di falco et al 2011). this is due to the fact that impact of the climate change is not evenly distributed over different geographic areas and hence the adaptation mechanisms also vary with types and amount of the impact of climate change. therefore, we can find a number of adaptation strategies that the farmers used to reduce the impact of climate change in different literature. this includes: crop diversification, small scale irrigation ,changing crop variety, changing planting dates, mix crop and livestock production, decrease livestock, moving animals/temporary migration, change livestock feeds, soil and water management, planting trees, planting drought tolerant crop, change from livestock to crop production, change animal breeds, seek off-farm employment, planting short season crop, and irrigation/water harvesting are among some of the several strategies available to enhance social resilience in the face of climate change (deressa, t. 2009; temesgen et al. 2014) problem of the statement agriculture is the most important sector in sub-saharan africa, including ethiopia, but it is predicted to be negatively impacted by climate change. it is clear that climate change was brought about substantial welfare losses especially for smallholders whose main source of livelihood derives from agriculture. as site specific issues require site specific knowledge, it is very important, therefore, to clearly understand what is happening at community level, because farmers are the most climate vulnerable group. in the absence of such location specific studies, it is difficult to fine tune interventions geared towards achieving effective and efficient adaptation options to cope with the adverse impact of climate change at the local (lemessa, 2019; moa 2016). farmers of dale woreda are, like farmers in any other part of ethiopia, is suffering from climate upheavals which have become common natural disasters in the country. first, there has been more erratic and unreliable rainfall in the rainy seasons, bringing drought and reduction in crop yields and plant varieties; the rainfall especially in the later rains towards the end of the year has been reported as coming in more intense and destructive downpours, bringing floods, landslides and soil erosion. second, there has been a fluctuation in temperature which disturbs the physiology of crops, the micro-climate, and the soil system on which they grow. third, the crop production has been recurrently hit by erosion, and floods. fourth, annual river runoff and water availability has been reported to decrease dramatically. food insecurity in the area is a major challenge and all these climate shocks have exacerbated the negative impacts on the livelihood of poorer farm households as they have the lowest capacity to adapt to changes in climatic conditions (moa. 2016). however, farmers in the study area were responded to climate change through various adaptation strategies. but, there was no empirical data that substantiates or supports the existing adaptation strategies practiced by the farmers in the area. the information obtained in various literatures was insufficient and general, but adaptation strategies vary contextually and spatially (within communities and even within individuals).in this regard, no empirical study has been conducted to identify adaptation choices, examine the perception of farmers to climate change and their determinants in the study area to date to the best of the researchers‟ knowledge. consequently, the primary motive to embark on this research was to investigate and fill the existing gap of knowledge on farmers‟ perception and adaptation strategies to changing climate and their determinants in the study area. research gap the determination and understanding of cumulative and combined impacts of factors affecting smallholder farmer’s adaptation strategies to climate change. the most of empirical literature carried out on demographic, socioeconomic and institutional factors affecting adaptation strategies to climate change is debatable because they come up with different conclusion on the same independent variable about climate adaption strategies to climate alter. for example, regarding to age of household according to elderly farmers were more active and experienced in farming activities than youths. on the contrarily way, the investigation conducted by shows that even though there is high variability in the ages of the sampled households, generally the household within the productive age that can fully and efficiently engaged in agricultural activities than elderly people. https://journals.e-palli.com/home/index.php/ajase pa ge 17 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 15-27, 2023 research question this study was attempted to answer the following questions: 1. what are the determinant components that influence farmers’ choice of adaptation strategies to the climate change in dale woreda? 2. what are the adaptation measures that were employed by farmers in study site? 3. did farmers perceive the existing climate change? specific objective of the study 1. to analyze the factors that impact farmers’ choice of adaptation strategies to climate change in dale woreda. 2. to identify adaptation strategies used by farmers in response to adverse effects of climate change in the study area. 3. to analyze farmers‟ perception to climate change methodology determining sample is very important issue because samples that are too large may waste time, resources and money while samples too small may lead inaccurate results. as discussed in the above section, kebeles being differ in both in terms of size and variability of agro-ecological zone; they was different level of adaptation strategies to climate change. using data on climate change and small holder farmers’ characteristics the number of survey producers per division was computed according to the formula developed by yamane (1967) because it is simplest formula to calculate sample sizes and its importance in small sample size to solve resource and time constraint. where n is the sample size, n is the population size (total number of household heads in selected kebeles), and e is the level of precision. n =367 is the total sample size planned to be covered. the sample size (n) for precision (e) of 5% where confidence level is 95% will be it is assumed that the sample will have 95% reliability about population and sampling error will be 5%. the selected sample size will be identified from six kebeles by proportionate random sampling. at this stage, to give equal chance and free from selection bias, a total of 367 households of respondents was selected from the respective list of farmers which is complete list of households in each kebele obtained from the woreda administration and kebele offices in 6 kebeles by using systematic sampling technique. the list kebeles covered by size of ultimate sampling unit was determined by using proportionate sampling technique giving a size of 640,762,852,806,788 and 670 from wenenata ,hidaqalite, soyama, kalitesimita,shiifa and beera respectively. table 1: sample kebeles and sample size determination kebeles no of hh proportion of each kebele sampling for each kebele sample size wenenata 640 0.14 367*0.14 51 hidaqalite 762 0. 17 367*0.17 62 soyama 852 0.19 367*0.19 70 kalitesimita 806 0.18 367*0.18 66 shiifa 788 0.17 367*0.17 63 beera 670 0.15 367*0.15 55 total 4518 1 367 source: dale woreda rural development and agricultural office theoretical model in this study it is interesting and necessary to develop theoretical framework on farm household. this theoretical framework draws on adopting a version of model based on the random utility model as specified by (greene, h.w. 2003). this random utility model is commonly used as a framework in determining of farmers’ choice for different adaptation options. we can specify a common formulation of linear random utility model as; uij =βj xij + εij for j ∈…… (1) following greene (2003), we can modify it to adapt the objective of the study. where, i = 1,…., n are the individual farmer and j = 1,…..j are the alternative adaptation methods, xij vector are the factors that influence farmers’ choice an adaptation method to climate change and εij is the random error term /disturbance term. to elaborate the model, we assume that farmers’ are rational decision makers who maximize the utility from adaptation strategies in their farming activities. and also assuming that farmers face climatic change in their farming activities was looked for adaptation strategies. if farmer i make choice j adaptation in particular, then we assume that uij is the maximum utility among the j adaptation methods. prob (uij > uik) for all other k ≠ j. the probability of that a particular farmer will choose a particular alternative j is given by the probability that the utility of that alternative to the farmer is greater than the utility to that farmer of all other alternative j. multinomial logit model multinomial logistic regression is a simple extension of binary logistic regression (takes only two categories of the dependent) that allows for more than two categories or levels of the dependent or outcome variable and is easy, simple in calculating the choice probability and https://journals.e-palli.com/home/index.php/ajase pa ge 18 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 15-27, 2023 expressible in analytical form. mnl model is appropriate for this study because mnl model is the simplest and often preferable compared to more complex multinomial probit to identify factors affecting adaptation strategy of smallholder farmers to climate change. mnp is susceptible to a number of estimation problems, the most serious of which is that the mnp is often weakly identified in application to analyze factors affecting the choice of adaptation strategy of smallholder farmer to climate change. weak identification is difficult to diagnose and may lead to plausible, yet arbitrary or misleading inferences. the main limitation of the model is the independent of irrelevant alternative (iia) property, which states that the ratio of the probability of choosing any two alternatives is independence of the attributes of any other alternative in the choice set. the multinomial probit (mnp) model specification for discrete choice model does not require the assumption of the iia. the mnl model was used by many researchers to the model climate change adaptation practices of smallholder farmers. therefore, the multinomial logit model is appropriate to the model of climate change adaptation practice of smallholder farmers in this study area the multinomial logit model for the adaptation choice can be specified as in the following relationship between the probability of choosing option and a set of explanatory variables x [11] prob(yi = j) = equation (1) is normalized to remove indeterminacy in the model by assuming β0=0 and the probabilities can be estimated as: prob(yi = j/xi) = β_(0=0) …..(2) maximum likelihood estimation of equation (2) yields the log-odds ratio the dependent variable of any adaptation option is therefore the log of odd in relation to the basealternative. according to greene (2003), the mnl coefficients are difficult to interpret and associating theβ_j with the jth outcome is tempting and misleading. marginal effect is useful to interpret the effect of independent variable on the dependent variable in terms of probabilities. the marginal effects, measure the expected change in probability of a particular choice being made with respect to a unite change in explanatory variable. independence of irrelevant alternative (iia) test for mnl model as it is discussed earlier, the multinomial logit model requires the fulfillment of the assumption of the independence of irrelevant alternatives (iia), otherwise the model is not appropriate. different literatures suggest different ways to handling the problem of iia and to test the fulfillment of the assumption. for instance, mcfadden (1973) forwarded that models with independence of irrelevant alternative assumption should be used in cases where the alternatives can plausibly be assumed to be distinct and weighted independently in the eyes of each decision option. moreover, multinomial logit models are work well when the alternative is dissimilar. beside, two most commonways that are used to test independence of irrelevant alternative (iia) are (bazeley, p. 2009). in this model six categorical outcome tests of iia are reported here. then the study computed the model using no adaptation strategy as a base category. the study was used (tse, y.k. 1987) test of independence of irrelevant alternatives. statistical and specification tests before carry out the final model regressions, all the hypothesized explanatory variables were checked for some statistical problems such as the issue of multicollinearity. basically, multicollinearity problem may arise due to a linear relationship among explanatory variables and the problem is that, it might cause the estimated regression coefficients to have wrong signs of coefficients, smaller t-ratios for many of the variables in the regression and high r-square value. besides, it causes large variance and standard errors with a wide confidence interval. hence, it is quite difficult to estimate accurately the effect of each variable on the dependent. there are different methods suggested to detect the existence of multicollinearity problem between the model explanatory variables. correlation matrix method will be used to detect the degree of association explanatory variables. these variables are said to be collinear if the value of the coefficient correlation matrix is greater than 0.75. definitions research variables used in the model independent variable is climate change adaptation strategies. dependent variables the dependent variable for multinomial logit model of this study was adaptation strategies that the sample households employed in response to climate change. the choice of adaptation strategies was based on the actions the sample households take to counteract the negative impact of climate change/variability. from previous researches, different climate change adaptation methods have been identified. the researcher was asked numerous alternative adaptation strategies to the sample respondents and finally identified five major adaptation methods most commonly used in the area as dependent variable for the multinomial logit model. these included crop diversification, changing planting dates, use of water and soil management practices, use of drought tolerant varieties and use of irrigation. https://journals.e-palli.com/home/index.php/ajase pa ge 19 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 15-27, 2023 table 2: summary of definition, measurement and hypothesis of explanatory variables independent variable crop diversification expected sign growing drought tolerant crop expected sign soil and water management expected sign changing planting date expected sign small scale irrigation expected sign age + + + + + sex + + + + + education + + + + + farm experience + + + + + off-farm income + + + + + farm income + + + + + credit access + + + + + market distance farm size + + + + + family size + + + + + soil fertility + + + + + extension access + + + + + access to climate information + + + + + yi=β0+β1age +β2sex+β3edu+β4fex+β5ofi+ β6cra+β7c+β8mkd+β9fas+β10fms+β11sof+ β12aci+β13exa +ei………..... 4 where: yi are the climate change adaptation strategies that are currently being used to deal with climate change and others are demographic, institutional and socio economics factors. where β0 is constant, βk are regression and coefficients ei error term data analysis descriptive analysis method under this section the responses of the farm households of dale woreda was analyzed by using descriptive statistical method. the results found in this part could help for the later econometric methods in section 4.3. additionally, it is also important for analyzing some of the necessary information which is not easily captured by the econometrics methods. results and discussion background characteristics of respondents this section summarizes the demographic characteristics of respondents, which includes gender, and age. the purpose of the demographic analysis in this research is to describe the characteristics of the sample respondents accordingly, and the following tables provide the demographic profile of the respondents. from the data presented in the above table 3, the majority table 3: demographic characteristics of respondents variables categories frequency percentage sex male house hold head 195 54.3 female 164 45.7 total 359 100 age 15-30 47 13.2 31-40 134 37.3 41-50 125 34.8 51-64 53 14.7 total 359 100 source:own survey data, 2022 (54.3%) of the respondents were male and the remaining (45.7%) of the respondents were female. this indicates that out of 359 household respondent, around 195 household head were male and the remaining 164 were female. this shows that most of the household head of the farmers are male. as indicated in table 3 among the total gathered questionnaire, 13.2% of the respondents were found to be in the age category of 15-30 years. the respondents who compose of 37.3% are in the age category of 31-40 years, 34.8% in the age categories of 41-50, and 51-64 are 14.7 this data indicated that most of the respondents categorized between age group of 31-40. https://journals.e-palli.com/home/index.php/ajase pa ge 20 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 15-27, 2023 table 4: social characteristics of respondents variables categories frequency percentage education status illiterate 178 49.5% 1-6 87 24% 7-12 64 17.8% certificate/diplom 22 6.2% degree 8 2.5% total 359 100 respondent farm experience 1-10 97 27% 11-20 111 30.9% 21-30 110 30.6% >30 41 11.5% source: own survey data, 2022 educational status as shown in the above table 4, the majority of the respondents were grouped under the educational level of respondent are illiterate covering 49.5% of the total respondents, followed by 1-6 composes 24%. the respondents were categorized under the educational level of 7-12 who covers 17.8%, and 6.2% and 2.5% are certificate/diploma and degree holder respectively. this shows that most of the smallholder farmers are illiterate. from the above table 4, indicated that 30.9% smallholder farmer have experience of between 11-20 years; 30.6% had an experience of 21-30 years; 27% had between 1-10 years and the remaining 11.5% of the respondents had experience of greater than 30 years with in farm. this indicated that majority of respondents had found under 11-20 years’ of farm experience. table 5: economic characteristics of respondents variables categories frequency percentage off farm income no off farm income 218 60.7 500-1000 63 17.5 1001-1500 32 8.9 1501-2000 20 5.6 >2000 26 7.3 total 359 100 farm income no income 44 12.3 500-1000 birr 186 51.8 1001-1500 birr 96 26.7 1501-2000 birr 17 4.7 >2000 16 4.5 total 359 100 source: own survey data, 2022 considering the off farm income characteristics of the respondents, majority of the smallholder farmer have no off farm income; who fall to 60.7%, followed by respondents 17.5%, 8.9%, 5.6% and 7.3% who off farm income 500-100,1001-1500,1501-200 and greater than 2000 respectively. the income category clearly shows the majority(51.8%) of the smallholder farmer have farm income between 500-1000and 12.3% of the respondent have no farm income and the remaining 26.7%, 4, 7% and 4.5% have income between 1001-1500, 1501-2001 and greater than 2000 respectively. the above table shows that, about 39.3% of the respondents from weyna-dega agro ecological zone had used crop diversification as primary choice of adaptation strategy to climate change followed by soil and water management (15.3%), 15.3%, 7.4% and 4.9% had practiced soil and water management, changing planting date and growing drought tolerant crop respectively and on other side; only 4.3% had used small scale irrigation as least used adaptation strategy. the result indicated that most of smallholder farmers used crop to reduce consequences of climate change in the weyna-dega agro-ecological zone of study site. https://journals.e-palli.com/home/index.php/ajase pa ge 21 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 15-27, 2023 table 6: cross tabulation of agro-ecological zone and adaptation decision agro-ecological zone types of adaptation strategy practiced number of respondents who took strategy type of adaptation practice in percent (%) woyna-dega crop diversification 64 39.3% growing drought tolerant crop 8 4.9% soil and water management 25 15.3% changing planting date 12 7.4% small scale irrigation 7 4.3% dega crop diversification 11 17.2% growing drought tolerant crop 2 1.2% soil and water management 21 33.3% changing planting date 4 6.9% small scale irrigation 3 4.8% kola crop diversification 4 3% growing drought tolerant crop 56 42% soil and water management 18 13.1% changing planting date 7 5.3% small scale irrigation 16 12% source: own survey data, 2022 summary statistics for explanatory variables the above table shows that about 33.3% of the respondents from dega agro-ecological zone had used soil and water management as their major choice of adaptation strategy to cope with the impact of climate change followed by crop diversification (17.2%) and growing drought tolerant crop is least choice of adaptation strategy to moderate climate change in dega agro-ecology of the woreda. table 7: sum agehh sex fex edu ofi fai cra mkd fms fas sof aci exa independent variables description summary statistics mean std.dev. min max age of household continuous variable 2.5125 .8995554 1 4 sex dummy 1 if male, otherwise .84958220 .357979 0 1 farm experience continuous in year 2.264624 .9828995 1 4 education continuous .8718663 1.051875 0 4 off farm income in etb (continuous variable) 1.785515 1.26677 0 5 farm income 2.37325 .9184382 1 5 credit access dummy 1 if yes, 0 otherwise .6991643 .4592615 0 1 distance from farm to market farm continuous in km 2.509749 .9271146 1 5 farm size continuous in hectare 2.34818 .9680314 1 4 family size continuous 2.07799 .942207 1 4 soil fertility categorical 1 very fertile,2 for moderate fertile and 3 infertile 1.793872 1.793872 1 3 access to climate information dummy 1 if yes, 0 otherwise .5933148 .4919008 0 1 extension access dummy 1 if yes, 0 otherwise .6406685 .4804742 0 source: own survey data, 2022 https://journals.e-palli.com/home/index.php/ajase pa ge 22 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 15-27, 2023 factors that affecting smallholder farmers’ choice of adaptation strategies in the study area estimated results of the multinomial logit regression modelshowed how factors that affecting farmers’ choice of adaptation strategies in the study area. the mnl adaptation model was run and showed that significant levels of the parameters estimates. table 8 represents that the results of mnl regression model. the likelihood ratio statistics as indicated by ch2 statistics (lr chi-square (65) = 649.46 and pseudo r2 = 0.5421 are highly significant p < 0.0000), explaining the model has a strong explanatory power. in all cases, the estimated coefficients should be compared with the base category of no adaptation. therefore, table 8 presents the mnl results along with the levels of statistical significance. coefficient from the multinomial logit model can tell table 8: parameter estimates of the multinomial logit climate change adaptation model explanatory variable crop diversification growing drought tolerant crop soil and water management changing planting date small scale irrigation coef coef coef coef coef p-value p-value p-value p-value p value age 0.154 0.063 0.358 0.275 0.032 0.639 0.867 0.33 0.715 0.931 sex -1.371 6.03 6.836 12.163 -14.983 0.434 0.001** 0.00** 0.000** 0.990 education 1.285 2.067 1.149 1.34 0.879 0.000** 0.000** 0.003** 0.037** 0.029** farm experience 2.314 1.316 2.162 2.296 1.852 0.000** 0.001** 0.000** 0.009** 0.001** off farm income 0.876 0.039 1.149 0.637 0.185 0.017** 0.924 0.003** . 0.227 0.682 farm income 3.222 3.245 2.505 1.216 0.575 5.533 0.000** 0.007** 0.018** 0.574 0.000** credit access 2.558 3.407 4.125 -0.444 2.379 0.005** 0.001** 0.000** 0.811 0.032** market distance -2.804 -2.965 -3.166 -3.12 -3.987 0.000** 0.008** 0.001** 0.139** 0.000** farm size 4.016 4.276 4.786 6.447 4.076 0.000** 0.000** 0.000** 0.000** 0.000** family size 0.701 0.985 1.25 1.519 1.419 0.072** 0.030** 0.004** 0.207** 0.002** soil fertility 1.53 4.101 3.773 1.734 1.589 0.020** 0.000** 0.000** 0.173** 0.032** access to climate information 1.381 1.791 2.349 -1.683 0.96 0.063** 0.027** 0.004** 0.316 0.24 extension access 3.222 2.393 3.328 2.903 18.686 0.000** 0.008** 0.000** 0.085*** 0.979 base category no adaptation number of observation 359 lrchi2(65) 649.46 prob > chi2 0 loglikelihood -274.30607 pseudo r2 0.5421 nb *, **, *** = significant at 1%, 5%, and 10% probability level, respectively source; own survey data 2022 https://journals.e-palli.com/home/index.php/ajase pa ge 23 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 15-27, 2023 about the direction effect not the magnitude effect. how to compute the magnitude of effect by using stata-15 command mfx after multinomial logit regression and it results marginal effect. marginal effect of marginal probabilities is the function of probabilities and measures the expected change in probabilities where particular adaptation choice is being made by a unit change of the independent variable from the mean. table 9: marginal effects of explanatory variables from multinomial logit model independent variable crop diversification growing drought tolerant crop soil and water management changing planting date small scale irrigation marginal effect marginal effect marginal effect marginal effect marginal effect age -.0143 -.022 .041 .000 -.000 sex .688 -.070 -.334 -.330 .001 education .081 .018 -.000 .012 -.000 farm experience .034 .017 .016 .011 -.000 off farm income .193 .091 -.087 .000 -.000 farm income .141 .053 -.107 -.003 .000 credit access .133 .093 .232 -.015 -.000 market distance .018 -.023 -.076 -.005 -.000 farm size .064 .027 .152 -.004 .002 family size -.089 .022 .090 -.001 .014 soil fertility .528 .304 .297 .000 .012 access to climate information .122 .033 .160 0.013 .001 extension access .154 -.098 087 .000 .019 (*) dy/dx is for discrete change of dummy variable from 0 to 1. source: own survey data 2022 interpretation of regression result sex of household head from the result of multinomial logistic regression the coefficients of sex of household head are negative and statistically not significant for crop diversification and small scale irrigation, keeping other variables constant. being female household head is better in practice of crop diversification and small scale irrigation as adaptation measure with (p< 0.434) and (p<0.099) respectively at 5% level of significance in the study area. being male household head has positive and highly significant effect on adapting strategy like growing drought tolerant crop, for soil and water management and changing planting date to climate change impact with probability of p<0.001, p<0.000 and p<0.000 at 5% level of significance in the study site. based on the result of marginal effect, even if the effects of sex on the probabilities of three of the strategies are negative and do not suggest important information, this could be an indication of the different implications of sex on adaptation measure. given these situations, the result is justified with the possibility that being male and female farmer usually practice of adaptation measures which can be practiced to increase farm labour. education level of smallholder farmer the result of multinomial logistic regression shows that education had a positive effect on farmers’ adaptation strategies and statistically significant in increasing adaptation strategies of crop diversification, growing drought tolerant crop, soil and water management, changing planting date and small scale irrigation with respective p-value p<0.00, p<0.00, p<0.03, p<0.037 and p<0.029. education is highly significant in determining crop diversification and growing drought tolerant crop at 5% level of significance keeping other variables constant. farmers who have more education level were more likely to adapt to climate change using crop diversification and growing drought tolerant crop practices than those who do have lower education level. this result might source of the fact that education improves farmers‟ capacity of obtaining and analyzing new information about climate change and best adaptation practices that increases the probability of adapting to climate change. more specifically, it equipped farmers with knowledge of selecting appropriate crop diversification and drought tolerant variety. the result of from marginal effect shows that a unit increases in number of years of education could increase by 8.1% of the likelihood of adopting crop diversification and 1.8% practice of growing drought tolerant crop as adaptation measures at 5% level of significance. farm experience of household in this study the coefficient of household farm experience is positive and statistically significant in determining adaptation measure such as crop diversification, growing drought tolerant crop, soil and water management, https://journals.e-palli.com/home/index.php/ajase pa ge 24 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 15-27, 2023 changing planting date and small scale irrigation with p<0.00, p<0.001, p<0.00, p<0.009 and p<0.001 respectively in the study site. the result of marginal effect shows a unit increases in number of years of farm experience could increase by 1.4% of the likelihood of adopting crop diversification, 5.7% of growing drought tolerant crop and 1.6% of soil and water management as adaptation measure at 5% level of significance. the number of years a farmer has spent cultivating crops on a farm is considered as his/her agricultural experience. possessing many years of farming experience implies that one is better informed about climate variability and change in relation to crop produce, in the study areas; hence, experienced farmers are likely to use adaptation strategies which had reduced the effects of change and improved crop production. off-farm income off-farm income is significantly and positively affected crop diversification and the practice of soil and water management with p<0.01 and p<0.03 at 5% level of significance. the result of marginal effect shows a unit increases in number of years of farm experience could increase by 1.9% of the likelihood of adopting crop diversification. the higher farmers have off-farm income, the more likely they were to adapt climate change using crop diversification and soil and water management. perhaps the reason is farmers have had an optional income source that help them withstand the impact of climate change and they are also capable of buying instruments for soil and water management. farm income coefficients of farm income from result of multinomial logistic regression is positive and statistically significant in determining the practice of adaptation measure such as crop diversification, growing drought tolerant crop, soil and water management and the use small scale irrigation at 5% level of significance with p-value 0.000, 0.007 ,0.001 and 0.000 respectively. the result of marginal effect shows a unit increases in number of years of farm experience could increase by 1.4% of the likelihood of adopting crop diversification, 5% of growing drought tolerant crop at 5% level of significance. this implied that smallholder farmers who have higher farm income are more likely to adapt to the change in climate using these strategies. when the main source of income in farming increase, farmers tend to invest on productivity smoothing options such as using crop diversification and growing drought tolerant crop. farm income enables the farmer to perceive and adapt to climate change by devoting higher cash for the purchase of seed and seedlings whenever the rain comes, buying a drought tolerant variety and apparatus for the use of soil and water management practice and irrigation at higher price. credit access the multinomial regression model revealed that farmers access to credit has a statistically significant positive effect on using of crop diversification, growing drought tolerant crop, soil and water management and using small scale irrigation at 5% level of significance, keeping another variable constant. farmers who have access to credit are more likely to adapt climate change by practicing these adaptation strategies. a unit increase of access to credit could increase likelihood of crop diversification by 13.3%, growing drought tolerant crop by 9.3%, soil and water management by 23.2%. the result showed that having access to credit increases the propensity of farmers to apply the four adaptation strategies in response to climate change. this is due to the fact that access to affordable credit mitigates the financial limitation of the farmer and increases their ability to meet transaction costs associated with the various adaptation options they might want to take. it enables farmers to change their management practices in response to changing climatic factors and to buy varieties for crop diversification and drought tolerant varieties, tool/ instrument for soil and water management and small scale irrigation irrigation technologies like water pumps and other inputs to smoothening production and reduce the negative impact of climate change. market distance this variable is a continuous variable measured in kilometers from farmers home/farm to their market and coefficients of market distance is negative and statistically significant in determining crop diversification, growing drought tolerant crop, soil and water management and using small scale irrigation at 5% level of significance with p-value 0.000, 0.008, 0.001 and 0.000 respectively, keeping other variables constant. market is an important determinant to buy input for crop diversification, growing drought tolerant crop, soil and water management practices and small scale irrigation tool to take adaptation measure to climate change, most probably reason the market serves as a means of exchanging information with other farmers. moreover, access to inputs and transportation cost will be high for households far from a given market. farm size landholding size highly significant and positively affected use of crop diversification, growing drought tolerant crop, soil and water management, changing planting date and small scale irrigation in response to climate change at 5% level of significance with all p-value 0.00, keeping other variables constant. the bigger the landholding, the more likely the farmer is to adopt crop diversification, growing drought tolerant crop, soil and water management, changing planting date and small scale irrigation. the possible reason is that farmers who have bigger farm size have an option to divide their farm into different enterprises. from results of marginal effect shows a unit increase in farm land by hectare could increase the likelihood of adopting crop diversification by 6.4%, growing drought tolerant crop by 2.7%, soil and water https://journals.e-palli.com/home/index.php/ajase pa ge 25 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 15-27, 2023 management by 1.5% and small scale irrigation by 0.02%. family size family size also has statistically significant and positive effect on adaptation strategies to climate alter. a unit increase of number of family size, could increase likelihood of using crop diversification by 7.2%, growing drought tolerant by 0.3%, soil and water management by 0.04% and small scale irrigation by 0.02% as adaptation measure respectively, keeping other variables constant. because household size can influence adaptation, due to the fact that its association with labor endowment. soil fertility soil fertility has statistically significant and positive effect on adaptation strategies to climate alter. from marginal effect result, a unit increase of number of soil fertility , could increase likelihood of using crop diversification by 5.2%, growing drought tolerant by 3%, soil and water management by 2.9% and small scale irrigation by 0.02% as adaptation measure respectively, than infertile soil keeping other variables constant. fertile soil make easy farmers production during climate change adaptation measure such as crop diversification, growing drought tolerant crop and soil and water management. access to climate information access to climate information significantly and positively affected using of drought tolerant varieties and using soil and water management at 5% level of significance. farmers who have access to climate related information from different media like radio and television have a higher probability of awareness creation in using drought tolerant varieties and using soil and water management as an adaptation strategy to reverse climate change. most likely, the reason is that access to climate information admits to perceive the change and choose appropriate strategies in response to climate change. climate information notifies the condition of the existing climatic situations to enable the farmers to use alternative adaptation strategies like drought tolerant variety and soil and water management. from result of marginal effect unit increase climate information, could increase likelihood of using crop diversification by 12.2%, drought tolerant crop by 3.3%, and soil and water management by 16.1%. extension visit is also among the positive and significant explanatory variable in this model. a result from marginal effect, those who have access to farm extension service expected to have had unit increase of farm extension service could increase likelihood of using crop diversification adaption methods by 15.4%, growing drought tolerant crop by 8.7% and using small scale irrigation by 1.9% as compared to the farmers who have no access to farm extension service to handle climate change at 5% level of significance. conclusion multinomial logistic regression analysis was employed to analyze the factors influencing smallholder farmers’ choice of adaptation strategies to climate change. the result from the multinomial logit regression analysis shows that sex, education, farm experience, off-farm income, credit access, market distance, farm size, family size, soil fertility, access to climate information and extension access have a significant influence on smallholder farmers’ choice of adaptation strategies to climate change and age is not significant to climate change. the strategy of crop diversification was positively affected by education, farm experience, off-farm income, credit access, farm size, family size, soil fertility, soil fertility, climate information and extension access, while sex and market distance negatively affect crop diversification. the unit increase in education, farm experience, off-farm income, credit access, farm size, family size, soil fertility, climate information and extension access will increase the practice of crop diversification likelihood of 6.8%, 8.1%, 3.4%, 1.7%, 14.1%, 6.4%, 5.3%, 1.2% and 1.5%. the unit increase in kilometer of market distance from farm to market could decrease likelihood of 1.8% use of crop diversification. growing drought tolerant crop was positively affected by education, farm experience, off-farm income, farm income, credit access, farm size, family size, soil fertility, access to climate information and extension access, while growing drought tolerant crop negatively affected by market distance. the unit increase in education, farm experience, farm income, credit access, farm size, family size, soil fertility and access to climate information could increase adoption of growing drought tolerant crop by 1.8%, 1.7%, 5.3%, 9.3%, 2.7%,2.2%, 3.04% and 3.3%. soil and water management as climate change adaptation strategy was positively affected by farm experience, farm income, credit access, farm size, family size, soil fertility, access to climate information and extension access, while sex and market distance negatively affect soil and water management.the unit increase of farm experience, farm income, credit access, farm size, family size, soil fertility, access to climate information and extension access could increase likelihood of 1.6%, 23.2%, 15.2%,9%,29.7%, 16% and 8.7% practicing soil and water management. the strategy of changing planting date positively affected by education, farm experience and farm size while sex negatively affect the strategy changing planting date.the unit increase of education, farm experience and climate access information could likelihood of adopting strategy of changing planting date 1.2%, 1.1% and 1.3%. small scale irrigation as climate change measure was positively affected by credit access, farm size and family size while small scale irrigation negatively affected by market distance. the unit increase of farm size and family size 0.02%, 1.4% and 1.2% could increase likelihood adopting small scale irrigation. https://journals.e-palli.com/home/index.php/ajase pa ge 26 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 15-27, 2023 recommendation based on the findings of this study, the following recommendations were forwarded for decreasing the impacts of climate change on smallholder farmer agricultural production. encouraging efforts on enhancing the farmers’ adaptive capacity to climate change and variability is an important policy measurement. the government and any concern body should give emphasis to address this issue of climatic change through paying greater focus. in the study area problem of food shortage is a common by the farmers of this woreda especially when there is crop failure due to increased frequency of farm destruction, increased frequency of drought, off-seasonal rainfall and little rainfalls. therefore, effort and strengthen the farmers’ adaptive capacity to climate change through by providing different varieties for crop diversification and drought tolerant crop,introducing climate change problem and use of adaptation strategy into educational curriculum, organizing continuous climate related training center, providing modern tool for soil and water management and small scale irrigation and disseminating climate related information by local language through social media for farmers to enhance awareness has an important mechanism. due to the availability of different micro climate in the area and to improve the coverage and quality of climatic data local meteorology station should be established at least at woreda level. and it is important for monitoring climate data, developing climate forecasts and early warning for climatic hazards as early as possible. the level of perception farmers to climate change has a very important role on the level of using adaptation strategies to lessen the effect of climate alter. but there are still a considerable number of smallholder farmers who did not perceive the changing climate. therefore, emphasizing on awareness creation work about the changing climate is crucial. improving farmer’s farm and off/non-farm incomeearning opportunities is of great need for smallholder farmers. thus, sufficient input supply which increases farm income and creation of off/non-farm employment opportunities in the rural areas can be underlined as a policy option in the reduction of the negative impacts of climate change. improve farmers access to affordable credit and support the growth and development of credit institutions and it is important to consider its accessibility to farmers nearby their locality and of other income generating activities to increase their ability and flexibility in response to climate change. policy interventions aimed at mitigating the adverse effect of climate alter and variability need to focus on supporting farmers to intensively use and expand the existing adaptation strategies in the way match with agroecology: by using crop diversification, growing drought tolerant crop, soil and water management, changing planting date and small scale irrigation practices. electing model farmers of adopter and making them to share their experiences to the non-adopter of farmers is very important to promote adaptation in the community. this encourages promotion of a given adaptation strategies should consider the agro-ecological setting of the area special consideration for successful use of adaptation measures by smallholder farmers. policies aimed at promoting farm-level adaptation need to emphasize on the crucial role of providing information on better production techniques and enhancing farmers’ awareness on climate change (through extension) and creating the financial means through affordable credit schemes to enable farmers to use different adaptation measures to climate change. including climate change related agenda in education curriculum will increase the continuous knowledge about the use of adaptation strategy as a culture because it is necessary to design and implement policies that aim to expand adult education so that improve education level of farmers. literate farmers could be able to easily collect, analyze and interpret relevant information about climate change and adaptation strategies. it will enable them to select appropriate adaptation strategies and farming practices to manage climate change impacts. hence, it is essential to improve education level of farmers through expansion of adult schools and crafting systems that allow farmers to get education. generally, future policy should focus on awareness creation on climate change through different sources such as media and extension, facilitating the availability of credit especially to adaptation technologies, enhancing research on use of new crop varieties that are more suited to drier conditions, improving farmers farm and off-farm income earning opportunities, improving their literacy status, and improving their access to credit. moreover, encouraging informal social net-works and environmental settings enhance the adaptive capacity of smallholder farmers. information on appropriate adaptive measures should be made available to the entire community. as part of this effort, communication between policymakers, research institution,universities, and the media, among other actors, should be strengthened in order to ensure accurate information is available and widely disseminated. reference belay, (2017). farmers’ perception and adaptation strategies to climate change: the case of woreillu district of amhara region, northeastern ethiopia,haramaya university, haramaya di falco, s. veronesi, m., & yesuf, m. (2011). does adaptation to climate change provide food security? a micro-perspective from ethiopia, american journal of agricultural economics, 93(3), 829-846. deressa, t. (2007). measuring the economic impact of climate change on ethiopian agriculture: ricardian approach. world bank policy research 4342. washington d.c. world bank deressa, t. (2009). measuring the economic impact of https://journals.e-palli.com/home/index.php/ajase pa ge 27 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 2(1) 15-27, 2023 climate change on ethiopian agriculture: ricardian approach. ceepa discussion 21. south africa: ceepa, university of pretoria. temesgen, d., yehualaeshet, h., & rajan, d.s. (2014). indications for food security and scarce resource : department of economics university of addis ababa. eisenack, k, &stecker, r. (2012), a framework for analyzing climate change adaptations as actions. mitigation and adaptation strategies for global change, 17(3), 243-260. fao. (2010). fn24, world bank. 2010. opportunities and challenges for a converging agenda: country examples. (prepared for the 2010 the hague conference on agriculture, food security and climate change). econometrica, 52(5), 1219–40. ipcc. (2007). climate change, impact, adaptation and vulnerability. fourth assessment report of the intergovernmental panel on climate change cambridge university press cambridge. ipcc. (2014). climate change 2014: impacts, adaptation, and vulnerability, part a: global and sectoral aspects. contribution of working group ii to the fifth assessment report of the intergovernmental panel on climate change, cambridge: cambridge university press, 1132. i greene, h.w. (2003). econometric analysis, 5th edition, pearson education, inc., upper saddle river, new jersey, usa, 720-723. lemessa, (2019). cogent food & agriculture (2019). 5164083. https://doi.org/10.1080/23311932 .2019.1640835 moa, (ministry of agriculture). (2016). ethiopia’s agriculture sector policy and investment framework. bazeley, p. (2009). analysing qualitative data: more than‘ identifying themes, malaysian journal of qualitative research, 2(2), 6–22. yaro, j. a. (2013). building resilience and reducing vulnerability to climate change: implications for food security in ghana. accra: department of geography and resource development, university of ghana. yenesew, s., eric, n. o., & fekadu, b. (2015). determinants of livelihood diversification strategies: the case of smallholder rural farm households in debre elias woreda, east gojjam zone, ethiopia. african journal of agricultural research, 10(19), 1998–2013. doi:10.5897/ ajar2014.9192. tse, y. k. (1987). a diagnostic test for the multinomial logit model. journal of business and economic statistics, 5(2), 283–86. https://journals.e-palli.com/home/index.php/ajase pa ge 1 pa ge 1 american journal of applied statistics and economics (ajase) effective health care plan for national health insurance scheme patients with non-communicable diseases in plateau north senatorial district philemon polycarp davwar1* volume 2 issue 1, year 2022 https://journals.e-palli.com/home/index.php/ajase article information abstract received: february 25, 2023 accepted: may 08, 2023 published: may 14, 2023 patients of non-communicable diseases (ncds) are usually placed on a life-long prescription or procedure. this is sometimes a problem in itself. they could get tired since they are in most cases, not sickly, they could get careless as a result of familiarity or boredom, etc. the greatest challenge is that they could decide not to access care or continue to access care at a particular location for several reasons which may be culturally, politically, or socially influenced. consequently, their situation could become more complicated. the location of access points to healthcare for them becomes a critical issue here. in this research, we looked at location of healthcare access points for ncd patients living in plateau north senatorial district who are also registered with the national health insurance scheme (nhis). data on specialist opinions as to the relevance of each specialist and equipment for the effective management of the associated ncd was collected and analysed to determine relevance ratings. these ratings along with data on availability of specialists and equipment from each care provider registered with the nhis in the study area, was analysed using the patientbased set covering location model (davwar, wajiga & okolo, 2021). the results show care providers that can provide healthcare services to such patients at a quality level t, the threshold value for patients in each location, below which a service provider is not considered. this was done for each ncd (diabetes and cancer) under consideration. in the analysis, each ncd was considered a scenario. for each patient of diabetes and or cancer, in each location in the study area, this research determines points of access to quality care. keywords patient-based facility location, non-communicable diseases, diabetes, cancer, set covering location models 1 department of mathematics and statistics, federal polytechnic idah, kogi state, nigeria * corresponding author’s e-mail: ppdavwar@gmail.com introduction patients of chronic non-communicable diseases are usually placed on a life-long prescription or procedure. this is sometimes a problem in itself. they could get tired since they are in most cases, not sickly, they could get careless as a result of familiarity or boredom, etc. the greatest challenge is that they could decide not to continue to access care at a particular location for a number of reason which may be cultural, political, or social. they are mostly healthy looking and so may feel uncomfortable that other people know that they are sick. they may prefer to pretend they have no problems. they most times prefer to access medical care in some concealed manner. so this study attempts to locate points of access to healthcare service for them. since this is a medical problem, it is required that a total coverage is achieved. ordinarily, the classical set covering facility location model would suffice, except that if the classical model optimizes well, a patient will be assigned to one point of healthcare service only. therefore, if this patient has other psychological considerations but compelled to access care in this one facility prescribed by the model, he may do so but suffer from a psychological stress which may complicate the management of his case over time. the objective of this research is to locate points of access to health care services for patients suffering from either diabetes or cancer and who reside in any of the local government areas in plateau north senatorial district. these points of access provide ‘cover’ for each of these patient locations at a quality level of at least a threshold value as prescribed by the patient-based facility location model (davwar, wajiga & okolo, 2021). literature review ncds results from a mixture of genetic, physiological, environmental and behavioural factors with pronounced dangers because of its chronic nature. annually, its global mortality is 41 million people, which accounts for 70% of global deaths. approximately 40% of these deaths occur among people aged between 30 and 69 years (who, 2018a), while 80% of these early deaths occur in low and middle-income countries (who, 2018b). global health observatory data in 2018 predicted that deaths from ncds would rise to about 52 million worldwide in the year 2030 (who, 2020). of all ncds, cardiovascular disease accounts for about 40% of all deaths annually while cancers, respiratory diseases and diabetes account for 22%, 10% and 4% respectively. these four diseases similarly account for over 80% of all premature deaths (olukoya, 2017). in a who report, the probability of dying prematurely from ncds in nigeria is put at 20% (who, 2018c) while the projected prevalence estimate of diabetes in nigeria is 4.04% (idf, 2011). according to the 2012 globocan data, nigeria’s top five cancer burdens are breast, cervix uteri, liver, prostate and colorectal cancers (awodele, adeyomoye, awodele, fayankinnu & dolapo, 2011). workable and evidence-based solutions must be provided (ezzati & riboli, 2012) to relieve the burden of ncds in nigeria which is the aim of this research. current, https://journals.e-palli.com/home/index.php/ajahs mailto:ppdavwar@gmail.com pa ge 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-6, 2023 daskin, & david (2001) maintained among others that location decisions are often strategic in nature, frequently impose economic externalities and often extremely difficult to solve. he further puts it that there does not exist a general location model that is appropriate for all potential or existing applications. therefore, different models are developed for different location decisions. the model employed in this study davwar, wajiga & okolo (2021) was developed to cater for the peculiarities of patients with ncds. it will be recalled that diabetes and cancer are both ncds. they are probably the most common chronic health challenges in the contemporary society. any research that could lead to an enhanced management of ncds is very needful because ncds are an important contemporary health issue, and is growing in importance, because: i. a person’s social circumstances affect the chance of him/her having a ncd greatly. so, the chances are right that more people will come down with one ncd or the other. ii. some patients have multiple ncds, which make their care particularly complex. iii. ncds usually have a mild beginning, a simple social habit, or what appears to be a normal life, but gradually grow into a life-threatening monster. iv. there is evidence that ncds can be better managed through increased ease of access to special medical help. methodology data from plateau north senatorial district on diabetes and cancer were collected for this study. data on availability of specialists and equipment/procedures were collected from all nhis service providers in the district and used for the analyses. the microsoft excel solver was used to analyse the data. the analyses and results are presented below: the patient-based set covering facility location model developed by davwar, wajiga & okolo (2021) was used in this analyses. given a set of nhis service providers in the study area, ni= {j\qjis≥ts }, ∀j ∈ j, ∀i ∈ i and for each scenario s, s= 1,2 and given that: ni is a set of facilities capable of offering service to patients in location i with scenario s at a quality level of at least ts where: ts= quality threshold for scenario s. qji= 1/dij{fjs+ ejs } (1) fjs= rating of specialists at service point j for management of scenario s. ejs= rating of equipment at service point j for management of scenario s. qjis= quality of service facility j can provide to patient location i with scenario s. and dij= distance from patient location i to hospital j then the minimum number of health facilities zs that can provide coverage for patients with chronic condition results and discussion to evaluate equation (1) we require the following: the specialists and equipment/procedure ratings (fjs and ejs) respectively for every specialist and equipment/procedure using the data collected a score was determined for each specialist and equipment/procedure respectively, as a measure of their relevance to the management of a particular condition (scenario). the rating which is from a five-point scale is the average rating from the responses of professionals in the field of health care provision (doctors, laboratory scientists, pharmacists, others) on the relevance of a given specialist or equipment/ procedure in the management of the particular chronic challenge as presented in table 1 below: table 1: showing specialists and equipment/procedure ratings scenario specialists fjs equipment ejs 1. diabetes dietician 5.00 spectrophometer 4.83 endocrinologists 4.80 glucometer 4.80 pharmacists 4.80 insulin infusion pump 4.08 lab. scientists 4.71 emergency rm med 4.50 dialysis nurses 3.45 2. cancer oncologists 4.97 radiation machines 4.66 pathologists 4.59 microscopes 4.53 surgeons 4.46 ct scan mri 4.24 s in all patient locations is given as: where: zs= minimum number of facilities required to offer complete coverage to all patient locations with scenario s. j= the set of eligible nhis facilities (indexed by j) i= the set of patients’ locations (indexed by i) https://journals.e-palli.com/home/index.php/ajase pa ge 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-6, 2023 pharmacists 4.44 immunohistochemistry 4.21 psychologists 3.47 x-ray machine 3.83 psychotherapists 3.15 *key to abbreviations in table above: ct computerized tomography, mri magnetic resonance imaging determination of health service centre potentials (total scores for specialists and equipment/ procedure availability (fjs+ ejs)) data on the availability of specialists and equipment/ procedure at each facility was collected and used to determine specialists’ availability score fjs and equipment/procedure availability score ejs (ie potential) for each facility as presented in table 2 below: table 2: hospital potentials (total scores for specialists and equipment/procedure availability (fjs+ ejs) s/n hospital diabetes cancer 1 pl/0057 14.29 14.02 2 pl/0163 14.29 14.02 3 pl/0003 14.29 14.02 4 pl/0172 14.29 14.02 5 pl/0171 14.29 14.02 6 pl/0150 27.21 30.78 7 pl/0014 14.29 14.02 8 pl/0103 14.29 17.86 9 pl/0159 14.29 14.02 10 pl/0116 14.29 14.02 11 pl/0169 14.29 14.02 12 pl/0100 41.13 40.60 13 pl/0168 14.29 14.02 14 pl/0010 14.29 17.86 15 pl/0067 14.29 14.02 16 pl/0079 14.29 14.02 17 pl/0021 14.29 14.02 18 pl/0008 14.29 14.02 19 pl/0075 22.71 26.28 20 pl/0104 14.29 17.86 21 pl/0170 14.29 14.02 22 pl/0009 14.29 17.86 23 pl/0076 14.29 14.02 24 pl/0064 14.29 14.02 25 pl/0120 14.29 14.02 26 pl/0176 14.29 14.02 27 pl/0058 14.29 14.02 28 pl/0019 14.29 14.02 29 pl/0018 14.29 14.02 30 pl/0115 14.29 14.02 31 pl/0111 14.29 14.02 32 pl/0077 14.29 14.02 33 pl/0020 14.29 14.02 34 pl/0066 14.29 17.86 35 pl/0158 28.21 31.94 36 pl/0007 14.29 14.02 37 pl/0057 14.29 14.02 https://journals.e-palli.com/home/index.php/ajase pa ge 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-6, 2023 38 pl/0153 14.29 14.02 39 pl/0023 14.29 14.02 40 pl/0108 14.29 14.02 41 pl/0117 14.29 14.02 42 pl/0080 14.29 14.02 43 pl/0139 14.29 14.02 44 pl/0148 14.29 14.02 45 pl/0161 14.29 14.02 46 pl/0165 14.29 14.02 47 pl/0106 14.29 17.86 48 pl/0122 14.29 14.02 49 pl/0164 14.29 14.02 50 pl/0107 14.29 14.02 51 pl/0167 14.29 14.02 52 pl/0102 23.79 27.36 53 pl/0162 14.29 14.02 54 pl/0141 14.29 17.86 55 pl/0121 17.71 21.28 56 pl/0001 14.29 14.02 57 pl/0071 14.29 14.02 note: see appendix iii for key to hospital codes. determination of the quality of facility j to handle patients in location i with scenario s. (qjis) we required the distances from the patient location to the various potential facilities (appendix ii) and the potentials (∑(ejs+fjs)) of these facilities to handle each of the chronic conditions. the potentials were appropriately combined with the distance factors (dij) to form the quality level of facility j to handle patients in location i, with scenario s. this quality level of facility j to handle patients in location i, with scenario s is computed as distance weighted. this is because of the negative effect distance has on the quality of service. it is computed for the two scenarios (diabetes and cancer) therefore as: qjis= 1/( dij ) {ejs+ fjs } and presented in 3a (for diabetes) and 3b (for cancer) below: table 3: quality level of facility j to handle patients in location i with scenario s. (qjis) diabetes cancer hospital b as sa jo s n th jo s e st jo s st h b /l ad i r iy om b as sa jo s n th jo s e st jo s st h b /l ad i r iy om pl/0057 4.76 0.41 0.19 0.27 0.18 0.17 4.67 0.40 0.19 0.27 0.18 0.17 pl/0163 4.76 0.41 0.19 0.27 0.18 0.17 4.67 0.40 0.19 0.27 0.18 0.17 pl/0003 4.76 0.41 0.19 0.27 0.18 0.17 4.67 0.40 0.19 0.27 0.18 0.17 pl/0172 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0171 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0150 0.78 9.07 0.70 1.60 0.60 0.58 0.88 10.26 0.79 1.81 0.68 0.65 pl/0014 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0103 0.41 4.76 0.37 0.84 0.32 0.30 0.51 5.95 0.46 1.05 0.40 0.38 pl/0159 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0116 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0169 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0100 1.18 13.71 1.05 2.42 0.91 0.88 1.16 13.53 1.04 2.39 0.90 0.86 pl/0168 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0010 0.41 4.76 0.37 0.84 0.32 0.30 0.51 5.95 0.46 1.05 0.40 0.38 https://journals.e-palli.com/home/index.php/ajase pa ge 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-6, 2023 pl/0067 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0079 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0021 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0008 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0075 0.65 7.57 0.58 1.34 0.50 0.48 0.75 8.76 0.67 1.55 0.58 0.56 pl/0104 0.41 4.76 0.37 0.84 0.32 0.30 0.51 5.95 0.46 1.05 0.40 0.38 pl/0170 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0009 0.41 4.76 0.37 0.84 0.32 0.30 0.51 5.95 0.46 1.05 0.40 0.38 pl/0076 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0064 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0120 0.41 4.76 0.37 0.84 0.32 0.30 0.40 4.67 0.36 0.82 0.31 0.30 pl/0176 0.19 0.37 4.76 0.30 0.19 0.17 0.19 0.36 4.67 0.29 0.19 0.17 pl/0058 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0019 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0018 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0115 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0111 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0077 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0020 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0066 0.27 0.84 0.30 4.76 0.51 0.38 0.34 1.05 0.37 5.95 0.64 0.47 pl/0158 0.54 1.66 0.59 9.40 1.01 0.74 0.61 1.88 0.67 10.65 1.14 0.84 pl/0007 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0057 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0153 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0023 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0108 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0117 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0080 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0139 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0148 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0161 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0165 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0106 0.27 0.84 0.30 4.76 0.51 0.38 0.34 1.05 0.37 5.95 0.64 0.47 pl/0122 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0164 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0107 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0167 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0102 0.46 1.40 0.50 7.93 0.85 0.63 0.53 1.61 0.57 9.12 0.98 0.72 pl/0162 0.27 0.84 0.30 4.76 0.51 0.38 0.27 0.82 0.29 4.67 0.50 0.37 pl/0141 0.27 0.84 0.30 4.76 0.51 0.38 0.34 1.05 0.37 5.95 0.64 0.47 pl/0121 0.34 1.04 0.37 5.90 0.63 0.47 0.41 1.25 0.44 7.09 0.76 0.56 pl/0001 0.18 0.32 0.19 0.51 4.76 0.30 0.18 0.31 0.19 0.50 4.67 0.30 pl/0071 0.18 0.32 0.19 0.51 4.76 0.30 0.18 0.31 0.19 0.50 4.67 0.30 determination of the quality threshold (t) the threshold value t is determined as the lowest quality level a service point can offer a demand point to qualify that service point for consideration as a candidate in the analysis for the scenario under consideration. it therefore varies from scenario to scenario and is set by experiment as the smallest quality value for which the model has a feasible solution. https://journals.e-palli.com/home/index.php/ajase pa ge 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-6, 2023 results from model analyses the results from the model analyses shows the hospitals selected and the patient location that can enjoy service from them for each of the two scenarios (diabetes and cancer respectively) at a quality level of at least t (the quality threshold for that scenario). these results are here presented on the table below. the results shown below are identical. this is a pure table 4: showing results of the model analyses diabetes cancer facility patient location facility patient location jo s n or th jo s so ut h jo s e as t b /l ad i b as sa r yo m jo s n or th jo s so ut h jo s e as t b /l ad i b as sa r yo m pl/0150 ok ok ok ok ok ok pl/0150 ok ok ok ok ok ok pl/0100 ok ok ok ok ok ok pl/0100 ok ok ok ok ok ok pl/0075 ok ok ok ok ok ok pl/0075 ok ok ok ok ok ok pl/0158 ok ok ok ok ok ok pl/0158 ok ok ok ok ok ok pl/0102 ok ok ok ok ok ok pl/0102 ok ok ok ok ok ok *key: ok means covered by the corresponding facility coincidence and does not mean that other scenarios will have identical results. from the above results, a patient in any of the six patient locations (i = 1,2,…6) can access service from any of the five hospitals (j = 1,2,…5) chosen by the model with a quality level of at least t (t= 0.2 for diabetes and t=0.24 for cancer). conclusion this study therefore concludes that the five hospitals (bingham university teaching hospital, jos university teaching hospital, our lady of apostles hospital, dee medical centre and plateau specialist hospital) shown in the results provide adequate coverage (makes accessible) to patients with any of the chronic conditions (diabetes or cancer) who reside in any part of the plateau north senatorial district and who are nhis subscribers. recommendation the study recommends any of the five identified hospitals to nhis subscribers (patients) having diabetes or cancer and who reside in any part of the plateau north senatorial district. policies that allow the establishment of community parks, sidewalks, bike lanes, playgrounds or village square areas with beautiful landscapes where people can gather, jog, meet and play during leisure encourages people to participate in physical activity. acknowledgements i would like to acknowledgement the advice and encouragement received from professor g. wajiga and professor. h. g. muazu all of modibbo adama university yola, adamawa state, nigeria. references awodele, o., adeyomoye, a. a., awodele, d. f., fayankinnu, v. b., and dolapo, d. c. (2011). cancer distribution pattern in south-western nigeria. tanzan j health res., 13(2), 125-31. https://doi.org/10.4314/ thrb.v13i2.55226. current, j., daskin, m., and david, s. (2001). discrete network location models, in drezner, z. and hamacher, h. w., (eds). (2002). facility location: applications and theory, berlin: springer, 81-118. davwar, p. p., wajiga, g. m. and okolo, a. (2021). a patient-based set covering facility location model, global scientific journals, 9(10), 2423-2428 ezzati, m., and riboli, e. (2012). can non-communicable diseases be prevented? lessons from studies of populations and individuals. science, 337(6101), 14827. https://doi.org/10.1126/science.1227001 international diabetes federation (idf, 2011). idf diabetes atlas. 5th ed. brussels: international diabetes federation. olukoya, o. (2017). war against non-communicable disease: how ready is nigeria? [eds], annals of ibadan postgraduate medicine, 15(1). world health organization (2018a). non-communicable diseases. retrieved on april 12, 2020. https:// www.who.int/news-room/fact-sheets/detail/ noncommunicablediseases. world health organization (2018b). non-communicable diseases progress monitor 2018. retrieved on april 12, 2020. http://apps.who.int/iris/ bitstream/10665/258940/1/9789241513029eng. pdf?ua51. world health organization (2018c). global health observatory data 2018: premature ncd deaths. retrieved on april 12, 2020. https://www.who.int/ gho/ncd/mortality_morbidity/ncd_premature_text/ en/. world health organization (who, 2020). noncommunicable diseases. [cited 2020 april 12]. available from: http://www.who.int/mediacentrefactsheets/ fs355/en/. https://journals.e-palli.com/home/index.php/ajase pa ge 1 pa ge 65 american journal of applied statistics and economics (ajase) advancing statistical modelling: a comparative study of zero-truncated distributions in economic analysis b. e. omokaro1, c. o. aronu2* volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.4235 https://journals.e-palli.com/home/index.php/ajase article information abstract received: december 19, 2024 accepted: january 21, 2025 published: july 03, 2025 this study explores the application of two zero-truncated distributions, geometric-zero truncated poisson (gztp) and zero-truncated poisson pareto (ztpp), in modelling economic datasets, with a particular focus on nigeria’s key economic indicators. secondary data from the central bank of nigeria’s statistical bulletin (2021), spanning from 1989 to 2020, was used, covering variables such as real gross domestic product (rgdp), export and import goods, money supply, and brent crude oil prices. the objectives were to: introduce and describe the mathematical properties of the gztp and ztpp distributions; compare the performance of these distributions across datasets using metrics such as aic, bic, and mse; and recommend the most efficient distribution for modelling economic variables and predicting trends. the study employs the maximum likelihood estimation (mle) method for parameter estimation, implemented in r programming. model performance was evaluated using the akaike information criterion (aic), bayesian information criterion (bic), and mean squared error (mse). the results indicate that the ztpp distribution outperforms the gztp distribution across all datasets, with significantly lower aic, bic, and mse values. specifically, the ztpp achieved an average aic of -1422.54, bic of -1422.55, and mse of 30,161.94, compared to the gztp’s -766.39, -762.82, and 31,163.2, respectively. these findings highlight the superior model fit and predictive accuracy of the ztpp distribution, making it a more robust tool for economic analysis, particularly in cases where zero occurrences are impossible. this comparative study underscores the importance of choosing the right distribution for economic modelling to achieve high accuracy and reliable results. keywords aic, economic analysis, geometric-zero truncated poisson, maximum likelihood estimation, model fit, mse, zero-truncated poisson pareto 1 department of statistics, delta state polytechnic, otefe, oghara, delta state, nigeria 2 department of statistics, chukwuemeka odumegwu ojukwu university, uli, anambra state, nigeria * corresponding author’s e-mail: amaro4baya@yahoo.com introduction considering the growing significance of zero-truncated distributions in econometrics and economic forecasting. the zero-truncated distributions, including the zerotruncated poisson (ztp), gamma zero-truncated poisson (gztp), and zero-truncated poisson pareto (ztpp) distributions, have garnered attention for their ability to model count data that exclude zero observations, a common occurrence in economic datasets. recent advancements in these models, such as the work by niyomdecha and srisuradetchai (2023), who introduced the complementary gamma zero-truncated poisson (cgztp) distribution, have demonstrated the effectiveness of these distributions in modelling lifetime and economic data. the research by niyomdecha et al. (2023) proposed the gamma zerotruncated poisson (gztp) distribution, combining gamma and zero-truncated poisson distributions using the minimum function. their study explored the distribution’s characteristics, including hazard function and mle estimation, with simulation tests confirming its adaptability in modelling lifetime data. ngamkham and panta (2023) addressed the estimation challenges of the zero-truncated poisson (ztp) distribution, proposing a delta method for parameter estimation, which was applied to a real dataset on unrest events in southern thailand. similarly, the development of the ztpp distribution by badr et al. (2023) has provided superior fit metrics, including akaike information criteria (aic) and bayesian information criterion (bic), when applied to economic datasets. the work by panichkitkosolkul (2023) proposed the zero-truncated poisson-ishita distribution and evaluated various bootstrap methods for estimating confidence intervals, concluding that the simple bootstrap approach was most efficient for larger sample sizes. irshad et al. (2023) developed the lagrangian intervened poisson distribution (lipd), a generalized approach for overdispersed and under-dispersed datasets, and showcased its application using mle and simulations. akdogan et al. (2019) introduced the geometric-zero truncated poisson (gztp) distribution, demonstrating its usefulness for discrete systems with increasing hazard rates. shukla et al. (2020) adapted the poisson-ishita distribution to the zerotruncated poisson-ishita distribution (ztpid), showing its superior fit in count data without zero values. agarwal and pandey (2024) introduced the inflated zero-truncated poisson ailamujia distribution (imztpad), which demonstrated a better fit in modelling child mortality and genetic count data. panichkitkosolkul (2024) further investigated the zero-truncated poisson-lindley (ztpl) distribution and found that non-parametric bootstrap methods performed best for larger sample sizes. pankaj et al. (2023) examined system reliability using a dual repair technique and regenerative methodologies, providing insights into improving system reliability. ghosh et al. (2023) introduced a bivariate geometric distribution for negatively correlated count data, and abbas (2023) proposed a bivariate generalized geometric distribution pa ge 66 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 65-69, 2025 (bggd) for correlated count data, utilizing a bayesian approach for analysis. shang et al. (2023) presented a novel method for predicting aero-engine coaxiality using geometric distribution error modelling and deep learning, achieving high prediction accuracy. these advancements underline the potential of zerotruncated models to enhance the precision and efficiency of economic analysis, particularly when dealing with data that exhibits truncation at zero, such as income, expenditure, or event counts. despite these advancements, a notable gap remains in the comprehensive comparison of these zero-truncated distributions in the context of economic analysis. while individual studies have demonstrated the utility of these distributions, there is a lack of systematic evaluation across various economic datasets, with limited attention to performance metrics like aic, bic, and mean squared error (mse) in this domain. this study aims to fill this gap by introducing and comparing the mathematical properties of the gztp and ztpp distributions, alongside their performance in economic modelling. by applying these distributions to real-world economic datasets, the study seeks to identify the most efficient distribution for modelling economic variables and predicting trends. through this comparison, the study will provide valuable insights into the suitability of these distributions for addressing specific economic modelling challenges, thereby contributing to the advancement of statistical methods in economic analysis. hence, the aim of this study was to advance the understanding of zerotruncated statistical distributions and their application in economic data modelling. the specific objectives are to: introduce and describe the mathematical properties of the gztp and ztpp distributions; compare the performance of these distributions across datasets using metrics such as aic, bic, and mse; and recommend the most efficient distribution for modelling economic variables and predicting trends. materials and methods source of data collection for the study the study utilized secondary data, sourced from reliable publications such as the central bank of nigeria’s statistical bulletin for 2021, included key economic indicators from 1989 to 2020. these indicators were real gross domestic product (rgdp), export and import goods, money supply, and brent crude oil prices. this data set provided crucial insights into nigeria’s economic performance, trade, monetary policy, and the impact of oil prices, forming the basis for detailed econometric analysis. materials and methods table 1 presents the probability mass functions (pmfs) of two advanced distributions the geometric-zero truncated poisson (gztp) and zero-truncated poisson pareto (ztpp) table 1: the pmf of the gztp and ztpp distribution s/no. distribution pmf source 1. gztp distribution e-λ((1-p)x-(1-p)x-1),/(1-e-λ),λ>0,0≤p≤1,x∈{1,2,3,…} niyomdecha et al. (2023) 2 ztpp distribution (λne-λ)/n!(1-e-λ) ,λ>0,n∈{0,1,2,3,…} badr et al. (2023) the geometric-zero truncated poisson (gztp) and zero-truncated poisson pareto (ztpp) distributions in table 1 exhibit notable flexibility and unique properties, making them suitable for modelling diverse real-world phenomena. the gztp distribution incorporates a geometric component through the parameter p, allowing it to capture overdispersion and varying probabilities for successive events, which is crucial in applications with decaying probabilities. its range of p between 0 and 1 further enhances its adaptability to different datasets. on the other hand, the ztpp distribution, characterized by its poisson-based structure truncated at zero, is particularly effective in handling count data where zero occurrences are impossible. its dependency solely on the rate parameter λ simplifies its application while maintaining robustness in capturing event frequencies. both distributions are zero-truncated, addressing scenarios where non-zero occurrences are mandatory, and their closed-form pmfs enable straightforward parameter estimation and interpretation. these properties underscore their utility in fields such as ecology, reliability analysis, and actuarial science. parameter estimation parameter estimates for each distribution were derived using the maximum likelihood estimation (mle) method, which maximizes the likelihood function l(θ|x) given by: where f(xi;θ) represents the probability mass function (pmf) of the distribution, θ is the vector of parameters, and xi are the observed data points (casella & berger, 2002). mle implementation was performed in the r programming language (r core team, 2023). model performance measures of the distributions the model performance was evaluated using the following criteria: i. akaike information criterion (aic): aic=-2ln(l)+2k (2) where l where likelihood of the model, and k is the number of estimated parameters (akaike, 1974). ii. bayesian information criterion (bic): bic=-2ln(l)+kln(n) (3) pa ge 67 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 65-69, 2025 where n is the sample size (schwarz, 1978). iii. mean squared error (mse): where y_i represents the observed values, and y ̂_i are the predicted values. these metrics were computed for both the gztp and ztpp distributions across datasets, as shown in table 3. to ensure a comprehensive evaluation, the average values of aic, bic, and mse for each distribution were also computed and summarized in table 4. comparative analysis the comparative analysis focused on the ability of the gztp and ztpp distributions to minimize aic, bic, and mse. the ztpp distribution demonstrated superior performance across the datasets, as evidenced by its consistently lower average aic, bic, and mse values compared to the gztp distribution. this suggests that the ztpp distribution provides a better fit for the economic datasets under consideration. results and discussions table 2 provides an overview of the descriptive statistics for key economic variables employed in the study, highlighting their central tendencies, variability, and table 2: descriptive statistics of dataset variable mean st. dev minimum median maximum skewness kurtosis rgdp 365 99 237 330 569 0.72 -0.72 export_goods 40 30 10 31 99 0.76 -0.78 import_goods 26 19 6 20 66 0.77 -0.78 money_supply(m2) 59694188 20216127 32262332 56782642 109951956 0.57 -0.36 brent crude (brt) 47 32 11 35 133 0.89 -0.32 table 3: performance comparison of gztp and ztpp distributions across economic datasets dataset distributions parameter estimates aic bic mse rgdp gztp λ=0.1000,p=1.0000 -2258.61900 -2255.8700 148809.8000 ztpp λ=1.0000 -2150.6200 -2154.2200 143807.4000 export_goods gztp λ=1.0000,p=0.1000 -158.61900 -155.8170 2488.2640 ztpp λ=33.5437 -791.1342 -791.733 2487.7510 import_goods gztp λ=1.0000,p=0.1000 -158.6300 -155.8170 1009.2800 ztpp λ=25.4986 -541.2217 -543.8205 1009.6800 l o g ( m o n e y _ supply) gztp λ=1.0000,p=0.1000 -167.8410 -164.273 318.6976 ztpp λ=17.8489 -202.3526 -198.5684 315.3769 brt gztp λ=1.0000,p=0.1000 -1088.2200 -1082.3400 3189.9470 ztpp λ=38.1595 -3427.3550 -3424.4130 3189.4930 distributional characteristics. the real gross domestic product (rgdp) exhibits a mean of 365 units with a standard deviation of 99, indicating moderate variability around the average, and ranges from 237 to 569 units, with a median of 330. the skewness of 0.72 suggests a slight rightward skew, while the negative kurtosis (-0.72) indicates a relatively flat distribution compared to the normal curve. exported goods have a mean value of 40 units and a standard deviation of 30, with values spanning from 10 to 99 units. its skewness (0.76) and kurtosis (-0.78) reflect a similar pattern to rgdp, indicating asymmetry and platykurtic tendencies. import goods average 26 units with a standard deviation of 19, ranging from 6 to 66 units, showing a slightly higher skewness (0.77) and comparable kurtosis (-0.78). the money supply (m2) variable, measured in millions, has a mean of 59,694,188 and exhibits substantial variability (standard deviation of 20,216,127), ranging from 32,262,332 to 109,951,956. its skewness (0.57) and kurtosis (-0.36) indicate a mild rightward skew and a distribution closer to normal. lastly, brent crude (brt) prices show the highest variability, with a mean of 47, a standard deviation of 32, and a range from 11 to 133. its skewness (0.89) highlights a more pronounced rightward skew, while the kurtosis (-0.32) remains slightly platykurtic. these statistics collectively reveal distinct patterns and variability across the variables, providing insights into their economic implications and informing further analyses. the result presented in table 3 summarizes the performance metrics of the gztp and ztpp distributions across various economic datasets, providing insights into their parameter estimates, model fit, and predictive accuracy. for rgdp, the gztp distribution achieved a lower aic (-2258.62) and bic (-2255.87) compared to the ztpp (-2150.62 and -2154.22, respectively), but the ztpp showed a marginally lower mean squared error (mse) of 143,807.4 versus 148,809.8 for the gztp. similarly, pa ge 68 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 65-69, 2025 for export_goods, ztpp demonstrated superior fit metrics (aic = -791.13, bic = -791.73) and a slightly better mse (2487.75) than gztp (aic = -158.62, bic = -155.82, mse = 2488.26). in the import_goods dataset, gztp slightly outperformed ztpp in mse (1009.28 vs. 1009.68), but ztpp had better aic and bic values (-541.22 and -543.82, respectively). for log(money_ supply), ztpp consistently outperformed gztp with a lower mse (315.38 vs. 318.70), aic (-202.35 vs. -167.84), and bic (-198.57 vs. -164.27). lastly, in the brent crude (brt) dataset, ztpp exhibited a significant advantage in aic (-3427.36) and bic (-3424.41) while maintaining a marginally lower mse (3189.49 vs. 3189.95). these results underscore the flexibility and robustness of ztpp across datasets, particularly in achieving better model fit and predictive accuracy. table 4 presents the average performance metrics of table 4: comparative analysis of average performance metrics for gztp and ztpp distributions distributions average aic average bic average mse gztp -766.386 -762.823 31163.2 ztpp -1422.54 -1422.55 30161.94 the geometric-zero truncated poisson (gztp) and zero-truncated poisson pareto (ztpp) distributions across multiple datasets, highlighting their comparative effectiveness. on average, the ztpp distribution significantly outperformed the gztp in terms of model fit, as indicated by its lower average aic (-1422.54) and average bic (-1422.55), compared to the gztp’s average aic (-766.39) and average bic (-762.82). furthermore, the ztpp demonstrated superior predictive accuracy, with a lower mean squared error (mse) of 30,161.94, as opposed to 31,163.2 for the gztp. these results suggest that the ztpp distribution offers a more robust modelling framework, with consistently better performance metrics across datasets, making it a preferable choice for applications requiring high accuracy and reliable fit. conclusion this study highlights the flexibility and robustness of the gztp and ztpp distributions in modelling real-world phenomena, particularly in economic datasets. the gztp distribution, with its geometric component, is well-suited for capturing overdispersion and varying probabilities, making it adaptable to datasets with decaying probabilities. conversely, the ztpp distribution, relying solely on the rate parameter λ, excels in handling count data where zero occurrences are impossible, providing a simpler yet effective approach. the descriptive statistics of key economic variables, such as real gdp (rgdp), exported goods, import goods, money supply (m2), and brent crude (brt), reveal distinct patterns and variability, which inform further analyses. the comparative performance of the two distributions indicates that, on average, the ztpp outperforms the gztp in terms of model fit, with significantly lower aic, bic, and mean squared error (mse) values across multiple datasets. specifically, the ztpp achieved lower aic and bic values, such as -1422.54 and -1422.55, respectively, compared to the gztp’s -766.39 and -762.82, and demonstrated superior predictive accuracy with an average mse of 30,161.94 versus 31,163.2 for the gztp. these findings underscore the ztpp distribution’s superior capability in providing reliable model fit and predictive performance, making it a more suitable choice for applications that demand high accuracy in economic modelling. references abbas, n. (2023). on classical and bayesian reliability of systems using bivariate generalized geometric distribution. journal of statistical theory and applications, 22(3), 151–169. agarwal, a., & pandey, h. (2024). an inflated modelling of zero truncated poisson ailamujia distribution and its application to child mortality and genetics count data. journal of scientific research, 16(1), 89–96. akaike, h. (1974). a new look at the statistical model identification. ieee transactions on automatic control, 19(6), 716–723. https://doi.org/10.1109/ tac.1974.1100705 akdogan, y., kus, c., bidram, h., & kinaci, i. (2019). geometric-zero truncated poisson distribution: properties and applications. gazi university journal of science, 32(4), 1339–1354. badr, a. m. m., hassan, t., el din, t. s., & ali, f. a. m. (2023). zero truncated poisson-pareto distribution: application and estimation methods. wseas transactions on mathematics, 22, 132–138. casella, g., & berger, r. l. (2002). statistical inference (2nd ed.). duxbury press. ghosh, i., marques, f., & chakraborty, s. (2023). a bivariate geometric distribution via conditional specification: properties and applications. communications in statistics: simulation and computation, 52(12), 5925–5945. irshad, m. r., monisha, m., chesneau, c., maya, r., & shibu, d. s. (2023). a novel flexible class of intervened poisson distribution by lagrangian approach. stats, 6(1), 150–168. ngamkham, t., & panta, c. (2023). on the normal approximations to the method of moments point estimators of the parameter and mean of the zerotruncated poisson distribution. lobachevskii journal of mathematics, 44(11), 4790–4797. niyomdecha, a., & srisuradetchai, p. (2023). complementary gamma zero-truncated poisson distribution and its application. mathematics, 11(11), 2584. niyomdecha, a., srisuradetchai, p., & tulyanitikul, b. (2023). gamma zero-truncated poisson distribution with the minimum compounded function. thailand statistician, 21(4), 863–886. panichkitkosolkul, w. (2023). bootstrap confidence pa ge 69 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 65-69, 2025 intervals for the index of dispersion of zero-truncated poisson-ishita distribution. science and technology asia, 28(2), 9–17. panichkitkosolkul, w. (2024). non-parametric bootstrap confidence intervals for index of dispersion of zero-truncated poisson-lindley distribution. maejo international journal of science and technology, 18(1), 1–12. r core team. (2023). r: a language and environment for statistical computing. r foundation for statistical computing. https://www.r-project.org/ schwarz, g. (1978). estimating the dimension of a model. annals of statistics, 6(2), 461–464. shang, k., wu, t., jin, x., zhang, z., li, c., liu, r., wang, m., dai, w., & liu, j. (2023). coaxiality prediction for aeroengines precision assembly based on geometric distribution error model and point cloud deep learning. journal of manufacturing systems, 71, 681–694. shukla, k. k., shanker, r., & tiwari, m. k. (2020). zero-truncated poisson-ishita distribution and its applications. journal of scientific research, 64(2), 287– 294. pa ge 1 pa ge 15 0 american journal of applied statistics and economics (ajase) enhancing business performance using statistical quality control techniques reuben cheruiyot lang’at1* volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.6372 https://journals.e-palli.com/home/index.php/ajase article information abstract received: october 16, 2025 accepted: november 20, 2025 published: december 05, 2025 quality control (qc) is central to ensuring that business processes produce goods and services that meet customer requirements and regulatory standards. statistical methods underpin modern qc by enabling measurement, monitoring, and improvement of processes. this paper reviews the theoretical foundations of statistical quality control (sqc), describes key statistical tools such as control charts, process capability indices, and links them to business applications. it explores how statistics help identify variation (common-cause or special-cause), support decision‐making, and drive continuous improvement (e.g., six sigma). practical implications, challenges (data quality, assumptions, culture), and future directions (big data, multivariate control) are discussed. the aim is to provide business managers and researchers with a comprehensive overview of how statistics can contribute to effective quality control in business operations. keywords business performance, control charts, process capability, quality control 1 university of kabianga p.o. box 2030-20200 kericho, kenya * corresponding author’s e-mail: rlangat@kabianga.ac.ke introduction in a competitive global business environment, maintaining product or service quality is essential for customer satisfaction, brand reputation, and profitability (zacharias, 2022). quality control (qc) involves the operational techniques and activities used to ensure that product characteristics meet established requirements. statistics plays a pivotal role in qc by providing objective methods to measure process performance, identify variation, evaluate conformity, and guide improvement. this paper explores how statistical tools support qc in businesses. it reviews the major methods, discusses their applications and limitations, and outlines future directions. literature review the discipline of statistical quality control (sqc) emerged in the early twentieth century, primarily through the work of walter a. shewhart at bell labs, who introduced control charts to monitor process variation. shewhart defined the central idea of distinguishing common‑cause variation (inherent in the process) from special-cause variation (assignable factors) thus enabling process monitoring and control. over time, sqc has been integrated into broader framework such as six sigma and total quality management (tqm). among the approaches that stand out in sqc is the six sigma methodology, which when applied, can only allow a defect rate of not more than 3.4 per million opportunities. in such a process high‑quality output is definitely assured. this approach emphasizes continuous improvement and data-driven decision-making (connaughton, 2021). more recently, reviews have analysed the evolution of sqc tools and their research tradition (nagar & gahlot, 2022). in parallel, business-oriented research has explored how qc practices impact reputation, market share and operational performance (zacharias, 2022). statistical quality control techniques add value in multiple ways in business. through real-time monitoring, businesses can detect drift or abnormal variation early, reducing waste and defects. statistical tools enable managers to distinguish between common and special causes of variation, allowing targeted corrective actions. sampling plans save costs compared to 100% inspection. moreover, statistical summaries such as process capability or defect rates guide data-driven decision making. in framework such as six sigma, statistical measurement and analysis underpin the define measure analyze improve control (dmaic) process, ensuring systematic quality improvement (connaughton, 2021). strategically, effective qc enhances customer trust, reputation, and compelling competitiveness (zacharias, 2022). theoretical foundations: variation, capability, control all processes exhibit variation. the key theoretical insight from shewhart is that if only common-cause variation is present, the process is “in statistical control”; if special-cause variation occurs, the process may produce unpredictable outcomes or defects. control charts help visualize and distinguish these phenomena. once a process is stable (in control), businesses can evaluate how well it meets specification limits using capability indices such as process potential capability (cp) or process centering capability index (cpk), which rely on statistical measures of process mean and standard deviation. the objective is to keep the process within control limits so that only natural variation remains, facilitating predictable performance and enabling improvement initiatives. these indices are given by: pa ge 15 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 150-153, 2025 cp= (usl‑lsl)/6σ ....(1) where usl is the upper specification limit lsl is the lower specification limit σ is the process standard deviation it should be noted from equation (1), that a cp value greater than 1.0 indicates the process has the potential to meet specifications. though so, it doesn’t consider if the process is actually centered. in other words it shows that the process could actually work if it is centered. ....(2) control (spc). for example, while x̅ and r (or s) charts are for variable data, p-charts, np-charts, and c-charts are for attribute data. control chart rules help detect signals that may indicate special-cause also called assignable variation. acceptance sampling, on the other hand, allows businesses to inspect samples rather than entire lots, reducing costs while maintaining confidence in product quality. descriptive statistical tools such as histograms, pareto charts, and scatter plots assist in diagnosing process issues, identifying major defect causes, and exploring relationships among variables. inferential methods like regression analysis and design of experiments (doe) support root cause analysis and optimization of process inputs. results and discussion to illustrate the use of the control charts the real inbuilt dataset on piston rings in r has been used. this statistical process control charts were generated using r (r core team, 2024) with the qcc (scrucca, 2004) and sixsigma (panwar, 2023) packages. figure 1 shows the x̅chart for the dataset while figure 2 shows the corresponding r-chart. where μ is the process mean a value shows how well the process is centered between the upper and lower specification. from equation (2), if cpk > cp, then there is a high possibility that the process is not centered and may have a higher rate of defects. further a cpk value of 1.33 or more is considered good because it implies that less than 0.01% of the products will be scraped. control charts are the hallmark of statistical process figure 1: x-bar chart for piston ring diameters in figure 1 it is clear that 2 points are outside the upper control limit signifying that the process is out of control. in addition there was one (violating run) sequence of 2 points that violated the statistical rule. the process therefore needs to be stopped and action taken to correct. the rchart is hereby presented in figure 2. according to figure 2 the process is in control since all the points are within the lower and upper limits. were it not for the x̅chart, it would have been concluded that the process is statistically under control. in general therefore the process is not in control. pa ge 15 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 150-153, 2025 to facilitate the illustration of the analysis of the attributes, the dataset of orange juice from qcc was used. the dataset has the number of defectives or nonconforming cans in each the sample of 50 drawn. it is particularly useful in monitoring the defect rate. the output is as given in figure 3. figure 2: r chart for piston ring diameters figure 3: pchart for defective orange juice bottles in figure 3 quite a number of points lie outside the limits. specifically, 5 points are beyond the upper limits. additionally, there are 17 violating runs portraying extreme case of pattern which is unacceptable even if all the points were within the limits. clearly the chart in figure 3 indicates an out of control process. this study picks two commonly used indices (cp and cpk) as evaluated in figure 4 which indicate the process capability of meeting the specification and the centralizing factor. cp = 0.33<1 implying the process does not meet the specification. the index that show the centeredness of the process is cpk = 0.212 < 1.33. this value is far from 2 the index that corresponds to the high quality six sigma level of performance. it can therefore be concluded that the process is not capable of consistently meeting the specifications. with this outcome, action should be taken to reverse the situation. the process should be stopped and action taken to look for the cause of the problem. pa ge 15 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 150-153, 2025 replacement of worn out parts or scraping of some parts equipment in use may be done to enhance the quality of the products. this analysis of the dataset illustrates how statistical monitoring, detection of special-cause variation, corrective action, and capability analysis all combine to deliver business benefit challenges and limitations although the statistical qc toolkit is powerful, there are several challenges. data quality and measurement system error can mislead analysis. many tools assume independent and normally distributed data; violations of these assumptions can invalidate conclusions. cultural and organizational barriers can limit the effectiveness of qc if management fails to act on findings. the cost‑benefit balance must also be considered, as overly complex statistical methods may not yield a return on investment. finally, modern processes often involve multivariate and dynamic data that require more advanced statistical approaches beyond traditional charts. future directions the future of statistics in quality control lies in big data and real-time analytics. with the advent of sensors and internet of things (iot), companies can collect massive amounts of data and apply machine learning to detect subtle patterns. multivariate spc methods will become essential as processes grow more complex. moreover, statistical qc is expanding to service sectors like healthcare and finance, and integrating with sustainability metrics such as waste reduction and energy efficiency. these innovations will continue to redefine how businesses monitor and manage quality. conclusion statistics underlies effective quality control in business performance by providing tools to measure, monitor, analyze, and improve processes. from control charts to capability indices, from sampling plans to regression analysis, statistical methods give managers objective insight into variation and process performance. when appropriately applied and combined with organizational commitment, statistical quality control can transform business performance from inspection to process management, yielding improved quality, reduced costs, higher customer satisfaction, and competitive advantage. organisations should therefore invest in data quality, personnel training, and a culture of continuous improvement. future advances in big data and multivariate analysis promise to broaden the scope and impact of statistical quality control even further. references connaughton, s. (2021). statistical quality control in manufacturing. retrieved november 16, 2025, from https://www.ebsco.com nagar, h., & gahlot, a. (2022). a review on statistical quality control. neuroquantology, 20(9), 637-642. panwar, a. (2023). sixsigma: six sigma tools for quality control and improvement. cran. r core team. (2024, ). r: a language and environment for statistical computing. r foundation for for statistical computing. scrucca, l. (2004). qcc: an r package for quality control charting and statistical process control. r news, 4(1), 11-17. zacharias, m. v. (2022). the importance of quality control for the success of a company. asian journal of logistics management, 1(2), 99-106. figure 4: process capability analysis for diameter of the piston rings pa ge 1 pa ge 14 5 american journal of applied statistics and economics (ajase) survival regression model allowing for exposure-mediator interaction: analysis of kenya demographic health survey (kdhs) 2014 data irene mundia1*, hellen waititu1, nelson owuor2 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.3686 https://journals.e-palli.com/home/index.php/ajase article information abstract received: september 11, 2024 accepted: october 15, 2024 published: october 17, 2024 although infant and under-five mortality rates have decreased over the past decade, kenya, like many other african nations, did not achieve the millennium development goal (mdg) 4 target. to accelerate progress towards reaching sustainable development goal 3 by 2030, it is essential to understand the factors influencing under-five child mortality (ufcm), taking into account all potential confounding variables and effect modifiers. the child mortality rate serves as a key health indicator for any country. this study used data from the 2014 kenya demographic and health survey (khds). this paper introduces the concept of decomposing the total effect of an independent variable into three components: a direct effect, an indirect effect, and an interactive effect. we attempt to account for the direct effect of an independent variable on the outcome, then proceed to check the effect of the presence of a possible mediator and, furthermore, the possible interactions between an exposure variable and a mediator variable. the outcome variable was ufcm, and the study was to determine the effect of mother’s education on ufcm, in the presence of mediators such as mother’s income and the effect via the interaction between mother’s education and maternal income was estimated. to capture the effect of mediation and interactions in the context of survival analysis, an aalen additive model, including a product term for the exposure and mediator term, was developed. the methods were further illustrated with practical approach to kdhs data. kdhs has data on a broad scope of risk factors for ufcm. computations for all data sets was implemented using the freely available r-software package. this analysis suggests that while a significant portion of the impact of maternal education on ufcm is mediated by increasing maternal income, it is the interaction between maternal education and maternal income that leads to a reduction in ufcm. interventions targeting an increase in income among mothers with no education level would yield a greater reduction in ufcm than interventions targeting mothers with a higher education level. the total effect was ascribed to an interaction between the mother’s education level and maternal income, and part of it is attributable to pure indirect effect, and a given proportion is attributable to pure direct effect. the majority of the total effect (70%)is attributed to the interaction between change from no education level to primary education level and maternal income. 22% is associated with the pure direct effect, while 8% is linked to the pure indirect effect. interventions with a given increase in income among those with no education level would yield a greater reduction in ufcm than interventions targeting mothers with a higher education level. keywords aalen additive model, child mortality, mediation 1 department of mathematics and actuarial science, catholic university of eastern africa, kenya 2 quintiles east africa, kenya * corresponding author’s e-mail: imundiam@gmail.com introduction child mortality still remains a global problem despite many interventions going on, both in terms of health research and even at the level of government interventions. despite the global and national decline in infant and under-five mortality rates over the past decade, kenya, like many other african countries, failed to meet the target for millennium development goal (mdg) 4. the analysis was conducted using house hold data from kenya demographic and health survey (kdhs) data. to accelerate progress towards achieving the sustainable development goals (sdgs), effective interventions must be implemented in order to meet goal number 3 by 2030. determinants of child health, such as under five child mortality (ufcm) need to be understood in the context of all possible forms of confounding and/ or effect moderation. child mortality rate is one of the major health indicators for any country. kenya, just like other sub-saharan countries, have recorded high cases of ufcm. the sdg, goal number 3, target 3.2 on neonatal and child mortality has not been achieved in kenya, with 43.2 deaths per 1000 live births being reported in the year 2019 (rod et al., 2012) which is way above the 25 deaths per 1000 that is the target. part of the kenya government’s previous mid term development plans, known as the “big four agenda (2018-2022)” had universal healthcare as one of the key pillars, to align with the sdg’s vision 2030. this work therefore aims at evaluating determinants of ufcm using appropriate statistical models and assumptions. we have in this case applied regression with mediation and appropriate survival analysis models to conduct this analysis, taking into account the rarely considered aspect of a possibility of mediators and interactions among some useful ufcm determinants. intervention strategies using statistical outcomes from regression type models, frequently encounter the task of dissecting the impact pa ge 14 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 145-153, 2024 of an exposure into various causal pathways that operate through specific intermediary variables. the primary objective of conducting regression analysis is typically twofold: to gain insights into the underlying mechanisms and to then to propose potential intervention strategies. there is always a complex network surrounding decomposition of exposure effects in the presence of mediation and interactions. it’s important to note that implementing mediation analysis methods can be complex, as they rely on stringent assumptions that must be satisfied to obtain valid and interpretable estimates. exposure-mediator interaction is a potential source of bias, that if not carefully addressed, may lead to flawed conclusions in statistical analysis. mediation analysis examines the effect of the exposure variable and how changes in the mediator variable subsequently affect the outcome. therefore, controlling for exposure-mediator confounding, if it happens that such exist, is essential. although a number of studies where the determinants of ufcm are of interest have been done without due consideration of the possible role of mediation or moderation, attempts have been done in that direction. vanderweele (2013) developed findings on direct and indirect effects for linear and logistic regressions in the presence of exposure–mediator interaction. however, many studies have not considered the possibility that the exposure and mediator may interact in influencing the outcome. including interaction effects in a model allows us to compare the relative importance of several pathways mediated by interdependent variables (hougaard, 1999). recent advancements in causal inference theory have introduced concepts for mediation analysis and effect decomposition, enabling the separation of a total effect into direct and indirect components. the indirect effect can be further broken down into a pure indirect effect and a mediated interactive effect, resulting in a three-part decomposition of the total effect (direct, indirect, and interactive). this three-way decomposition offers deeper insight by allowing us to determine how much of the total indirect effect is due to mediated interaction versus the pure indirect effect (vanderweele, 2013). this work makes use of the concept of decomposing the total effect into three components: a direct effect, an indirect effect, and an interactive effect. we perform this three-way decomposition using an additive regression type model. these additive hazard models have the potential to reveal intricate effects when examining the impact of various factors on ufcm in kenya. three-way decomposition also applies to additive hazard scales. an additive model including a product term for the exposure and mediator term was developed and r codes for decomposition expanded. the analysis was conducted using household data from the kenya demographic and health surveillance (kdhs) data. results of the study shows that maternal education is recognised as a determinant of child health. the pathways linking maternal education level to ufcm are constrained by the statistical methods commonly found in the literature. to observe the complex pathways between maternal education and ufcm, we employed a statistical model that was able to accommodate interactions between the maternal education as an exposure and maternal income as a possible mediator. materials and methods this section describes the datasets and the structure of a model with mediation and a model with possible exposure-mediator interaction. the mediation models are concerned with seeking answers to the relationship between two variables based on how, or why questions. we hypothesize m as an intervening or mediating variable to show the relationship between x and y.x is an independent variable, and y is a dependent variable or outcome. the test for statistical mediation has been supported by recent studies based on regression equation’s coefficients from two or more equations as follows: y= i1+cx+e1 (1) figure 1: x= the independent variable, y= the dependent variable, and m= the mediating variable. c is the overall effect of the independent variable on y; c’ is the effect of the independent variable on y controlling for m; b is the effect of the mediating variable on y; a is the effect of the independent variable on the mediator; i1, i2, and i3 are the intercepts for each equation; and e1, e2, and e3 are the corresponding residuals in each equation (fairchild & mackinnon, 2009) pa ge 14 7 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 145-153, 2024 y= i2+c’x+bm+e2 (2) m= i3+ax+e3 (3) mediation analysis uses a mediator to investigate the effect of an exposure on an outcome through a mediator. a vast discussion on mediation analysis can be found in martinussen (2006)the aalen additive model being additive,is directly suitable to incorporating the role of mediation as opposed to other forms of survival regression models. survival regression model incorporating one mediator the aalen model specifies that the rate as a function of mediator (m), other baseline covariates (z) and exposure (x) is γ(t;x,m,z)= λ0 (t)+λ1 (t)x+λ2 (t)z+λ3 (t)m (4) where γ(t;x,m,z) represents the rate, expressed as a function of the mediator (m), other baseline covariates (z), and exposure (x). λj (t) denote potentially timedependent functions. a straightforward linear regression can be employed to characterize the mediator, under the assumption that it follows a normal distribution. thus, with other covariates (z) and exposure (x), the mediator is determined by: m= α0+α1 x+α2 z+e (5) where e is normally distributed error with zero mean and its variance σ2.the parameters are estimated using some statistical methods such as least squares method. suppose the exposure is set to x and the mediator to m, then we can denote the counterfactual rate for the event by γ(t; x, m, c) in the presence of other baseline covariates. the rate difference scale at time t is used to measure the total casual effect of changing the exposure from x* to x is γ(t;x, mx)-γ(t;x*, mx)= γ(t;x, mx)-γ(t;x*, mx*)+γ(t;x*, mx)-γ(t;x*, mx*)) =λ1(t)(x-x*)+λ3 α1(t)(x-x*), the equation te(t) = de(t) + ie(t) decomposes the effects into three distinct components: the natural indirect effect (ie), the natural direct effect (de), and the total effect (te). each of these terms carries a specific interpretation. the indirect effect quantifies the number of deaths caused by mediation through the mediator, while the direct effect represents deaths due to the direct pathway (or through mediators not considered in the analysis). the overall effect, which accounts for the total deaths resulting from changes in exposure, is determined by combining the direct and indirect effects, as explained in lange and hansen (2011). when both the exposure and mediator exhibit no time-dependent effects in the aalen model, with λ1 (t) and λ3 (t) remaining constant, theorem 1 simplifies to: therefore, both the direct and indirect effects can be represented by a single numerical value rather than being functions of time (t). the aalen additive model offers a framework for directly deriving confidence intervals for the direct effects. the model assumes no confounding in the relationships between (i) exposure and mediator, (ii) mediator and outcome, and (iii) exposure and outcome, provided pre-exposure confounders are controlled for, as outlined in nguyen et al. (2016). survival regression model allowing for exposuremediator interaction even when there is interaction between the exposure and the mediator in its impact on the outcome, it is still possible to perform mediation analysis by breaking down the total effect into direct and indirect effects. the model for outcome including an exposure mediator interaction is e[y|a, m, c]= θ0+θ1 a+θ2 m+θ3 am+θ4 c (7) we once again fit a linear regression model for the mediator. e[m|a, c]=β0+β1 a+β2 c (8) if the models are accurately specified, and the assumptions related to confounding are met, then the estimates of direct and indirect effects resulting from a change in exposure from a level denoted as “a*” are provided as follows: de=θ1+θ3 (β0+β1 a*+β2 a)(a-a*) (9) ie=(β1 θ2+β1 θ3 a)(a-a*) (10) standard errors for these equations are also calculated (vanderweele tj et al., 2009). when there is no interaction between the exposure and mediator (i.e., when θ3=0), the equations simplify, with θ1 representing the direct effect and β1 θ2 representing the indirect effect. as the explanatory variables are expressed on the additive hazards scale, product terms can be included to evaluate deviations from additive effects, similar to standard linear models. in the specified additive model, the rate is treated as a function of the mediator (m), baseline covariates (z), and the interaction between exposure (x) and the mediator (xm). this method is outlined in rod et al. (2012) work . γ(t;x,m,z)=λ0 (t)+λ1 (t)x+λ2 (t)z+λ3 (t)m+λ4 (t)xm (11) whereγ(t;x,m,z) is the rate, which is written as a function of mediator (m), other baseline covariates (z) , exposure (x) and interaction (xm). λj (t) are potentially timedependent functions. casual diagrams with exposure-mediator interaction representing a three-way decomposition. the total indirect effect consists of both the pure indirect effect and the mediated interaction. the difference between the total indirect effect and the pure indirect effect serves as a measure of interaction, referred to as the mediated interactive effect (vanderweele, 2013). pa ge 14 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 145-153, 2024 data the research utilized data from the kdhs 2014 survey, collected from a random sample of 20,964 respondents. this data set offers comprehensive information about every child under five years old within households, including the child’s gender, survival status, birth intervals, birth circumstances, and birth weight. in addition, the data set encompasses a wide range of information related to domestic and community characteristics, access to healthcare care, maternal and prenatal care, infant feeding practices, vaccination exposure, and more. our dependent variables include the time until a specific event occurs and the corresponding event status. in this context, the event status is coded as 1 for “dead” and 0 for “alive.” all children still alive during data collection were considered right-censored, meaning their event status was unknown. the data used in this article was right-censored. right censored data is vastly discussed by hougaard (1999). notable strengths of the kdhs data set include its large sample size, which represents the country’s entire population, and its rigorous quality control measures (manski, 2003). variables the risk factors examined in the study were selected based on the results of articles already published. they include sex of the child, age of mother, mother’s level of education, maternal income, and maternal health behavior. maternal education is considered in this context as an exposure,while maternal income and health behavior are considered as mediators. education is grouped in four categories; no education, primary education, secondary education, and higher education. the level of household wealth was used for maternal income. the wealth level was derived from an index calculated using data on ownership. the outcome variable is mortality status and month of death. status was recorded and subsequently coded according to whether the child is alive or not, with 0 for being alive while 1 for being dead within the first 5 years. age at death is given in months. the covariates included in the model were the sex of the child and the age. aalen additive model was used in the presence of mediation and interaction. it was used to assess mediation and interaction on two factors that are significant regulators of ufcm. mediation analysis can still be conducted even when the exposure and mediator interact in their effect on the outcome, by dividing the total effect into direct and indirect components. an aalen additive model was developed that incorporated a product term for the exposure and mediator, allowing the total effect to be broken down into three distinct components rather than just two. 1. the effect due to mediator only (maternal income). 2. the effect due to interaction (between maternal income and education) 3. the effect due to the exposure only (education) results and discussion through a model including a product term for the exposure and the mediator the total effect was decomposed into three distinct components (direct effect, pure indirect effect and the interactive effects). additive hazard models are implemented in the software package r. in the present paper, we adopt the ordinary least squares (ols) in approximating the parameters. this was achieved via r-programming using the package ‘timereg’. the functions required for estimating and analyzing the additive hazards model are contained in the package.the model is fitted using the aalen function.lange and hanssen (2011) discusses this technique widely. descriptive statistics figure 2: x is the exposure,m is the mediator and x*m is the interaction between the exposure and the mediator.y is the outcome and c is a set of con founders.red line shows each effect a.pure direct effect b.pure indirect effect and c. mediated interaction effect pa ge 14 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 145-153, 2024 on mortality, mother’s education, maternal income and adjusting factors are presented in table 1. descriptive characteristics a total of 20964 children were identified in the 2014 table 1: descriptive statistics of demographic variables and other determinants of under five-child mortality in kenya, 2014 width=tw 0 ( n=20093) 1 ( n=871) total (n=20964) residence urban 6532(32.5%) 296(34.0%) 6828(32.6%) rural 13561(67.5%) 575(66.0%) 14136(67.4%) education level no education 4406(21.9%) 179(20.6%) 4585(21.9%) primary education 10551(52.5%) 504(57.9%) 11055(52.7%) secondary education 3857(19.2%) 146(16.8%) 4003(19.1%) higher education 1279(6.4%) 42(4.8%) 1321(6.3%) religion roman catholic 3706(18.4%) 139(16.0%) 3845(18.3%) protestant 12405(61.7%) 553(63.5%) 12958(61.8%) muslim 3364(16.7%) 156(17.9%) 3520(16.8%) no religion 521(2.6%) 20(2.3%) 541(2.6%) other 59(0.3%) 3(0.3%) 62(0.3%) missing 38(0.2%) 0(0%) 38(0.2%) wealth index poorest 6893(34.3%) 285(32.7%) 7178(34.2%) poorer 4154(20.7%) 194(22.3%) 4348(20.7%) middle 3334(16.6%) 163(18.7%) 3497(16.7%) richer 3001(14.9%) 130(14.9%) 3131(14.9%) richest 2711(13.5%) 99(11.4%) 2810(13.4%) sex male 10157(50.6%) 476(54.6%) 10633(50.7%) female 9936(49.5%) 395(45.4%) 10331(49.3%) age group 15-19 1024(5.1%) 28(3.2%) 1052(5.0%) 20-24 4773(23.8%) 210(24.1%) 4983(23.8%) 25-29 6143(30.6%) 250(28.7%) 6393(30.5%) 30-34 4009(20.0%) 179(20.6%) 4188(20.0%) 35-39 2659(13.2%) 117(13.4%) 2776(13.2%) 40-44 1164(5.8%) 69(7.9%) 1233(5.9%) 45-49 321(1.6%) 18(2.1%) 339(1.6%) birth type single birth 19596(97.5%) 784(90.0%) 20380(97.2%) 1st of multiple 240(1.2%) 52(6.0%) 292(1.4%) 2nd of multiple 257(1.3%) 35(4.0%) 292(1.4%) no of children 0 586(2.9%) 251(28.8%) 837(4.0%) 1 7415(36.9%) 372(42.7%) 7787(37.1%) 2 8314(41.4%) 198(22.7%) 8512(40.6%) 3 3086(15.4%) 38(4.4%) 3124(14.9%) 4 570(2.8%) 8(0.9%) 578(2.8%) pa ge 15 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 145-153, 2024 kdhs data. of these 871 had died within their first years of life and 20,093 were alive. table 1 shows the descriptive characteristics of some the variables included in the study. around 34 per cent of those who died were living in urban areas and 66 percent in rural areas. among the dead children 54.6 percent were male and 45.4 percent were female.there were more children born in the poorest households than the richest households. almost 53% of the mothers had primary level of education, 22% no education, 19% secondary education and 6% higher education. bivariate analysis between maternal education and household wealth level 5 98(0.5%) 3(0.3%) 101(0.5%) 6 19(0.1%) 1(0.1%) 20(0.1%) 7 5(0.0%) 0(0%) 5(0.0%) table 2: bivariate analysis between maternal education and household wealth level highest education level wealth index poorest poorer middle richer richest no education 3690 306 186 229 174 80.5 % 6.7 % 4.1 % 5.0 % 3.8 % secondary education 311 718 917 1096 961 7.8 % 17.9 % 22.9 % 27.4 % 24.0 % higher education 17 64 142 321 777 1.3 % 4.8 % 10.8 % 24.3 % 58.8 % table 3: parameter estimates and standard errors for the regression of maternal income on education adjusting for age and sex coefficients estimate std. error t value pr(>|t|) (intercept) 1.281 0.048 26.565 <2e-16*** age 0.005 0.001 3.942 8.09e-05*** sex 0.013 0.016 0.810 0.418 primary education 0.987 0.021 47.064 <2e-16*** secondary education 1.980 0.026 76.482 <2e-16*** higher education 2.896 0.037 77.901 <2e-16*** signif. codes: 0***,0.001**,0.01*,0.05.,0.11 table 4: parameter estimates and standard errors(se) from the aalen additive model adjusting for maternal income, education level, age and sex education level estimate(se) x10-3 no education 0.00(0.00) primary education -1.45(0.057) mothers residing in households with higher levels of wealth and affluence exhibited a greater level of educational attainment in comparison to mothers from economically disadvantaged households. among the poorest families (80.5%) were at no education level. among the richest households only(3.8%) were in the no education level. however only (1.3%) of the poorest households had a higher education level compared with (58.8%) from the richest households. regression of maternal income on education adjusting for age and sex table 3 suggests that, on average, mothers at higher education levels have an income of 2.9 units higher than mothers at no education level when adjusted for age and sex. mothers in secondary education have an income of 1.98 units higher than mothers in no education level. mothers in primary level have an income of 0.99 units higher than mothers in no education level.mothers with higher education level in general have higher incomes compared to those with low education levels. aalen additive model adjusting for maternal income, education level, age and sex pa ge 15 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 145-153, 2024 secondary education -2.30(0.812) higher education -2.75(0.26) maternal income -2.16(0.728) table 4 shows that children born of mother’s in higher education level have a mortality rate that is 2.75x10-3 units lower than those of mothers in no education level adjusted for age and sex. children born of mothers in secondary education level have a mortality rate that is 2.30x10-3 units lower than those of mothers in no education level. children born of mothers in primary education level have a mortality rate that is 1.45x10-3 units lower than those of mothers in no education level. the higher the education level for the mothers,the lower the mortality rate for their under-five children. mediation analysis of maternal income on mothers education level for under five child mortality table 5: mediation analysis of maternal income on mothers education level for under five child mortality education level total effect direct effect indirect effect 0-i -1.9 -1.4 -0.5 0-ii -2.5 -2.3 -0.2 0-iii -3.5 -2.8 -0.7 as in the findings of he et al. (2020) , high education level leads to a lower rate of child mortality.the effect of mothers education level has two components,direct effect without maternal income and mediation effect of maternal income. change from no education level to higher education level would reduce the number of deaths by 3.5 per 1000 childrenβ=-3.5 of this decrease 0.7 fewer deaths (β=-0.7) resulted from maternal income pathway(natural indirect effect)representing 20 percent of the total effect. this implies that if an intervention were able to increase the maternal income of individuals with no education to that of those with higher education, while leaving other aspects of social deprivation unchanged, then 20% of the effect associated with education level could be mitigated. a transition from having no education to attaining a secondary level of education is associated with a reduction of 2.5 deaths per 1000 children (β=-2.5). among this decrease, 0.2 fewer deaths (β=-0.2) can be attributed to the maternal income pathway, considered the natural indirect effect. this represents 8% of the total effect. if an intervention were to elevate the maternal income of individuals with no education to the level of those with secondary education while keeping all other aspects of social deprivation constant, it could potentially eliminate 8% of the educational level’s impact. estimation of the interaction between maternal education and maternal income table 6: mediation analysis of maternal income on mothers education level for under five child mortality education level total effect direct effect indirect effect interaction effect 0-1 -111.8 -25.1 -8.1 -77.8 0-ii -131,6 -119.7 18.5 -30.4 0-iii -345.6 -366.4 -26.8 47.6 the majority of the total effect 70%is attributed to the interaction between change from no education level to primary education level and maternal income. 22% is associated with the pure direct effect, while 8% is linked to the pure indirect effect. this analysis suggests that while a significant portion of the impact of maternal education on under-five child mortality (ufcm) is mediated by increasing maternal income, it is the interaction between maternal education and maternal income in most cases of mediation that leads to a reduction in ufcm. discussion while conducting regression analysis, it is an important subject to attempt to decompose exposure effects in the presence of mediation and interactions. evaluation of mediators and interactions is vital in guiding public health interventions, clinical judgment, and healthcare planning policies. by attempting to use varied techniques in analysis, more insights into the data becomes clearer leading to improved inference. this study used the aalen’s additive model which differs slightly from the cox regression model. the latter approach involves modeling the hazard rate and assumes a proportional hazard structure, while the former employs an additive model, assuming a linear parametric structure for the hazard rate. additive hazard models allow for the decomposition of effects into total, direct, and indirect components. the analysis utilized khds 2014 data to identify the determinants of ufcm. kenya is one of the countries in the african region with high ufcm rates. identifying factors leading to mortality among children under 5 years is crucial problem that needs consideration. this could help inform more appropriate health and intervention strategies. using the aalen additive models, we identified pa ge 15 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 145-153, 2024 the total effects, direct effects, indirect effects and interactive effects between children’s survival time, mother’s education, maternal income and maternal health behaviour. we evaluated the effect of maternal income in mediating the impact of maternal education on child mortality among children under the age of five in kenya. our results confirm what has generally been found. in the analysis, the selection of covariates is linked to the findings in nguyen et al. (2016). to assess interaction, we adopted a pragmatic approach grounded in the idea that interventions and preventive measures should target patients or specific subgroups of the population where most cases can be prevented. interactions are typically evaluated by incorporating a product term into the regression model, a method that relies on the model’s underlying scale (martinussen, 2006). we evaluated interaction by calculating the deviation from the additivity of effects using a model incorporating a product term to account for the interaction between maternal education level and maternal income. the findings were as follows: ufcm was higher amongst mothers with no education level. some of the differential effect of maternal income is most likely due to varying incomes across educational groups. a large percentage of the total effect (70%)was attributed to the interaction between change from no education level to primary education level and maternal income. suppose we consider the observed relationships to be causal, that would imply that a specific intervention aimed at increasing income among uneducated individuals would result in a more significant reduction in ufcm than interventions directed toward mothers with a higher education level. similarly, a universal intervention such as educating the mothers with the same increase in income in both groups would be expected to have a stronger effect on the reduction of ufcm in the no-education level group. results from other researches concur with our findings. for example, imbo et al. (2021) highlighted that mothers without any education had significantly higher odds of neonatal deaths, with an adjusted odds ratio (aor) of 2.201, 95% ci: 1.43-4.15, p=0.049, when compared to mothers with higher levels of education. however, this study employed logistic regression, only adjusting for some specific covariates of interest. a similar study based on dhs data, conducted in ethiopia showed that neonates born to fathers with secondary and higher education level (aor=0.51; 95%ci: 0.22-0.88) had lower odds of neonatal mortality in ethiopia. the analysis was conducted using multiple logistic regression and missed out on the possible role of mediation, moderation or interactions in directing the role of some of the defined exposure effects (basha et al., 2020). another study in ethiopia also made the following findings. neonatal mortality was significantly associated with being born to a mother without formal education (aor = 1.79, 95% ci: 1.12-2.88), a mother who did not participate in healthcare decision-making (aor = 1.25, 95% ci: 1.14-1.79), and being part of a twin birth (aor = 6.85, 95% ci: 3.69-12.70). the model used was a multivariable logistic regression model without any additional assumptions on covariate interactions or role of mediation, as is commonly done for such cross-sectional studies using dhs datasets. the main limitation of this study is that only a select few select variables were included in the final model, being the outcome variable (ufcm), the exposure variable (maternal education) and the mediator variables (maternal income and health characteristics). a more parsimonious model could be useful to illuminate even more realistic effect of the exposure variable on ufcm. conclusion the study involves quantification of mediation in a survival context. we applied an easier and interpretable measure of natural direct, indirect effects, and interactive effects in addition to their confidence intervals. the additive hazard scale is used to calculate the effects. this helps in direct translation of expected no of extra cases. this study offers a simple and intuitive approach to evaluating deviations from additive effects in survival analysis by utilizing additive models in the context of mediation and interaction. in the absence of bias, deviations from risk additivity suggest that certain subgroups may experience greater absolute risk reduction than others. additionally, a researcher might be interested in determining the extent to which a mediated effect depends on the combined influence of the exposure and the mediator. the method is illustrated by analysis of the linkage among education, maternal income and under-five mortality previously examined in the study of soe et al. (2019). this paper contributes to the literature on mediation analysthe is as well as literature on the importance of education on ufcm. the influence of education on ufcm had different pathways in this study. interventions with a given increase in income among those with no education level would yield a greater reduction in ufcm than interventions targeting mothers with a higher education level. the results of this study may contribute to improve relevant interventions for ufcm among children in kenya. it will assist the kenyan government, non-governmental organizations, and other health sector partners in identifying key focus areas and relevant statistical tools needed to formulate policies and implement projects aimed at reducing ufcm, which aligns with the sustainable development goals (sdgs). future experimental studies are then necessary to provide more information on the implementation of mediation analysis methods in cases where the strong assumptions that need to be met are violated. studies on statistical power to detect interactions and to incorporate additive hazard models into other conventional software package are required. references basha, g. w., woya, a. a., & tekile, a. k. (2020). determinants of neonatal mortality in ethiopia: an analysis of the 2016 ethiopia demographic and health survey. african health sciences, 20(2), 715–723. pa ge 15 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 145-153, 2024 fairchild, a. j., & mackinnon, d. p. (2009). a general model for testing mediation and moderation effects. prevention science: the official journal of the society for prevention research, 10(2), 87–99. he, w., aboderin, i., & adjaye-gbewonyo, d. (n.d.). international population reports. afrique.maisonphilo.com. https://afrique.maisonphilo.com/doc/aging.pdf hougaard, p. (1999). fundamentals of survival data. biometrics, 55(1), 13–22. imbo, a. e., mbuthia, e. k., & ngotho, d. n. (2021). determinants of neonatal mortality in kenya: evidence from the kenya demographic and health survey 2014. international journal of mch and aids, 10(2), 287–295. lange, t., & hansen, j. v. (2011). direct and indirect effects in a survival context. epidemiology (cambridge, mass.), 22, 575–581. manski, c. f. (2003). partial identification of probability distributions (2003rd ed.) [pdf]. springer. martinussen, t., & scheike, t. h. (2006). dynamic regression models for survival data [pdf]. springer. nguyen, t. t., tchetgen tchetgen, e. j., kawachi, i., gilman, s. e., walter, s., & glymour, m. m. (2016). comparing alternative effect decomposition methods: the role of literacy in mediating educational effects on mortality. epidemiology (cambridge, mass.), 27(5), 670–676. rod, n. h., lange, t., andersen, i., marott, j. l., & diderichsen, f. (2012). additive interaction in survival analysis: use of the additive hazards model. epidemiology (cambridge, mass.), 23(5), 733–737. soe, k. (2019). what is the association between maternal education and childhood mortality, childhood illnesses and utilization of child health services in myanmar? proquest dissertations publishing. https:// search.proquest.com/openview/7d1bf595eeab8315d f6a31af5f452c53/1?pq-origsite=gscholar&cbl=1875 0&diss=y vanderweele, t. j. (2013). a three-way decomposition of a total effect into direct, indirect, and interactive effects. epidemiology (cambridge, mass.), 24(2), 224–232. pa ge 1 pa ge 87 american journal of applied statistics and economics (ajase) research on the digital transformation of corporate finance in the digital economy era yuhao gu1* volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.5329 https://journals.e-palli.com/home/index.php/ajase article information abstract received: june 12, 2025 accepted: july 18, 2025 published: august 15, 2025 in the context of the digital economy, the digital transformation of corporate finance has become an irreversible trend. this article discusses the necessity, difficulties and innovative strategies of digital transformation of corporate finance. in view of the difficulties of digital transformation of corporate finance in the digital economy era, this article proposes innovative strategies such as strengthening guidance and thinking transformation, strengthening the construction of digital talent team, optimizing data integration application and risk control, and ensuring data governance is in place. these strategies are aimed at helping enterprises effectively respond to transformation challenges and improve financial management efficiency and competitiveness. keywords corporate finance, digital economy, digital transformation 1 internotional institute of management and business, minsk, belarus * corresponding author’s e-mail: yuhaogu1128@163.com introduction with the full arrival of the digital economy era, corporate development faces severe challenges. only by actively carrying out digital transformation can enterprises meet market demand and achieve high-quality development goals. however, judging from the previous financial digital transformation, enterprises still face some problems. the low level of financial digital technology application and the lack of compound financial talent reserves have restricted the financial digital transformation of enterprises and affected the healthy development of enterprises. therefore, actively carrying out the financial digital transformation of enterprises is of great significance to achieving high-quality development of enterprises. the country’s “14th five-year plan” development plan proposes to develop the digital economy and promote digital industrialization and industrial digital transformation with the help of high-tech technologies such as big data, the internet, cloud computing, the internet of things, and artificial intelligence to build a digital china. digital transformation is an important means to promote the organic combination of digital technology and the enterprise value chain and promote enterprise transformation and upgrading. the digital transformation of financial management is a strategic change in the organizational structure, process, and model of financial management. as a connecting point for the digital transformation of enterprises, it plays a positive role in enabling management innovation, deepening the integration of business and finance, and improving the operational efficiency of enterprises. the article analyzes the current status of the digital transformation of financial management in chinese enterprises, points out the path of digital transformation, and proposes strategies to promote the digital transformation of financial management. as a new economic form that is developing rapidly, the digital economy was first proposed by don tapscott in 1996. with the progress of the times and the innovation of information technology, the digital economy has become an important economic form to improve the level of national economic development and enhance the comprehensive competitiveness of enterprises. it has effectively improved the productivity of enterprises and promoted the rational allocation of resources (tan & yang, 2024). the comprehensive development of the digital economy has accelerated the pace of digital transformation of enterprises. the financial department is an important data distribution center within the enterprise, and promoting the digital transformation of financial management has become a key measure in the process of digital transformation of enterprises. how to keep up with the trend of the digital economy, seize the opportunities of digital transformation, and promote the digital transformation of financial management is an important issue that needs to be solved in today’s era . literature review overview of enterprise finance digital transformation connotation of digital transformation of enterprise finance china’s enterprise management and accounting professional talent training system has only been developed for 30 to 40 years. the success of enterprises that have grown and developed based on economic take-off relies more on policy support, industry selection, huge market gaps, etc., and is less dependent on internal management, especially financial management. as a result, many enterprises, especially private enterprises, have a low level of awareness and attention to the importance of financial pa ge 88 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 87-93, 2025 management, and lack the motivation to promote the digital transformation of financial management. in the past five years, under the strong promotion of national policies, the speed of digital transformation of enterprises has accelerated, but the overall degree of digitalization is low. according to the survey of some leading enterprises in digital transformation, when promoting digital transformation, they first consider marketing and supply chain, and financial informatization is promoted in conjunction with the informatization construction of business departments. due to the lack of top-level design for financial digital transformation, enterprises can only passively follow up according to the business development and changes in business information systems, and carry out patch-type information system development. the problems of complex, redundant, and mismatched financial data are prominent, which cannot meet management needs at all. in addition to the information system, they continue to rely on a large amount of manpower and material resources for manual data processing (sun et al., 2024). overall, the digital transformation of financial management in some enterprises in china is still in the exploratory stage, and there is still a long way to go in the digital transformation of financial management, which is reflected in the following aspects. first of all, digital transformation is to achieve the transition of financial work from traditional manual processing to system digital processing, so that financial basic work no longer relies on manual work, but through digital automatic accounting, thus avoiding the risk of errors caused by manual operation. digital transformation means that enterprises need to establish an information-based financial data platform. relying on information technology, financial data can be accurately recorded, tracked and analyzed in digital form, improving the efficiency of accounting and auditing, reducing labor costs and improving work efficiency. secondly, digital transformation includes digital optimization of financial processes. traditional financial processes are often cumbersome and time-consuming, which easily leads to waste of resources and low efficiency. by introducing digital technology, enterprises can finely decompose and optimize financial processes, reduce labor costs and improve operational efficiency. finally, digital transformation also involves intelligent support for financial decision-making. traditional financial statements and data analysis can only provide static information, lacking real-time and forward-looking information, while digital transformation can improve data acquisition efficiency and accuracy through modern digital technology, providing managers with more convenient decision-making references, as shown in figure 1. figure 1: key drivers for accelerating the development of the digital economy the significance of digital transformation of enterprise finance first, promote the construction of financial management system and realize the transformation of financial functions. digital transformation can achieve a comprehensive transformation of financial functions by introducing advanced financial management tools and technologies, turning it from a passive data processor into an active business supporter and decision-maker. secondly, improve the efficiency of financial operations and support corporate management decisions. traditional financial management is limited by manpower and time, and is prone to information lags and decision delays. digital transformation can achieve efficient and intelligent financial operations through automated data processing and analysis, and provide more accurate support for corporate management decisions. it also improves the quality of financial data and enhances financial management capabilities (yi et al., 2024). traditional financial management often has problems such as data duplication, errors, and lags, which can easily affect the accuracy and effectiveness of decision-making. pa ge 89 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 87-93, 2025 digital transformation can improve the consistency and accuracy of financial data and enhance the ability and effectiveness of financial management by establishing a unified financial data platform and standardized data management processes. finally, judging from the current practice of digital financial transformation in many enterprises, there are generally unclear strategic goals for digital transformation, imperfect financial digital management models, and incomplete financial organizational structures, which cannot meet the requirements of digital financial transformation in the digital economy era. moreover, in the specific transformation process, the infrastructure and equipment are imperfect, the degree of interaction between business and financial data is not deep, and the operational development of the enterprise cannot be fed back in time, affecting the effectiveness of the digital transformation of the enterprise. at the same time, some enterprises have built many types of system platforms with low correlation, and the interconnection between systems is not smooth, which is not conducive to data aggregation and unified management, affecting data utilization. in addition, the financial management concepts of some enterprises have not been updated in a timely manner, and they still use previous management methods and models, which cannot fully tap the value of financial data, which to a certain extent hinders the innovation and development of enterprises in the new era. materials and methods problems faced by digital transformation of corporate finance through literature research and comparative argumentation, we sorted out relevant literature, used literature analogy to classify and summarize the issues of digital transformation of corporate finance in the digital economy era , and sorted out relevant issues. the relevant issues are as follows. the application level of financial digital technology is low at present, most enterprises are in the initial stage of financial digital transformation, and the application of digital technology is relatively limited, which makes it difficult to effectively play the advantages of digital technology in financial work (alisher, 2024). first, the functions of financial software introduced by some enterprises are relatively simple, and some financial software integrated with digital technology only have basic functions such as financial statement preparation and accounting, lacking in-depth application of artificial intelligence and big data technology. some financial software is not efficient in processing unstructured massive financial data, and it is difficult to deeply mine the valuable information in the data, resulting in the inability of enterprises to obtain more data support for financial analysis and forecasting. secondly, the digitalization system of financial data of some enterprises lacks collaborative functions. the financial system and the business system are independent of each other, and most of the enterprise operation data is difficult to share and interact, which further forms an “information island”, resulting in the disconnection between enterprise financial data and business development. financial managers find it difficult to effectively grasp the business development dynamics of the enterprise and cannot provide reliable support for various business decisions (javaid et al., 2024). for example, in an enterprise, if the financial system and the production system lack effective connection, the data of the entire production process cannot be shared with the financial system in a timely and effective manner, which will lead to delays in financial accounting and cost control, seriously affecting the actual efficiency of the enterprise. insufficient reserves of compound financial talents in the process of financial digital transformation, the ability of corporate financial personnel also plays an important role. financial personnel should not only have rich financial professional knowledge, but also have strong information technology application and data analysis capabilities. however, from the perspective of some current enterprises, they are still facing problems such as a shortage of compound financial talents. in the past, financial education was more inclined to learning accounting theory and financial knowledge, and less teaching of data analysis and information technology content, resulting in many financial personnel who have graduated for a long time lacking digital financial skills. in the financial work of enterprises, some financial personnel have been engaged in basic accounting work for a long time and rarely come into contact with new technologies and tools, which makes it difficult for financial personnel to transform from traditional finance to digital finance. in addition, some enterprises lack the introduction and training of compound talents (xia et al., 2024). compound financial talents are scarce in the market, and it is difficult for enterprises to introduce professional talents that meet the needs of digital transformation. at the same time, there is a lack of a complete talent training system within the enterprise, and insufficient investment in the cultivation of existing financial talents, which makes it difficult for financial personnel’s digital capabilities to meet the actual transformation needs, further exacerbating the problem of a shortage of compound financial talents in enterprises. imperfect financial data governance system financial data is an important foundation for enterprises to achieve digital transformation, and it is related to the success of the digital transformation of corporate finance. however, from the perspective of current corporate financial data governance, there are still some problems. first, the financial data standards of some companies are not unified, and there is a lack of standardization of pa ge 90 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 87-93, 2025 financial data formats within the company, which leads to inconsistencies in data collection and transmission. the data calibers of the financial departments and sales departments of some companies are inconsistent, which makes it difficult for financial data to truly reflect the actual operating status of the company, and the accuracy of financial analysis and decision-making is also affected. secondly, the data of some companies are not standardized when recording people, resulting in uneven data quality, missing and errors in corporate financial data, etc. lowquality financial data is not only difficult to provide good decision-making support for the company, but may even mislead corporate personnel to make wrong decisions. finally, some companies do not pay enough attention to data privacy protection. as financial data gradually shifts to digital storage, the risk of financial data leakage continues to increase. due to the lack of a sound data security protection mechanism, some companies do not strictly manage data access rights. if data leakage occurs problems such as leakage are bound to cause serious economic losses to the enterprise. results and discussion discussion on the path of digital transformation of corporate finance in the digital economy era the digital transformation of financial management uses modern information technology to extend financial management concepts and methods to the business level, and through business empowerment, it promotes business departments to carry out value creation activities. 3.1 “three-in-one” financial management model the digital transformation of financial management is a strategic change in the organizational structure, process and model of financial management. by building a new financial organizational structure led by strategic finance, with business finance as the main body and shared finance as the basis, we can focus on the key points of financial management and give full play to the role of the financial digital platform. first, shared finance is the basis for carrying out financial management work. by building a shared financial center, enterprises focus on standardized financial accounting, and uniformly handle all accounting business according to the systems and standards formulated by strategic finance and business finance, while providing data support for strategic finance and business finance for management decision-making. second, shared financial functions mainly include standardized businesses such as expense reimbursement, procurement and payment accounting, order and collection accounting, general ledger and report accounting (raihan, 2024). strategic finance is mainly responsible for group decision support, resource allocation, policy formulation, etc. its functions mainly include budget management, financial report analysis, performance appraisal, operation analysis, etc. business finance is mainly responsible for extending financial management activities to the business and operational levels, providing professional analysis for business decisions, and promoting the integration of business and finance. its functions mainly include budget preparation and control, cost and expense control, internal control risk management, etc. third, business finance personnel and the financial management work they are responsible for are extended to the business level through digital information systems, and the production and operation data at the business level are transmitted to the strategic finance level through digital information systems, opening up the data channel between the front-line business level and the corporate management level. the management level adjusts the corporate strategy, model, and business management model based on business data, and business finance assists the business level in implementing them. comprehensive budget management as an important tool for decomposing and implementing corporate strategic goals, comprehensive budget management is a core part of financial management. it is mainly divided into three steps: budget preparation, budget execution, and budget assessment. most of the other aspects of financial management can be directly or indirectly included in the framework of the comprehensive budget management system. the first is budget preparation. budget preparation is the beginning and foundation of comprehensive budget management. it is usually formulated by the responsible departments of various businesses. it needs to be decomposed into various departments in combination with the company’s strategic planning goals, and combined with the current situation of each department, historical operating conditions and corporate financial conditions, etc. (rachmad, 2025). after digital transformation, the budget management module configured by the enterprise based on the business information system can use big data technology, artificial intelligence technology, etc. to identify, mine, extract and summarize data from the business information system. each business responsible department only needs to complete the business operations within the scope of responsibility, and the business data and data required for budget preparation are automatically extracted through a well-configured information system. in this process, business finance is responsible for operational guidance, data analysis and review. in this way, on the one hand, work efficiency can be improved, and on the other hand, operational or subjective errors that may be caused by human participation can be avoided, thereby improving work quality. the second is budget execution. budget execution is an important step in budget implementation. budget implementation should be promoted according to the established rhythm, and supervision and control of budget execution should be maintained. deviations in the execution process should be corrected in a timely manner to ensure the smooth realization of budget targets. after digital transformation, while the responsible departments of various businesses complete their work within the pa ge 91 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 87-93, 2025 scope of their duties, the business execution data will be transmitted to the information system. through the background calculation of the system, the execution effect and deviation can be intuitively presented in the form of charts. the responsible departments and budget management personnel can promptly discover problems in budget execution, correct deviations in a timely manner, reduce possible losses, and ensure the smooth completion of the budget. in addition, through data modeling, business behaviors can be simulated by preentering simulated business data. through the simulation results, the impact of the simulated action on corporate performance and budget targets can be evaluated, and business behaviors can be adjusted. enterprises rely on information technology and data modeling to achieve management pre-positioning and drive business with data. the third is budget assessment. budget assessment is the assessment and evaluation of the budget execution results of the responsible departments by the enterprise. it is an effective incentive and constraint measure implemented on the responsible departments through the budget management system. it runs through the entire budget execution process and after the budget execution is completed. it is a dynamic assessment and a comprehensive assessment. the purpose of budget assessment is to better achieve corporate strategies and budget goals. after digital transformation, the information system can extract business data in a timely manner, so that it can meet both the assessment of business processes and the consideration of business results: it can conduct a single assessment of a certain indicator, or a comprehensive assessment of multiple indicators, which enhances the flexibility and timeliness of budget assessment. in addition, the budget execution data is directly read and displayed by the information system, which reduces the risk of the relevant responsible departments modifying and embellishing the data and improves the seriousness of budget assessment. implement rolling budget management the current market competition is fierce. if enterprises want to win the initiative in the fierce market competition, they must pay attention to the external market and industry environment at any time, respond to environmental changes in a timely manner, and adjust their business management strategies. the comprehensive budget of an enterprise is usually prepared on an annual basis at the beginning of the year, and has a certain rigidity and cannot be adjusted at any time. it is usually used as a basis for resource allocation and annual performance appraisal of various departments within the enterprise. since the comprehensive budget cannot reflect changes in the market environment in a timely manner, it has limited guiding significance for the management to conduct monthly management scheduling. therefore, it is necessary to implement monthly rolling budget management to make up for the shortcomings of the comprehensive budget. in actual work, enterprises can conduct rolling budget management in cycles of 3 months, 6 months, 9 months or even 12 months. the overall preparation ideas and methods of the monthly rolling budget are basically consistent with the preparation logic of the comprehensive budget. the difference is that the monthly rolling budget requires a strong timeliness, and each business unit should complete the budget preparation work of its department in a timely manner according to the time node (dong et al., 2024). in addition, the rolling budget is usually only used as a basis for business development and business management, and is not used for performance appraisal. for digital transformation, enterprises need to embed the data models, data flow relationships, data calculation logic, etc. required for rolling budget management into the business information system when planning and configuring the information systems of each business unit, and reserve corresponding data interfaces for business units to facilitate data input. after digital transformation, the information system generates rolling budget data reports on demand based on the budget data input by each business unit at the end of each month and in accordance with the preset data model, which serves as the basis for management decisions and business adjustments of the enterprise management. at the same time, the information system can capture the actual data of the current month’s business, compare and analyze it with the budget data generated last month, and present the relevant differences to business personnel and managers to analyze business execution deviations. financial operations based on data center the biggest pain point in the digital transformation of financial management is that each module within the enterprise configures its own data platform information system based on its own business needs, and there is an obvious “data fragmentation” problem between different information systems. breaking down data silos by building a data middle platform has become the key to transformation, and it has also become a core measure to build a data asset system and release the value of data assets. the data middle platform can integrate the existing scattered multi-system data of different business modules, purify and process it into data assets, and then reuse the data in a shared form to quickly build an agile data service system, empower business development and innovation, and improve enterprise operational efficiency. innovative strategies for digital transformation of corporate finance in the digital economy era improve data integration and application and strengthen risk management in the era of digital economy, enterprises are facing unprecedented data challenges and opportunities. as an important asset of enterprises, data integration and application and risk control are particularly important. in order to achieve the digital transformation of enterprise pa ge 92 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 87-93, 2025 finance, data must be effectively integrated and applied, supplemented by strict risk control measures. first, strengthen the integration and application of data. the integration and application of data is the core link of enterprise digital transformation. enterprises need to build a sound data governance system to ensure the accuracy, integrity and consistency of data. through technical means such as data warehouses and data lakes, centralized storage and unified management of various types of data can be achieved. in addition, big data analysis and mining technologies should be used to deeply explore the value of data and provide strong support for the company’s financial decision-making, market forecasting, etc. second, enterprises need to pay attention to data quality issues. low-quality data may lead to deviations in analysis results and even lead to wrong decisions. therefore, enterprises should establish a sound data quality management system, regularly clean, verify and optimize data to ensure the authenticity and reliability of data. third, strengthen data information security risk prevention and control. in the process of digital transformation, enterprises should establish a high level of data security awareness and formulate and implement strict data security policies. by adopting advanced encryption technology, access control means, etc., ensure the security of data during transmission, storage and use. first, at the organizational structure level, enterprises should set up special data management departments or positions to be responsible for data integration, application and risk management. by clarifying the division of responsibilities, we can ensure the effective promotion of various tasks. secondly, at the institutional level, enterprises should formulate a sound data management system and process to standardize the collection, storage, use and processing of data. through institutional constraints and guidance, we can reduce the risk of data abuse and leakage. finally, at the technical level, enterprises should continuously introduce and update data security protection technologies to enhance data security protection capabilities. building an intelligent financial technology application system in order to achieve the digital transformation of corporate finance and adapt to the needs of high-quality development of enterprises in the digital economy era, enterprises should actively integrate big data and intelligent technology with financial work and build a complete intelligent financial technology application system. for the selection of intelligent financial technology, it is necessary to give priority to software with high scalability and integration. by adopting financial analysis software with machine learning technology, the automatic processing of unstructured financial data of enterprises can be realized, and valuable information from financial data terminals can be deeply mined to provide accurate decision-making support for enterprise operations. in addition, enterprises should also actively solve the problem of “information islands”, promote the integration of talents under digital transformation, accelerate the deep integration of business systems and financial systems, and feed back procurement data and sales data to the financial system in real time by establishing a unified data center. enterprises should also actively formulate scientific technology plans, combine the actual development needs of enterprises, carry out phased technology introduction, give priority to the transformation of basic financial automation processes, and then gradually carry out intelligent technology introduction to ensure the sustainability and progressiveness of digital technology applications. build a digital financial management platform the construction of a digital financial management platform is an important part of promoting the digital transformation of corporate finance. enterprises should establish a unified financial data platform to integrate and centrally manage scattered financial data. the platform should have a powerful data integration function, which can integrate data from different business departments and subsidiaries into the same platform to achieve cross-departmental data sharing and collaboration. this integration can not only improve the consistency and accuracy of data, but also provide management with a more comprehensive financial view. the digital financial platform should have real-time processing capabilities and be able to collect, analyze and update financial data in real time. for example, with the help of a financial management platform, corporate managers can grasp updated financial information anytime and anywhere, helping the financial team to make decisions more quickly (ma et al., 2024). at the same time, automated data processing can also greatly reduce manual operations, further improving work efficiency while improving data accuracy. the digital platform should also support multi-dimensional data analysis, such as cost structure analysis, cash flow analysis, profitability analysis, etc. through these analyses, enterprises can dig deep into data, identify potential business opportunities and risks, and provide support for strategic decision-making. in addition to software construction, hardware facilities must also be improved accordingly. in order to ensure that the infrastructure can support the efficient operation of the service platform, enterprises need to upgrade and renovate the infrastructure including servers, operating equipment, network bandwidth, etc. to ensure that these facilities meet the corresponding standards. in the process of promoting the digital transformation of finance, enterprises should actively apply innovative technologies. for example, the application of blockchain technology in financial management can significantly improve data transparency and data security, especially in the links that require a high degree of trust such as contracts, payments, and audits. blockchain can prevent data from being tampered with and ensure information security. at the same time, with the help of cloud computing, enterprises can obtain more efficient pa ge 93 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 87-93, 2025 data storage and processing capabilities. the financial department can use the cloud platform to achieve crossdepartmental and cross-regional data sharing, achieve collaborative work, and further improve operational efficiency. “in addition, enterprises can also use artificial intelligence and big data technologies to analyze financial data, predict market trends and financial risks, and help enterprises make decisions more accurately. through the application of these innovative technologies, enterprises can not only optimize financial processes, but also gain an advantage in competition and improve their market responsiveness and strategic flexibility. it can be seen that the rational use of innovative technologies can significantly promote the digital transformation of corporate financial management. strengthening financial information risk management and control in the process of promoting the construction of digital financial management platform, the security management of financial information is an important part. first of all, it is key to establish information security awareness. enterprises should conduct regular training and drills to deepen employees’ understanding of information security and improve their ability to deal with security threats. enterprises should formulate training plans to help employees understand the latest cybersecurity knowledge and master basic response measures. at the same time, regular simulation drills should be conducted to test the response capabilities of enterprises by simulating network attack scenarios, so as to help enterprises find loopholes in various links and improve the level of information management. secondly, it is necessary to strengthen data control, improve the data management system, effectively integrate data, and achieve unified management, so as to further improve the security of data. enterprises should regularly screen existing data and use advanced technology for encryption processing to ensure the security of data transmission, storage and use. enterprises should strictly supervise data access, update authentication methods in a timely manner, and adopt multiple means to ensure that only authorized personnel can access sensitive data. enterprises should keep up with the forefront of technology, obtain the latest cybersecurity technology by hiring industry experts and third-party technology companies, improve the level of financial data protection of enterprises, and further enhance the risk resistance of enterprises. finally, companies need to conduct regular security assessments to identify and resolve potential risks and ensure the security of financial information. through these methods, companies can safely and effectively promote the construction of digital financial management platforms. conclusion in summary, the digital transformation of corporate finance has become an inevitable choice for enterprises to enhance their competitiveness in the digital economy era. through clear strategic planning, deep integration of technology and business, construction of talent teams and effective risk management, enterprises can cope with many challenges in the financial management process. with the development of the digital economy, enterprises should actively promote the digital transformation of finance, seek more opportunities for their development, and achieve efficient operation and long-term development. references alisher, s. 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(2024). a review of the potential opportunities and challenges of the digital economy for sustainability. innovation and green development, 3(4), 100174. sun, c., xu, m., & wang, b. (2024). deep learning: spatiotemporal impact of digital economy on energy productivity. renewable and sustainable energy reviews, 199, 114501. tan, l., yang, z., irfan, m., ding, cj, hu, m., & hu, j. (2024). toward low-carbon sustainable development: exploring the impact of digital economy development and industrial restructuring. business strategy and the environment, 33(3), 2159-2172. xia, l., baghaie, s., & sajadi, s. m. (2024). the digital economy: challenges and opportunities in the new era of technology and electronic communications. ain shams engineering journal, 15(2), 102411. yi, j., dai, s., li, l., & cheng, j. (2024). how does digital economy development affect renewable energy innovation?. renewable and sustainable energy reviews, 192, 114221. pa ge 1 pa ge 1 american journal of applied statistics and economics (ajase) postgraduate problems 1988 to 2000: ese-ipn case in mexico josé antonio villalobos lópez1* volume 1 issue 1, year 2022 https://journals.e-palli.com/home/index.php/ajase article information abstract received: august 15, 2022 accepted: august 26, 2022 published: october 02, 2022 this article is presented under the deductive method, in the hermeneutic paradigm, with a qualitative approach, of interpretative type and narrative design of topic. the escuela superior de economía: instituto politécnico nacional (ese-ipn) higher school of economics of the national polytechnic instituteoffers a master’s and doctorate in economics, starting courses in january 1970 and february 1986 respectively. during the last years of the last century, these postgraduate programs presented two complex problems: too long time between the completion of academic subjects and the achievement of the professional license, and they were not recognized by the programa nacional de posgrados de calidad (pnpc) -national quality postgraduate program. the first master’s degree card registration is kept until 1993 and the doctorate one is given in 2003, after completing the thesis and defense of the exam, taking up to fifteen years to obtain it. regarding the degree of excellence that the pnpc qualifies, the ese-ipn doctorate obtained this recognition until 2010, eighteen years after the first register of postgraduate excellence in the country was released, the master’s degree being in a similar situation. keywords doctoral degree, graduate student, master’s degree, public documents 1 instituto politécnico nacional: escuela superior de economía, méxico * corresponding author’s e-mail: jvillalobosl7500@egresado.ipn.mx introduction postgraduate studies are considered the pinnacle and culmination of the educational processes at the higher level, where the aim is to reinforce the training of the professionals required by the country to link them with the productive sectors of society, in aspects of science, technological development and innovation. the postgraduate program is potentially conceived as a series of methodologies to serve as a basis for research work or for the specialization and professionalization of human resources. until the end of the 1960s, postgraduate programs were scarce and insignificant in mexico, depending on foreign universities for the training of scientists and high-level professionals in the country. in 1970 there were only 13 institutions with graduate studies, with 226 programs offered and 4,088 graduate students (arredondo, 1989). the united nations educational, scientific and cultural organization (unesco, 2019) uses an international standard classification of education to divide higher education into: a) short-cycle tertiary education programs (level 5); b) the tertiary education degree or equivalent (level 6); c) master’s degrees, specializations or equivalents (level 7); d) the doctorate level or equivalent (level 8). these levels of higher education are recognized in mexico: > level 5: higher technician or professional associate, requires 180 credits. > level 6: bachelor’s degree, which is recognized with 300 credits. > level 7: divided into: 1) specialty, which requires 45 credits additional to the bachelor’s degree; 2) master’s degree, which requires 75 credits subsequent to the bachelor’s degree. the specialty is equivalent to 60% of the master’s degree, reaching the latter with 30 additional credits. > level 8: doctorate, equivalent to 150 credits after the bachelor’s degree, or 75 credits after the master’s degree, or 105 credits more if the specialty is taken as a starting point. until the end of the last century, according to reynaga obregón (2012), there was confusion and disorder in the country among the three recognized postgraduate levels, mentioning that there was no consensus on the specific requirements for accrediting each of them. sometimes a specialization was more rigorous than a master’s degree, or this in turn could be more rigorous than a doctorate, depending on the institutions offering these postgraduate programs: “this has led to the fact that on a national scale we have specialties that are taught as master’s degrees, master’s degrees that give the appearance of being a re-medial process of a bachelor’s degree, or that we sometimes accept international doctorates that could be studies equivalent to bachelor’s degrees”. the work is presented under the deductive method, following a hermeneutic paradigm, based on the experience on the subject. the approach addressed in the article is qualitative, since descriptions of specific situations are made, of interpretative type and narrative design. results and discussion registration and qualification of postgraduate studies by educational authorities for unesco (2019), specialization and master’s programs are oriented to develop research skills, are usually essentially theoretical and sometimes include practical training, while doctoral programs are oriented to advanced research, usually concluding with the presentation and defense of a thesis, which must make a https://journals.e-palli.com/home/index.php/ajase mailto:jvillalobosl7500%40egresado.ipn.mx?subject= pa ge 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-10, 2022 significant contribution to a specific field of knowledge. it is considered that postgraduate studies began to be offered on a massive scale in the country in 1970. according to alcántara santuario & canales sánchez (2004) in 1969 there were 5,011 postgraduate students in the country, representing 2.6% of the total number of higher education students in the country, of which 1,677 were for specialties, 2,802 for master’s degrees and 532 for doctorates. by institution, these students were: universidad nacional autónoma de méxico (unam) 3,055 (61%); instituto tecnológico y de estudios superiores de monterrey (itesm) 698 (13.9%); universidad autónoma de nuevo león (uanl) 408 (8.1%); instituto politécnico nacional (ipn) 294 (5.9%); centro de investigaciones y estudios avanzados (cinvestav-ipn) 121 (2.4%); universidad autónoma chapingo 200 (4%). these six universities absorbed 95.3% of the students enrolled. the total enrollment of postgraduate students in 1970 was 5,953 students, while in 1980 and 2000 there were 25,502 and 118,090 students, respectively, according to anuies (reynaga, 2002). this represents an exponential increase of twenty times in the number of graduate students registered in 30 years (from 1970 to 2000) or 4.6 times in 20 years (from 1980 to 2000). in 1970, postgraduate programs were offered by 13 institutions in the country, with 75% of enrollment concentrated in the federal district, where unam absorbed 56% of the total, ipn 8%; the state of nuevo leon was the next in postgraduate offerings with 20% of the total, with uanl and itesm standing out (kent et al, 2001: 66). of the country’s total graduate enrollment, mexico city had 75.5% in 1970; 57.8% in 1980; 43.55% in 1990 and 35% in 2000; where the remaining percentage was concentrated in universities in other states (alcántara & canales, 2004: 119). thus, it can be seen that the federal district absorbed 3 out of every 4 graduate students in 1970, but only 1 out of every 3 students in 2000, despite the fact that three of the five largest public universities in the country are concentrated in the capital: unam, ipn and universidad autónoma metropolitana (uam). the 1985 the asociación nacional de universidades e instituciones de educación superior (anuies) national association of universities and institutions of higher educationin statistical yearbook records a total of 37,040 graduate students, with 3% in agricultural sciences, 6.7% in natural and exact sciences, 29.5% in health sciences, 36.3% in social and administrative sciences, 10.2% in education and humanities, and 14.3% in engineering and technology. the same yearbook shows that the federal district had 52.4% of the students at this level. the main problem of postgraduate programs at the end of the 1980s was the graduation of the graduates, since the great majority of people who studied them in the country did not manage to formally complete their academic degree, as arredondo galván (1989) would say in this regard: “there is, however, a great concern for the low terminal efficiency of graduate programs...the concern is centered, above all, on the scarce graduates’ degrees, as well as on specifying the times for obtaining academic degrees... ” the integral program for the development of higher educationwas approved in 1986 by the general assembly of the anuies, where a specific part was contemplated for postgraduate education (mendoza, 1989). in 1988, the document declarations and contributions for educational modernization (anuies, 1990), with a section on postgraduate education that stated: “in recent years, postgraduate programs have undergone enormous growth and diversification. however, in many cases the conditions in which these programs are carried out do not guarantee a minimum of quality”. in the national program for educational modernization 1990-1994 (1990), the degree of concentration of postgraduate programs was noted, especially in social and administrative areas, highlighting: a) during the last five years, the relative participation of technological areas in postgraduate studies decreased; b) 50% of the enrollment is located in the federal district, 32% in five states and the remaining 18% in 23 other states; c) 81% of researchers in the technological-logical area are concentrated in the metropolitan area of the federal district. in 1991, the consejo nacional de cienciay tecnología (conacyt)-national council of science and technologyimplemented the graduate programs of excellence, acquiring transcendental importance in its supervision of graduate studies, giving them a boost and orientation towards research, reorienting the resources destined to the projects financed and above all to the scholarships granted to students. when the programa nacional de posgrados de calidad (pnpc) -national quality postgraduate programwas issued in 1991, only 424 postgraduate programs were approved, one fourth of the postgraduate programs existing in the country. the requirements of the pnpc are increasingly greater and by the year 2000 only 406 graduate programs were registered, which means a drop of 18 programs in a decade (4.2% decrease). the number of doctoral programs increased from 118 in 1991 to 151 in 2000, with 33 more programs at this level in a decade (28% increase). the case of graduate programs at the ese-ipn graduate section on may 11, 1970, the ese-ipn graduate section began its activities offering a master’s degree in science with a specialization in industrial economics. according to dean ruiz suárez (ipn, 2012: 14) in that cycle 22 students enrolled, who had to apply for financing through the banco de méxico -bank of mexico-, since tuition was onerous for that time: $2,000 pesos. the dean of the ese-ipn reminds us that sometime later the master’s degree was sponsored by other governmental institutions, such as the ministry of finance and public credit, the ministry of industry and commerce, and national https://journals.e-palli.com/home/index.php/ajase pa ge 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-10, 2022 financial. the $2,000 pesos of tuition in 1970, after making some calculations using consumer price indexes (aguirre, 2022), would be equivalent to $18,450 pesos in november 2021. according to the gazette polytechnic of january 31, 1970 (ipn, 1970), the master’s degree in industrial economics was the first postgraduate program in latin america to be offered on this subject. the ese-ipn graduate section incorporated the master of science with specialization in international trade and economic development in 1973 and 1974 respectively, offering for the first time the phd in economic sciences in february 1986. the specialty of financial economics was offered until 1998, which eventually changed its name and now the specialty taught is financial risk management. in order to establish a general regulation for postgraduate studies, in 1965 the first postgraduate regulations were approved at the ipn. in 1981, a new organic law of the ipn came into force and on november 16, 1982, the general advisory council approved the second regulation for graduate studies, while lópez portillo was still president of mexico. in 1983, the regulations for graduate studies were modified once again, as part of president miguel de la madrid’s modernization program (tena, 2008). in may 1991, a new regulation of graduate studies was approved, which was in force for fifteen years, and in 2006 substantial modifications were made to these graduate regulations (ruiz et al, 2006: 43-44). finally, on september 15, 2017, the new regulations for graduate studies of the ipn came into force. the postgraduate programs offered in mexico in the area of social sciences and especially in the division or subject of economics and development (related subject), according to kent serna et al (2001: 100) since 1980 were as follows: o in 1980 there were 14 master’s programs dedicated to economic-development, representing 9% of all social science programs; while in 1997 there were 58 programs (10.1% of social sciences). o in 1980 there were 2 doctoral programs dedicated to economic-development, representing 9.5% of all social science doctoral programs; while in 1997 there were 8 programs (15.4% of the social sciences). o in 1997, of the total number of programs offered in the area of economics-development: 2% corresponded to specialties; 91.1% to master’s degrees; and 6.9% to doctorates. of the 14 master’s degree programs registered in the area of economics-development in 1980, the ese-ipn graduate section had a master’s degree in science with its three specialties: industrial economics, international trade and economic development. in 1980, the two doctoral programs that existed in this area corresponded to the economics program offered by the faculty of economics (unam) since 1976 and the socio-economics, statistics and informatics program offered by the postgraduates college since 1979. until february 1986, the doctoral program in economic sciences was offered at the ipn, making it the third doctoral program in the economic area offered in the country. by 1997, eight doctoral programs in the area of economics and development were being offered. arredondo g. (1989) mentions that at the end of the eighties, most postgraduate programs were located within the same academic and administrative structure of the undergraduate schools or faculties, causing an almost inseparable inertia to be observed between the undergraduate and postgraduate programs, where the ways of teaching and evaluating were so similar, not to say that they were completely the same, observing that professors in the same school cycle taught undergraduate and postgraduate courses. garcía, j. (1995) agrees with this panorama, stating that the postgraduate sub-system was mixed with the undergraduate in academic-administrative procedures in several universities, for example, in some cases both levels of higher education share a budget, as well as professors and physical space. it should be noted that there should be a level of differentiation between undergraduate and graduate programs, where the latter should have a division, council or directorate to manage academic and administrative matters. in this aspect, the then ese-ipn graduate section (now graduate studies and research section) was so independent from the undergraduate area that at the beginning of the 1980s, the semester in the graduate section began in the first days of august, while the semester in the undergraduate area began in october (two months later), so that if a student from the school itself wanted to study a master’s degree, he had to wait four additional months to begin his studies. another point of independence of the bachelor’s and master’s degree areas at ese-ipn was that bachelor’s degree graduates did not have an automatic pass to the master’s degree, but had to take the propaedeutic course just like applicants from other institutions, noting that the mathematics content in these courses was demanding, therefore, a little more than half of the students enrolled in the master’s program came from engineering areas or from foreign countries (colombia, ecuador, peru, dominican republic, bolivia, costa rica), for which they were not as “severe” when it came to accrediting the propaedeutic course. in reference to the fact that they could be the same professors for the bachelor’s and master’s degrees at eseipn, no undergraduate professors were found teaching graduate classes, nor were undergraduate professors who were graduate students, at least from 1983 to 1987. regarding the physical space, the ese-ipn graduate section was located on the second floor of the building, where almost no undergraduate students circulated and it felt as if there was ‘a door’ when accessing the graduate part, where it had its own library, offices for student services, as well as its own auditorium and since 1986 it https://journals.e-palli.com/home/index.php/ajase pa ge 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-10, 2022 had its own computer center. in addition, its classrooms were totally different from those of the undergraduate part, where the professor sat in the center and the students in a half circle in how many benches (15 per classroom). before the end of the eighties, a small annex building was built exclusively for the graduate area. in the ese-ipn graduate section, the highest body that made decisions on academic and technical aspects was the college of professors, where only the head of the section, the professors and students representing the three specialties of the master’s program could participate. i never heard of any pressure exerted by the school administration or any other educational authority. arredondo g. (1989) mentions that at unam the general examination regulations governed students from high school to postgraduate level, where it was established that class attendance was not compulsory for high school and undergraduate levels, since there were extraordinary exams (called at some time at the ipn “proficiency examination”), while at postgraduate level class attendance was compulsory, since there were no extraordinary exams to accredit the subjects at that level. in this aspect, the same requirements were applied in the ipn graduate programs, since only ordinary subjects could be accredited, and if the subject was not accredited, it would have to be taken again in another period. also, the 1982 ipn graduate regulations established that the minimum passing grade for studies at that level was eight on a scale of ten, stating that if two different subjects were failed or the same subject twice, graduate students would be automatically dropped from the program. in general, in 1986, some graduate courses continued to be taught by personnel with bachelor’s degrees, since there were no better qualified academics due to the growing number of students registered in the graduate program; 15% of the professors had bachelor’s degrees and were teaching in some graduate program (garcía, 1995). it was also common for professors with master’s degrees to teach at the doctorate level, since there were not as many professors with the highest degree. in the case mentioned, since the ese-ipn graduate section worked independently, there was no case of a professor with a bachelor’s degree teaching at the postgraduate level. what i saw in the first generation of the doctoral program was that the subject of computer science was so new at the beginning of 1986 that one of the graduates of the master’s program (candidate) had to serve as a tutor for that course, since he was an expert in the subject and had dedicated himself professionally to it. master’s and doctoral graduates who had completed all the credits of the academic program, with the exception of the thesis with its respective presentation of the degree exam, were called candidates, where they used to put the initial ‘c’ after the academic degree, to denote that they did not have the respective title or degree. ruiz gutiérrez et al (2006) show that the educational institutions that offered the most doctorates in mexico in 2000 and those that were classified as of academic excellence in the pnpc were the following: o unam: 32 programs, of these 25 included in the pnpc (78%) o uanl: 23 programs, of which 5 were registered in the pnpc (22%). o cinvestav (ipn): 22 programs, the only institution with 100% endorsed in the pnpc. o universidad de guadalajara (udeg): 20 programs, 9 registered in the pnpc (45%). o ipn: 18 programs, 8 registered in the pnpc (44%). o uam: 15 programs, 11 included in the pnpc (73%). o college de méxico: 6 programs, 5 registered in the pnpc (83%). as can be seen, the country’s top university (unam) had the largest number of doctoral programs in 2000, with more than three quarters of them having academic quality recognition. ipn’s cinvestav ranked second in doctorates offered in the country in that year, besides being the only institution that has all its doctorates recognized for excellence in the pnpc. the ipn and the university of guadalajara (udeg) have similar figures in terms of doctoral programs offered, where both universities had less than 50% of their doctorates in the pncp excellence list. uam also had about three quarters of its doctoral programs recognized as high quality by conacyt, which speaks of the spirit of teaching-research. it is also noteworthy that the college de méxico has five doctoral programs rated as excellent out of the six it offered. adding the doctoral programs offered by cinvestav and the ipn would give the figure of 40 doctoral programs offered by both institutions in 2000, becoming the institution with the largest number of doctoral programs, as well as 30 programs recognized as excellent by conacyt, placing it in first place nationally. the diagnoses made in the national program for the improvement of graduate studies in 1988 by anuies and in the national program for educational modernization 1990-1994 of the sep were carried out in an adequate and timely manner. the problem is that no transition period was contemplated between the programs offered and the new educational quality requirements, leaving a good part of the postgraduate programs offered at the ipn in an impasse or trance, as was the case of the master’s degree programs in economics offered since 1970 and the new doctorate program in economics offered since 1986. the problem of the ipn’s postgraduate programs in economics was located in the registration before the dirección general de profesiones (dgp) -general directorate of professionsof the secretaría de educación pública (sep) –ministry of public education and in the national program of quality postgraduate programs (pnpc) of conacyt, in such a way that the postgraduate programs offered at the ese-ipn remained in the ‘limbo’ of recognition for a period of time. in this undefined space, it was not possible to process degree certificates, nor was it possible to obtain financing or https://journals.e-palli.com/home/index.php/ajase pa ge 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-10, 2022 scholarships. casanova del ángel (2006) shows that at the end of the 1980s, the educational authorities did not allow graduates of the escuela superior de ingeniería y arquitectura (esia-ipn) –school of engineering and architecture to obtain masters and doctorate degree certificates: “at the end of the 1980s, the general directorate of professions of the ministry of public education, through the department of educational institutions, informed the polytechnic institute that it was not going to issue any graduate certificates to ipn graduates because most of its master’s and doctoral programs were not duly registered”. in this regard, casanova (631-632) reminds us that the esia-ipn carried out several necessary procedures and on november 8, 1988, the sep issued several addendum communiqués for the registration of master’s degree programs (environmental engineering, architecture, soil mechanics and geology), later adding two more master’s degree programs. tena núñez (2008) shows us that in 1991 the ipn advisory council approved the new regulations for postgraduate studies and research, where changes were introduced in the operation of postgraduate sections, academic programs and teaching staff. also, with the entry into force of the conacyt’s national postgraduate program of excellence, criteria for rigorous evaluation of esia-tecamachalco’s postgraduate programs would be incorporated, such as the following: 1) adjustment of student records in school control, causing the opening of an audit related to the purging of files; 2) repatriation program for ipn scholarship holders who were studying abroad; 3) appointment of 10 new full-time positions for teachers who had completed master’s and doctoral studies, thus preventing professors without a master’s or doctoral degree from teaching courses in postgraduate programs. another factor that could have influenced the problems experienced by the ipn’s economics graduate programs could have been the application of neoliberal policies that salinas de gortari had been promoting since he became president, where this current of thought took complete control of public power, including the field of education, science and technology. in this sense, p. bordieu and s. khan (1986; 2012; cited by garrido, 2017) express that universities are vital to understand the elite in power in a country, where they play a crucial role in the creation and distribution of social, cultural and knowledge capital that the elites themselves need to emerge, survive or renew themselves. on this subject, garcía escalante (2008 quoted by villalobos, 2021) believes that the mexican government, during the last years of the 1980s, adopted the american model of postgraduate education, abandoning and rejecting centuries of european tradition, where from that time onwards, the performance of education began to be measured based on the number of articles and books published, where the neoliberal model would also establish profitability in higher education institutions as a reward, the author adds: “during the government of salinas de gortari (1988-1994) ‘the free market’ was introduced in higher education, which caused a proliferation of private ies -institutions of higher education-. these centers of education were given the generic term ‘university’ without having the necessary infrastructure, and without the universality that should distinguish universities from lyceum”. in the diagnoses of anuies and conacyt, it was mentioned that in the 1980s, postgraduate degrees in engineering and physical sciences were in decline, so it was necessary to promote them through the state’s educational policies, since young people had opted to study postgraduate degrees in administrative and economic areas in order to improve their labor position or obtain higher salaries. in this regard, adalid, c. (2011) points out that the classification of excellence was not very well received by some academics and researchers in the social and administrative sciences, where a large number of programs in the natural and exact sciences were included, as well as a considerable increase in the number of postgraduate programs offered in private institutions. similarly, padilla tirado & barrón magaña (2013) indicate that programs in psychology, psychology, economics and education, among others, were punished by the authorities in terms of funding and rating of excellence. according to the standards of quality postgraduate programs elaborated by conacyt, reyes garcía (2006) mentions that the ipn had these programs considered in this select list: in 1991: 21 master’s degrees and 6 doctorates; in 2001: 15 master’s degrees and 10 doctorates; and in 2004: 13 master’s degrees and 6 doctorates. as can be seen, very few ipn programs had been considered of quality by conacyt. the worst happened 13 years after that first evaluation in 1991, in 2004 the ipn only had thirteen master’s programs that were considered of quality by conacyt, which means that eight master’s programs lost that approbatory quality, which represented 38% of the master’s programs approved in 1991; the number of doctorates grew ten years later, but there were only six approved thirteen years later. according to data from the pnpc (conacyt; cited by tinajero, 2005), the ipn in 2000 was not going through its best times in terms of postgraduate quality, since it only had 4 consolidated master’s degrees (highest quality level), while it had 11 conditioned master’s degrees; in doctorates only 2 were considered consolidated and 5 conditioned. continuing with the same source, in the year 2000 there were only three doctoral programs in economics with excellent quality in the whole country, which were classified as emerging, so i can infer that this included the doctorate in economics offered by unam. muñoz garcía & suárez zozaya (2004) prepared a table with data from the 2000 census, as well as data from anuies and conacyt, where i highlight the following: https://journals.e-palli.com/home/index.php/ajase pa ge 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-10, 2022 o total population of the country 97’014,867, with graduate studies 388,397 inhabitants. this means that 0.4% of the country’s population had postgraduate studies. o academic personnel in higher education institutions 192,406 teachers, with postgraduate degrees 55,570 teachers, 28.9% of whom had postgraduate degrees in 2000. o students studying for doctorates 8,407 in 1999. students with doctoral degrees 1,069 in 2000. from this information we can see that out of every thousand inhabitants of the country, only four had access to postgraduate studies in the year 2000; we can also see that 3 out of every 10 professors of higher education institutions studied for a postgraduate degree. doctoral students who obtained their degree and academic degree represent one eighth. from the information provided by díaz de cossío (2002; cited by de la peña & tello, 2004) for the year 2000, i take it that 56 public or private universities in the country offer a degree in economics. in february 2010, the polytechnic gazette (ipn, 2010) reported that 12 more postgraduate programs were recognized by the pnpc, bringing the ipn’s total to 66 postgraduate programs registered in conacyt’s list of excellence, increasing its recognition by 22%. these postgraduate programs, together with cinvestav’s 53, add up to 119 options registered in the national postgraduate studies excellence list, making ipn the second national university offering more postgraduate studies, behind the unam (national autonomous university of mexico). the ipn went from 25 graduate programs recognized in conacyt’s pnpc in 2000 to 54 programs in 2009, the following year (2010) they reached 66, which shows a growth of 1.64 times in its quality graduate programs in a decade. ipn postgraduate director trujillo ferrara (ipn, 2010: 5) informed that four units that offer postgraduate studies entered the pnpc for the first time, among them the ese-ipn with its doctoral program. this shows that it took eighteen years for its doctoral program in economics to be recognized as being of excellence by the pnpc. the most serious problem i see in the postgraduate programs offered during the last two decades of the last century is the lack of a degree or the complete completion of studies, with a large majority of them being presented as candidates for the degree. with information from the anuies statistical yearbooks, we can see that there were 3,033 graduates with doctoral degrees in the country in the 2010-2011 school year, of which 1,120 corresponded to degrees awarded at universities in mexico city (36.9% of the total). in the latest data available, for the 2020-2021 school year, anuies reports that 8,439 students graduated from the doctoral program in the country, of which 2,098 did so in universities in mexico city (24.9%). on an annual basis, the number of doctoral degree graduates in mexico almost tripled from 2000 to 2010, while the annual number of 2020 doctoral degree graduates grew six times in relation to the number of 2010 graduates. it is also observed that the number of doctoral graduates in 2020 in mexico city decreased by one third in relation to the number of graduates in 2010. information on ese-ipn postgraduate graduates according to public information provided by the national polytechnic institute in its repositories: online catalog (ipn, 2021) and information on professional licenses from buholegal monitoring of judicial activity (2021), this information is derived from graduates of the graduate studies and research section ese-ipn: > the first master of science degree with a specialty in industrial economics was awarded to héctor allier c. in 1993, presenting the thesis ‘the exponential law as a statistical model of survival in systems reliability’ in 1992. h. allier already appeared in the master’s degree prospectus of the graduate section (1981) as a candidate for the master’s degree. this shows that the certificate issued by the general directorate of professions of the sep took at least twelve years after the completion of his studies to be granted. > the second certificate for a master’s degree in science with a specialty in industrial economics was registered in 1996, to a student who had presented his thesis in 1982, which meant that it took fourteen years to obtain the sep certificate. the third master’s degree certificate was registered in 1999 and also corresponded to a thesis presented in 1982, which took 17 years to obtain the certificate. > the fourth master’s degree that i recognized is that of socorro sánchez i., of the 1983 to 1985 generation, who completed her thesis and exam in 1993, obtaining her master’s degree in science with a specialty in economic development in 2001, which took her 8 years to obtain her certificate after completing her professional exam, obtaining her degree certificate 16 years after completing her studies. > the fifth master’s degree was obtained in 2002, when the thesis had been presented in 2000. the sixth certificate was obtained in 2003 and corresponds to the first of the master of science with a specialization in international trade, having presented the thesis and degree exam in 2001. from these experiences it only took two years to obtain the sep’s degree certificates. > as for the doctoral program, the first certificate registered corresponds to marcos portillo v., from the first 1986-1988 generation of the doctoral program, who presented the thesis ‘econometric model for agricultural technology adoption under ecologically restrictive conditions’ in 2000, obtaining the ipn doctoral degree in economics in 2003, obtaining his degree certificate fifteen years after completing the academic part. > the second doctoral degree in economics identified was obtained in 2006, where the thesis work was presented in 2002, so the process took four years to obtain the degree certificate. > the third identified doctoral degree in economics https://journals.e-palli.com/home/index.php/ajase pa ge 7 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-10, 2022 was obtained in 2008, where the doctoral thesis was presented in 2007, so the process was normal and it only took one year to register the degree with the sep. with the same sources of information, i will present information that could not be confirmed in obtaining a graduate certificate, but nevertheless presented thesis and degree exam at the ese-ipn, so that the complexity of the regularization of graduate studies at this school can be observed: > in 1973 the first exam for a master’s degree in science with a specialty in industrial economics was presented, but no degree certificate was obtained. in 1977 and 1980, two graduates presented theses and took their master’s degree exams, but did not obtain their respective certificates. > in 1981, a student from colombia presented a thesis and took the examination for a master’s degree in science with a specialization in economic development. foreign students cannot obtain a professional license in the country. > in 1986, two foreign students (dominican republic and ecuador) presented their thesis and took their master’s degree exams. in 1989 two foreign students (ecuador and peru) presented their master’s degree exam and thesis. the peruvian student was able to legalize his papers in mexico and the degree he obtained in that country is recognized with a mexican professional certificate in 2002, while in 2004 he obtained a master’s degree certificate from the ipn. > in 1990, two graduates presented thesis and degree exam, but neither obtained a degree certificate. in 1992, three graduates presented a thesis and a master’s degree exam, but none of them obtained a professional certificate. in 1993, another two graduates took their master’s degree exams and neither of them obtained a professional license. > the first doctoral examination in economics was given in 1991, very close to the date of completion of the first academic generation (1988), but there is no record of a degree certificate for this first graduate. > in 1992, a foreigner (dominican republic) presented a thesis and took a doctoral degree exam in economics, not requiring a professional degree certificate. these first two graduates belonged to the first generation of 19861988 doctoral graduates. > in 1995 and 1996, two doctoral theses and exams were presented, in which neither of the two graduates had a professional degree certificate. > in 1999, another graduate from a foreign country presented a doctoral thesis and examination. in 2001 and 2002, theses and degree exams were presented, where neither of the two graduates has a professional degree certificate. as can be seen from this information, the process of obtaining professional licenses for master’s degrees taken at the ese-ipn took up to fifteen years, as i noted, it was not until 1993 that the first license was issued for the master’s degree in industrial economics. although there was the will to completely close the end of graduate studies, in those years there was confusion and extreme complexity for the final processing of degrees and degree certificates. from 2002 onwards, the certificates for the master’s degrees taught at the ese-ipn were processed regularly, so i can affirm that since that year the complex situation of years of delays experienced by the first graduates in previous years to obtain the much-prized degree certificate granted by the general directorate of professions of the sep was unblocked. as of 2008, the complex and late processing of the ese-ipn’s phd in economics degree certificates became normal and it only took one year for a graduate to complete the process of obtaining his degree certificate. thus, from that year on, the chaotic and complex situation experienced by the first graduates of the doctoral program came to an end. in the research study carried out on the graduates of the doctorate in administrative sciences of the escuela superior de comercio y administración (esca-ipn) school of commerce and administration-, conducted by garduño román & ruiz saúl (2003), it is pointed out that the problem and weakness of many of the doctoral programs is fundamentally the obtaining of the degree, adding that by the beginning of the century less than 25% of the students who enroll in a doctorate program complete their studies. in the reference study (garduño & ruiz, 2003) they found: a) age: average age of graduates 43.3 years; b) average grades were 9.65; and c) time to graduate after graduation: 0.86 years. this shows that esca’s doctoral graduates conclude their studies in a short time (one year), after completing the academic subjects, in contrast to what happened eight years earlier: “this is very encouraging for the tutorial model, since some figures found in 17 graduates of different generations of the school program, in force from 1965 to 1994, showed that the time to graduate after finishing the credits was between 6 and 17 years, which broke with the time to obtain the degree established in article 88 of the regulations of postgraduate studies of the ipn”. from the esca study, it can be seen that until 1994 they had problems due to the long time it took for graduates of their doctoral program to obtain their degree, the same as those registered in the ese, but the first school was able to solve them fifteen years earlier. currently, the master’s and doctoral programs at eseipn already have the degree of excellence granted by the national program for quality graduate studies (pnpc). in this sense, i constructed a table with information issued by the statistical yearbooks of the national association of universities and higher education institutions (anuies) for the 2010-2011, 2019-2020 and 20202021 school years (the last year that has information). the specialty taught in the graduate studies ese-ipn is financial risk management. the information presented shows that for the 20102011 school year, the master’s and doctoral programs offered by ese-ipn reached a total of 108 students, of https://journals.e-palli.com/home/index.php/ajase pa ge 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-10, 2022 table 9: pattern of communication change in the rural society cycle postgraduate total new admission who terminated with title 2010-2011 phd. 34 14 11 5 masters 74 22 24 20 suma 108 36 35 25 2019-2020 phd. 17 2 4 8 masters 58 14 19 27 speciality 21 8 7 4 suma 96 24 30 39 2020-2021 phd 16 0 7 5 masters 55 16 20 19 speciality 22 8 12 11 suma 93 24 39 35 which 36 were new entrants and 35 graduates, achieving stability between new entrants and graduates; the master’s program had 20 graduates and the doctoral program had 5 graduates. for the 2019-2020 cycle, 96 students were enrolled in the postgraduate program, where it is observed that there are already students in the specialty, with a decrease of twelve students in relation to nine years ago. this year, 27 master’s degree graduates are pre-sentenced, seven more than in 2011. at the doctoral level in 2019-2020, there was a drop of 17 students compared to nine years ago, with 8 doctoral graduates in that year, three more than in 2011. where there is a sharp drop is in the number of new students entering the doctoral program (2 vs. 14) compared to nine years ago, even before the covid-19 pandemic hit at its peak. in the 2020-2021 cycle, 93 students are observed in the ese-ipn postgraduate programs, dropping a total of three students in relation to the previous year, with similar numbers for the three levels: specialty, masters and doctorate. the number of specialty and master’s degree graduates is 28, compared to 31 the previous year. the number of new specialty and master’s degree students is 24, the same as the previous year. at the doctoral level in the 2020-2021 cycle, there is one less student enrolled than in the previous year, a drop of more than 50% compared to ten years earlier. the number of ph.d. graduates drops to 5, keeping the same number as ten years ago. what would be of concern is that there are no new students enrolled in the doctoral program in this last school year. conclusions in the last fifteen years of the last century there has been a substantial and exponential increase in the number of students enrolled in graduate programs throughout the country, particularly in mexico city. since 1984, it was sought that postgraduate programs be evaluated and qualified by national educational bodies. in 1991, the first evaluation of conacyt’s national program of quality postgraduate programs took place, where only a quarter of the postgraduate programs offered in the country could count on this qualification of excellence, the ipn being one of the most punished public universities in its qualification. the master’s degree in science with a specialty in industrial economics, which opened in january 1970 at the eseipn, was the first master’s degree offered on this subject in latin america. the doctorate in economics, which began in february 1986, was the third doctoral program in economics to be offered in the country, after the one offered by unam and the graduate school. in 1988, the first warnings were given of how the qualifications of the postgraduate programs would be from then on, when sep educational authorities notified at least two ipn schools that their postgraduate programs were not duly registered, namely the esia and the ese. with the arrival of salinas de gortari to the presidency, the arrival of the neoliberal current in the application of public policies in the country became evident, and was also felt in the field of academia and higher education. for graduates of the ese-ipn master’s program, the processing of professional certificates was regularized until 2002, taking a year or a little longer to obtain this important document, after the presentation of the thesis and the exam it entails, while doctoral certificates were regularized in 2008. as for the degree of excellence granted by conacyt’s national program of quality postgraduate programs (pnpc), the ipn’s phd in economics obtained this recognition in 2010, eighteen years after the first postgraduate degree of excellence was issued. from the same polytechnic gazette that informs about the doctorate, it is inferred that the qualification of excellence of the ipn’s master’s degree in economics is also given in those years. references adalid, cm, & de urdanivia, d. (2011). conacyt and postgraduate: evaluation and quality policies. management and strategy magazine , 1(40), 87-98. http://zaloamati. azc.uam.mx/bitstream/handle/11191/2972/conacyty-el-posgrado-politicas-de-evaluacion-y-calidad. pdf?sequence=1 https://journals.e-palli.com/home/index.php/ajase pa ge 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-10, 2022 rmie-22-72-00295.pdf instituto politécnico nacional (1970). gaceta politécnica [polytechnic gazette]. núm. 43. publicada 31 de enero de 1970. méxico. instituto politécnico nacional (2010). gaceta politécnica [polytechnic gazette]. núm. 774. año xlvi, 12. https://docplayer.es/85150133-ipn-excelencia-en66-posgrados.html instituto politécnico nacional (2012). manual de organización de la dirección de capital humano [organizational manual of the human capital department]. méxico https://repositoriodigital.ipn. mx/bitstream/123456789/14185/3/mo_sip_2012. pdf instituto politécnico nacional (2021). repositorios: catálogo en línea. recursos digitales [repositories: online catalog. recursos digitales]. méxico. https:// www.ipn.mx/bibliotecas-publicaciones/recursosdigitales/repositorios/ kent serna, r. & álvarez mendiola, g. & ramírez garcía, r. (2001). capítulo ii: el desarrollo del posgrado en méxico en las décadas ochenta y noventa [chapter ii: the development of postgraduate education in mexico in the eighties and nineties]. in. garcía de fanelli, et al: entre la academia y el mercado. posgrados en ciencias sociales y políticas públicas en argentina y méxico [between academia and the market. postgraduate studies in social sciences and public policies in argentina and mexico]. méxico: colección biblioteca de la educación superior: serie investigaciones. google books. https://books.google.es/ mendoza rojas, j. (1989). informe de los proyectos nacionales del proides [proides national projects report]. revista anuies, 69. http:// publicaciones.anuies.mx/acervo/revsup/res069/ info069.htm muñoz garcía, h. & suárez zozaya, h. (2004). la ciencia en méxico: desarrollo desigual y concentrado [science in mexico: unequal and concentrated development]. in coord. ordorika, i.: la academia en jaque. perspectivas políticas sobre la evaluación de la educación superior en méxico [the academy in check. policy perspectives on the evaluation of higher education in mexico]. miguel ángel porrúa (pp. 131-174). https://www.researchgate.net/ publication/272740355 padilla magaña, r. & barrón tirado, c. (2013). políticas de acreditación y calidad en el posgrado. homogeneizar la diferencia [accreditation policies and quality in postgraduate education. homogenizing the difference]. in coord. barrón tirado, concepción & valenzuela ojeda, gloria: el posgrado. programas y prácticas [the postgraduate program.]. méxico: unam (pp. 1341). http://132.248.192.241:8080/jspui/bitstream/ iisue_unam/77/1/pol%c3%adticas%20de%20 acreditaci%c3%b3n%20y%20calidad%20en%20 el%20posgrado.pdf de la peña mena, j. & tello, n. (2004). economía nacional aguirre botello, m. (2022). evolución del salario mínimo en méxico de 1935 a 2021 [evolution of the minimum wage in mexico from 1935 to 2021]. méxico maxico. http://www.mexicomaxico.org/voto/salmininf.htm alcántara, a., & canales, a. (2004). tendencias y disyuntivas en la evaluación del posgrado. la academia en jaque. perspectivas politicas sobre la evaluacion de la educacion superior en mexico. mexico. unam-miguel angel porrua, 113-130. https://www. researchgate.net/publication/272740355 arredondo galván, v. (1989). evaluación y acreditación de los programas de posgrado [evaluation and accreditation of graduate programs]. retrieved july-september 1989. anuies journal. http:// publicaciones.anuies.mx/acervo/revsup/res071/ info071.htm asociación nacional de universidades e instituciones de educación superior (1990). programa nacional para el mejoramiento del posgrado. anexos orientaciones programáticas [national program for the improvement of graduate studies. annexes programmatic orientations]. mexico http:// publicaciones.anuies.mx/acervo/revsup/res073/ info073.htm buholegal recursos jurídicos (2021). búsqueda de cédula profesional [search for a professional license]. méxico. https://www.buholegal.com/consultasep/ casanova del ángel, f. (2006). desarrollo del posgrado en la escuela superior de ingeniería y arquitectura del instituto politécnico nacional [postgraduate development at the school of engineering and architecture of the national polytechnic institute]. el portulano de la ciencia, 2(16), 623-640. https://www. academia.edu/3631956/ garcía, j. maría (1995). el desarrollo del posgrado en méxico: el caso de los sectores público y privado [the development of postgraduate education in mexico: the case of the public and private sectors]. revista latinoamericana de estudios educativos, 25(1), 107-130. http://www.cee.edu.mx/rlee/revista/r1981_1990/r_ texto/t_1990_1_05.pdf garduño román, s. & ruiz saúl, l. (2003). perfil del egresado del doctorado en ciencias en especialidad en ciencias administrativas de la escuela superior de comercio y administración, santo tomás del instituto politécnico nacional [profile of the graduate of the doctorate in sciences in specialty in administrative sciences of the superior school of commerce and administration, santo tomás of the national polytechnic institute]. investigación administrativa, 32(92). https://biblat.unam.mx/hevila/ investigacionadministrativa/2003-04/vol32-33/ no92/4.pdf garrido de sierra, s. (2017). la educación de los mandarines mexicanos, 1970-2014 [the education of mexican mandarins, 1970-2014]. revista mexicana de investigación educativa, 22(72), 295-324. http:// www.scielo.org.mx/pdf/rmie/v22n72/1405-6666https://journals.e-palli.com/home/index.php/ajase pa ge 10 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ.1(1) 1-10, 2022 y educación [national economy and education]. in coord. didriksson, a. & campos, g. & arteaga, c.: retos y paradigmas: el futuro de la educación superior en méxico [challenges and paradigms: the future of higher education in mexico]. plaza y valdés, 101-118. https://books.google.es/ programa nacional para la modernización educativa 19901994 (1990). diario oficial de la federación [official journal of the federation]. méxico. http://dof.gob.mx/ nota_detalle.php?codigo=4642789&fecha=29/01/1990 reyes garcía, j. (2006). evolución del posgrado de excelencia: 1991-2003 [evolution of postgraduate excellence: 1991-2003]. área temática 3: tipologías, modalidades y diversión. méxico: xx congreso nacional de posgrado. https://www. repositoriodigital.ipn.mx/handle/123456789/5623 reynaga obregón, s. (2002). los posgrados: una mirada valorativa [postgraduate programs: an evaluative view]. revista de la educación superior en línea, 123. http://publicaciones.anuies.mx/acervo/revsup/ res124/txt5.htm ruiz gutiérrez, r. et al (2006). los estudios de posgrado en méxico: diagnóstico y perspectivas [postgraduate studies in mexico: diagnosis and perspectives]. méxico: instituto internacional para la educación superior en américa latina yel caribe (iesalc). https://www. researchgate.net/publication/44838669 sección de graduados de la escuela superior de economía del ipn (1981). folleto maestrías [master’s degree brochure]. mexico: instituto politécnico nacional. printed, 15 tena núñez, r. (2008). 70 aniversario del posgrado en arquitectura y urbanismo del ipn [70th anniversary of the postgraduate program in architecture and urbanism of the ipn]. méxico. https://www.repositoriodigital. ipn .mx/bi t s t ream/123456789/25117/1/170aniversarioposgradourbanismo.pdf tinajero villavicencio, g. (2005). una década de acreditación de programas de posgrado: 1991-2001 [a decade of accreditation of graduate programs: 1991-2001]. revista de educación superior, 34(33), 111124. http://publicaciones.anuies.mx/pdfs/revista/ revista133_s6a1es.pdf united nations educational, scientific and cultural organization (2019). educación superior. documento de eje [higher education. background paper]. siteal. https://siteal.iiep.unesco.org/sites/ default/files/sit_informe_pdfs/siteal_educacion_ superior_20190525.pdf villalobos lópez, a. (2021). desempeño posgrados en economía ipn 1983-2000 [postgraduate performance in economics ipn 1983-2000]. mpra paper. university library. https://mpra.ub.unimuenchen.de/105622/ https://journals.e-palli.com/home/index.php/ajase pa ge 1 pa ge 11 2 american journal of applied statistics and economics (ajase) predictive modeling of ghana’s private sector pensions asset under management contribution using arima model chinton emmanuel1*, donkoh kojo isaac2, acquah oware nana emmanuel3 volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.5751 https://journals.e-palli.com/home/index.php/ajase article information abstract received: july 24, 2025 accepted: august 25, 2025 published: october 06, 2025 this study applies an arima (1,1,0) model to analyze the private sector pension assets under management (aum) in ghana. the model’s parameters and performance metrics were evaluated using sarimax results and the dickey-fuller test for stationarity. the sarimax model demonstrated a significant autoregressive term (ar. l1 = 0.9693) and acceptable performance metrics (mae = 5.99, rmse = 13.89, mape = 23.97%), indicating a strong influence of past values on current aum. the diagnostic tests suggested that residuals were not autocorrelated and approximately normally distributed. the dickey-fuller test further confirmed the stationarity of the time series, with a test statistic of -5.3314 and a p-value of 4.7116e-06, allowing us to reject the null hypothesis of a unit root. overall, the arima (1,1,0) model provides a reliable framework for forecasting and analyzing the private sector pension aum in ghana, supported by robust statistical validation. keywords arima, asset under management (aum), pensions contribution in ghana, private sector 1 department of statistics, university of cape coast, ghana 2 financial engineering, worldquant university, usa 3 department of economics and finance, youngstown state university, usa * corresponding author’s e-mail: emmanuelchinton7@gmail.com introduction retirement planning plays a critical role in ensuring financial security during old age. without adequate savings or income-generating assets, many individuals face severe financial challenges after leaving active employment (diaw, 2017). to address this, most countries have adopted social security and pension systems that provide stable income for retirees and reduce old-age poverty. in ghana, the enactment of the national pensions act, 2008 (act 766), marked a major reform of the pension system. the act replaced the social security and national insurance law (pndcl 247) and introduced a contributory three-tier pension scheme. these tiers comprise: (i) a mandatory basic national social security scheme managed by ssnit, (ii) a mandatory occupational pension scheme managed by private trustees, and (iii) a voluntary provident and personal pension scheme. the act also established the national pensions regulatory authority (npra) to regulate and supervise pension administration. a key innovation of act 766 was the extension of pension coverage to informal sector and self-employed workers, alongside those in formal employment (abebrese, 2011). the scheme requires a total monthly contribution of 18.5% of basic salary, with 13.5% allocated to tier 1 and 5% to tier 2. tier 3 remains voluntary. the reform aimed to ensure income stability for retirees, harmonize pension provisions across the public and private sectors, and mobilize long-term funds for national development. literature review theoretical review life-cycle consumption theory modigliani and brumberg’s (1954) life-cycle hypothesis provides the theoretical foundation for pension systems. it posits that individuals plan consumption and savings over their lifetime to smooth income across working and retirement years. without adequate savings, retirees may face income insecurity. in ghana, where extended family support systems are weakening, the theory underscores the need for deliberate retirement planning. positive theory of social security according to sala-i-martin (1996) and tabellini (2000), public pensions improve economic efficiency by enabling older workers to retire, thereby creating employment opportunities for younger and more productive workers. verbon (2012) further argues that pension systems act as retirement incentives where significant productivity gaps exist between older and younger generations. pooling theory allen and santomero (1998) highlight the efficiency of pension schemes in pooling risks, reducing transaction costs, and enhancing diversification. by mobilizing contributions, pension funds achieve economies of scale and improved investment outcomes (matheson et al., 2004; bridgen & meyer, 2008). pa ge 11 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 112-118, 2025 three-tier pension scheme act 766 structures pensions into three tiers: the basic mandatory scheme (tier 1), the mandatory occupational scheme (tier 2), and the voluntary provident/ personal pension scheme (tier 3). of the 18.5% total contribution, ssnit retains 11% for retirement benefits and transfers 2.5% to the national health insurance scheme. the remaining 5% is invested by tier 2 trustees. self-employed individuals may voluntarily participate in tier 3, though participation levels remain low. empirical review ghana’s pension system has evolved since the colonial era, beginning with the workmen’s compensation ordinance of 1940 and the non-contributory pension ordinance of 1950 for civil servants (darkwa, 2007; npra, 2010). over time, reforms have sought to address sustainability, adequacy, and coverage gaps. kpessa (2011) observes that pensions in africa play a crucial role in alleviating poverty among the elderly and supporting households under demographic pressure. similarly, agnew (2013) notes that pensions provide stable income for the aged, disabled, and unemployed. however, fiiwe (2020) highlights shortcomings in benefit packages, particularly the absence of post-retirement healthcare, housing, and entrepreneurial support, which limit retirees’ welfare. international evidence shows similar trends. in the united states, private pension schemes date back to 1857, with american express pioneering corporate pensions in 1878 (bond, 2017). pension benefits gained popularity during world war ii as firms used them to retain workers amidst wage freezes (pradmin, n.d.). informal sector participation a major challenge in ghana is extending pension coverage to the large informal sector. although act 766 permits voluntary participation through tier 3, awareness and enrollment remain limited. a survey of self-employed workers revealed that over 70% were unaware of the scheme, while many who had knowledge of it contributed irregularly due to unstable incomes. this highlights the need for greater education, flexible contribution options, and innovative pension products tailored to informal sector workers. conceptual framework of the ghana pension system figure 1: conceptual framework: pension theories and ghana’s three-tier scheme materials and methods data collection the study utilized secondary data for the predictive modeling of the contributory pension assets under management of private sector in ghana. for this study, we collected a time series of aum of private sector pensions industry from the national pensions and regulatory authority (npra) annual reports from 2012 to 2023. statistical analysis tool the study employed time series statistical technique to analyze trends over time and forecast future pension aum growth in ghana’s private sector and the analysis was done using python programming. the study employed autoregressive integrated moving average (arima) and sarimax to analyze the contributory trend over the period (2012-2023). sample size the sample size for the study comprised of 817 pensions trustees in ghana autoregressive integrated moving average (arima) the auto-regressive integrated moving average (arima) model is one of the most common prediction models, which is a time series analysis tool raised in the 1970s. it is a time series prediction model based on the fitting value of the past data sequence to extrapolate into future. it has 5 expressions: ar(p), ma(q), arma (p, q), arima (p, d, q), arima (p, d, q) × (p, d, q)s the autoregressive integrated moving average (arima) model is a combination of the differenced autoregressive model with the moving average model. arima model is said to be a unit-root non stationary because its ar polynomial has a unit-root and a conventional pa ge 11 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 112-118, 2025 table 1: asset under management of the private sector pensions in ghana (2012 -2022) years private sector pension aum (ghs’billion) 2023 46.5 2022 35.3 2021 28.0 2020 22.0 2019 17.3 2018 13.0 2017 9.8 2016 8.9 2015 8.8 2014 7.4 2013 4.8 2012 4.0 source: national pensions authority (npra) annual report (20122022). approach for handling unit-root non-stationary is to use differencing (tsay, 2010). if the differencing wt = yt – y(t-1) = (1 − b ) yt or higher-order differencing wt = (1-b)d yt of nonstationary time series then we call yt an arima (p, d, q) process with order p of ar process, d the number of differences made for a series to become stationary and q is the order of ma process. it is expressed as: y’t=i+∝1y’(t-1)+∝2y’(t-2)+....+∝py’(t-p)+et+θ1e(t-1)+θ2e(t2)+....+θq e(t-q) (1.0) φp(b)(1-b)dyt=θq(b)∝t~arima (p,d,q) (1.1) multiplicative seasonal arima (sarimax) the seasonal arima model incorporates both non seasonal and seasonal factors in a multiplicative model: sarima (p, d, q) (p, d, q) s. box & jenkins proposed the following model when dealing with a time series that contains seasonal fluctuations: φp(b s)φp(b)(1-b)d(1-bs)dyt=θq(b)ε(q)(b s)∝t (1.2) where yt is the observed value at time t, ∝t is the value at time t of white noise, d is order of differencing, is φp (b) ordinary autoregressive component of order p and θq(b) and is the ordinary moving average component of order q, sis number of seasons in a year and d is order of the seasonal differencing, φp (b s ) and ε(q)(b s ) are the seasonal autoregressive and moving average difference of orders p and q at lag s. according to box & jenkins (1976), the operator polynomials are: φp(b)=(1-∅1b-…∅pb p) (1.3) θq(b)=(1+∅1b-…θqb p) (1.4) φp(b s)=(1-φbs-…-φpb sp) (1.5) box-jenkins (arima) model when performing a time series analysis using arima models, three iterative steps must be used: diagnostic checking by examining residuals to assess the model’s adequacy, parameter estimation by estimating the model’s unknown parameters, and model identification by analyzing historical data. model identification identification of the appropriate and suitable arima model requires skills obtained by experience. box & jenkins postulates the following summary table on how to identify the model. table 2: model identification (box & jenkins, 1976) model acf pacf arima (p, d, 0) infinite. tails off finite cuts off after p lags arima (0, d, p) finite cuts off after infinite. tails off arima (p, d, q) infinite. tails off infinite. tails off model identification for arima modeling, the autoregressive order (p) is usually determined by examining the partial autocorrelation function (pacf) of a stationary time series. if the pacf cuts off after a certain lag, the highest significant lag suggests the value of p. conversely, if the pacf does not cut off, then p is often set to zero (box & jenkins, 1976). similarly, the moving average order (q) is inferred from the autocorrelation function (acf). a cutoff in the acf after a few lags indicates the potential value of q, with the last significant lag serving as an estimate. in arima (p, d, q) models, the autocorrelation patterns typically show exponential decay or damped sine-wave behavior after the first q–p lags. parameter estimation once a tentative model structure is identified, parameter estimation follows. box and jenkins (1976) propose several approaches, including the method of moments, least squares, and maximum likelihood estimation (mle). given the non-linear nature of many arima specifications, mle is often preferred for its efficiency and robustness. when estimating residuals, backcasting may also be applied to obtain initial values for the error terms. diagnostic checking after estimation, diagnostic checks are essential to confirm model adequacy. residuals should resemble white noise, meaning they are uncorrelated, normally distributed, and exhibit constant variance. a residual scatter plot should appear structureless, without systematic trends or patterns. similarly, the residual autocorrelation function should not display significant spikes. statistical tests, pa ge 11 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 112-118, 2025 such as the ljung-box test or chi-square-based adequacy tests, are typically used to confirm that no significant autocorrelation remains in the residuals. once these conditions are satisfied, the fitted arima model can be considered adequate and used for forecasting. results and discussions this section presents the outcome of the estimation of the model of this study. this begins with the forecast of private sector aum using the arima. for this study, the result presented in chart 1 proves that private sector figure 2: arima forecast of private sector pension aum (ghs’ billion) to 2040 figure 3: sarimax results pensions aum is projected to grow steeply by 2040. figure 3 presents the estimtaed results of the sarimax method for ghana’s private pensions aum. the ar (autoregressive) term has a coefficient of 0.9693, which is significant (p-value 0.000). this indicates a strong influence of past values on the current value of the dependent variable. the variance of the error term is relatively high with a coefficient of 3.2070 and a marginally significant p-value (0.060), suggesting some level of uncertainty in the model. the diagnostic tests suggest that the residuals are not auto-correlated (ljung-box test) and are normally distributed (jarque-bera test). the heteroskedasticity test indicates no significant heteroskedasticity. the performance metrics indicate that the model’s predictions have a mean absolute error of 5.99, root mean squared error of 13.89, and mean absolute percentage error of 23.97%. overall, the sarimax model seems to fit the data well with significant ar term and acceptable performance metrics. however, the high variance of the error term suggests that there is some uncertainty in the model’s predictions. figure 3 shows diagnostic plots for a statistical model. these plots are essential for diagnosing and validating the model’s assumptions and fit. standardized residuals for “p” this plot displays the standardized residuals (differences between observed and predicted values) for a variable labeled “p” across different observations. the residuals fluctuate around the zero line, indicating how well the model’s predictions match the actual data. histogram plus estimated density this plot combines a histogram of the residuals with pa ge 11 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 112-118, 2025 figure 4: diagnostic plots for a statistical model estimated density curves: -histogram bars: show the frequency of residuals, orange line: kernel density estimate (kde) a smoothed version of the histogram and green line: represents the standard normal distribution (n(0,1)). the alignment of the orange and green lines with the histogram bars suggests whether the residuals follow a normal distribution. normal q-q plot a quantile-quantile (q-q) plot compares the sample quantiles of the residuals to the theoretical quantiles of a standard normal distribution:red line: represents the expected line for normally distributed residuals and points: represent the actual residuals. the closer the points are to the red line, the more normally distributed the residuals are. correlogram this plot shows the autocorrelation of the residuals at different lags: points with error bars: indicate the correlation values at various lags and the shaded area:represents the confidence interval. values within the shaded area suggest no significant autocorrelation, indicating the residuals are independent over time. table 3 shows the results of a dickey-fuller test, which is used to test for the presence of a unit root in a time series sample. the test statistic of -5.3314 is more negative than all the critical values at the 1%, 5%, and 10% significance levels. combined with the very low p-value, this provides strong evidence to reject the null hypothesis. this suggests that the time series is stationary and does not have a unit root. chart 5 present the times series plots of the private sector table 3: dickey-fuller test result metric value test statistic -5.331440528065261 p-value 4.711563618849688e-06 # lags used 2.0 number of observations used 9.0 critical value (1%) -4.473135048010974 critical value (5%) -3.28988060356653 critical value (10%) -2.7723823456790124 over the last 12 years indicating that the aum of private pensions has always been on the upward trajectory. table 4 presents on the forcasted times series values of the private sector aum from 2012 to 2040 taking into account a 95% confidence intervals for both the lower and upper bound. pa ge 11 7 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 112-118, 2025 figure 5: time series plot table 4: forcasted times series values of the private sector aum from 2012 to 2040 year forecasted values (ghs'billion) lower bound (95% ci) (ghs'billion) upper bound (95% ci) (ghs'billion) 2013 5.423536852 3.275484696 7.571589008 2014 6.720661869 -0.319161914 13.76048565 2015 7.247450909 -6.312625896 20.80752771 2016 8.354215719 -13.60046814 30.30889958 2017 11.15636763 -22.46023611 44.77297137 2018 15.24192579 -34.48246896 64.96632053 2019 19.61959785 -50.78297042 90.02216611 2020 24.0972536 -71.21520887 119.4097161 2021 29.28331686 -95.25346974 153.8201035 2022 35.6109406 -123.0317088 194.25359 2023 42.86059712 -155.3054794 241.0266737 2024 50.61826353 -192.6798141 293.9163411 2025 58.83731515 -235.2233702 352.8980005 2026 67.78828048 -282.8182577 418.3948187 2027 77.63626747 -335.6172463 490.8897813 2028 88.27024515 -394.0358105 570.5763008 2029 99.51904349 -458.4250597 657.4631466 2030 111.3777465 -528.9098723 751.6653653 2031 123.9652102 -605.5382018 853.4686221 2032 137.3429456 -688.4720041 963.1578953 2033 151.4567871 -777.979251 1080.892825 2034 166.2367155 -874.2954576 1206.768889 2035 181.6869161 -977.5598355 1340.933668 2036 197.8590336 -1087.881322 1483.599389 2037 214.7753175 -1205.417149 1634.967784 2038 232.4100458 -1330.365886 1795.185977 2039 250.7348986 -1462.908715 1964.378512 2040 269.7542712 -1603.185231 2142.693774 pa ge 11 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 112-118, 2025 figure 6: stationarity check: original data and rolling statistics conclusion the arima (1,1,0) model provides a reasonably good fit for the private sector pension aum series. the statistically significant coefficient of the autoregressive term ar (1) indicates that current values are strongly influenced by their immediate past observations. model diagnostics further show that the residuals are free from serious autocorrelation, as confirmed by the ljung-box test, and are approximately normally distributed according to the jarque-bera test. nonetheless, signs of heteroskedasticity were detected, which suggests the need for additional adjustments to enhance the model’s robustness. in terms of accuracy, the model yields acceptable forecast error measures, including mae, rmse, and mape, making it suitable for short-term predictions. moreover, the augmented dickey-fuller test confirms the stationarity of the series, meaning its statistical properties such as mean and variance remain stable over time. this stationarity is particularly important, as it provides a strong foundation for reliable time series modeling and forecasting. finally, we suggest extensions to the model (arima with garch, sarima with garch) to specifically tackle the heteroskedasticity problem for future research work references abebrese, j. (2011). social protection in ghana: an overview of existing programs and their prospects and challenges. friedrich ebert foundation. (pdf) available at http://www.fesghana.org/uploads/ pdf/fes_socialprotectionghana_2011_final. pdf accessed on november 28th, 2012 agnew, j. (2013). australia’s retirement system: strengths, weaknesses, and reforms. issue in brief, (13). boston college: center for retirement research bond, t. (2017). 160 years of public pensions in the united states. retrieved from https://protectpensions. org/2017/04/29/public-pensions-early-history/ box, gep, jenkins, g. (1970). time series analysis: forecasting and control. 2nd edition. san francisco: holden-day. pp. 240. darkwa, o. (2007). reforming the ghanaian social security system; prospects and challenges. cross cultural gerontology (12). fiiwe, j. l. (2020). analysis of retirement benefits in nigeria: a case study of selected federal establishment. equatorial journal of marketing and insurance policy, 4(1), 1-15. kpessa, m. (2011). a comparative analysis of pension reforms and challenges in ghana and nigeria. international social security review, 64. 92-104 kpessa, m. w. (2010). the politics of retirement income security policy in ghana: historical trajectories and transformative capabilities. african journal of political science and international relations, 92-102 modigliani, f. &brumberg, r. h. (1954). utility analysis and the consumption function: an interpretation of cross-section data, (in kenneth k. kurihara, ed.). post-keynesian economics, new brunswick, nj. rutgers university press. 388–436. national pensions act of ghana. (2008). act 766. vroom, v. h. (1964). work and motivation. san francisco, ca: jossey-bass world bank (1994). averting the old age crises policies to protect: the old and promote growth. washington d.c: the world bank. pa ge 1 pa ge 14 1 american journal of applied statistics and economics (ajase) migration dynamics in west africa: the nigeria experience with internet access and human capital investment obomeghie adamu muhammed1*, obomeghie adamu inusa2 volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.5542 https://journals.e-palli.com/home/index.php/ajase article information abstract received: june 19, 2025 accepted: july 22, 2025 published: november 14, 2025 the advent of the internet has led to increased cross-border interactions and transnational activities. similarly, the increase in human capital investment has further given rise to digital migrants further highlighting the role of internet access in altering traditional migration dynamics. this study therefore examines the migration dynamics in west africa using the nigeria scenario. the dynamic ols is used to analyze the data collected from various statistical bodies and agencies such as; the nigeria cbn statistical bulletin as well as, the world bank’s world development indicator databases. the data collected is from the period ranging from 2008 to 2023. findings indicates that internet access and human capital investment both contributes negatively to migration dynamics in west africa, as it significantly fuels the widespread emigration of skilled west africa nationals. the insights gained in this work can inform evidence-based strategies to optimize the benefits and mitigate the risks associated with complex migration phenomenon. this increase in emigration has further exacerbated brain drain and human capital flight, meaning that west africa countries would face significant challenges to its economic development, social cohesion, and long-term prosperity if migration issues are poorly managed. it is recommended that policy makers in collaboration with relevant agencies should, invest in expanding broadband infrastructure and ensuring affordable internet services with the view to using the technology to discourage brain drain and encourage digital migrant. it is further recommended that government should promote vocational and technical training programs aligned with domestic market needs with the view to encourage brain circulation. keywords brain drain, digital technology, internet, migration dynamics 1 department of statistics, auchi polytechnic, auchi, edo state, nigeria 2 department of polymer engineering, auchi polytechnic, auchi, edo state, nigeria * corresponding author’s e-mail: maoisdg@yahoo.com introduction migration has served as a driving force behind cultural exchange, economic growth, demographic changes, and the spread of innovations. migration is the movement of people from one place to another, involving a change of residence across borders or within a country, often driven by economic, social, political, or environmental factors. it encompasses various forms such as voluntary or forced migration, internal or international migration, and seasonal or permanent movement (iom, 2022). according to castles and miller (2009), it fosters cultural diversity and understanding through the exchange of traditions, languages, and customs, enriching social fabric and fostering intercultural dialogue. the un report (2010) noted further that, migration influences population dynamics by balancing age structures and addressing labor shortages in aging societies, thus impacting social services and economic sustainability. one may conclude that, migration is a vital component of human society that shapes demographic patterns, fosters economic and cultural development, and addresses societal challenges. the internet has revolutionized human life by serving as a global communication network that connects individuals, organizations, and governments across the world. since its inception in the late 20th century, the internet has become an integral part of daily existence, transforming the way people communicate, learn, work, and access information. it has bridged geographical gaps, facilitated instant communication, and enabled the dissemination of knowledge on an unprecedented scale. studies such as, castell, (2010), noted that the internet has made it possible for individuals to communicate instantly regardless of their physical location, fostering greater social interaction and global connectivity. social media platforms such as, email and other messaging apps enable real-time communication, thus strengthening personal and professional relationships. the internet has significantly enhanced migration processes in west africa by facilitating various aspects of migration, from information access to communication and service provision. the internet provides west africa nationals with vital information about migration opportunities abroad, including visa requirements, job openings, and educational prospects. online platforms and social media groups often share firsthand experiences, tips, and updates, enabling prospective migrants to make informed decisions (maqsood et al., 2025). many west africa nationals seeking to migrate or work abroad utilize online job portals, social media, and recruitment websites. these platforms streamline the job search process and connect prospective migrants with employers or recruitment agencies, making migration more accessible and efficient (obi-ani et al., 2020). the historical relationship between human capital and migration dynamics is a complex and evolving topic that pa ge 14 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 141-149, 2025 has garnered significant scholarly attention. broadly, this relationship can be understood through the lens of how the movement of people influences, and is influenced by, the distribution of skills, education, and expertise across regions and countries (obomeghie, 2025). migration was often driven by economic necessity, conflict, or colonization, with less emphasis on human capital considerations. however, skilled migration, such as the movement of artisans or scholars, did occur and influenced local development (docquier & marfouk, 2004). with globalization and technological advancements, migration increasingly became a mechanism for the transfer of human capital. countries with advanced economies attracted highly educated migrants, leading to what is often termed “brain drain” from developing nations and “brain gain” for developed nations (docquier & rapoport, 2012). statement of problem migration dynamics in west africa are complex and influenced by multiple factors, including economic opportunities, education, infrastructure, and technological development. despite the growing penetration of the internet and increasing investments in human capital, there is limited understanding of how these variables specifically influence migration flows within the region. while some studies suggest that internet access facilitates access to information, social networks, and opportunities, thereby potentially influencing migration decisions (irele & bababunmi, 2024), others highlight the risk of brain drain and uneven development (maharaj, 2014). similarly, investments in human capital are believed to shape migration patterns by either encouraging skilled migration or promoting retention of talent within countries (ozulumba et al., 2024). however, empirical evidence on the combined impact of internet usage and human capital development on migration in west africa remains fragmented and underexplored. this research deficit poses a challenge for policymakers seeking to harness technological and educational advancements to promote balanced migration and regional development. without a comprehensive understanding of these relationships, strategies aimed at leveraging internet infrastructure and human capital investments to optimize migration flows and minimize negative consequences may be ineffective or counterproductive. objectives of the study to evaluate the influence of internet access on migration dynamics in west africa. to identify the role of human capital in the flow of migration in west africa. to provide policy recommendations for leveraging internet technologies to improve safe and informed migration in west africa overall, these objectives aim to generate comprehensive empirical evidence on how internet usage impacts migration patterns, experiences, and outcomes in west africa. significance of the study the significance of studying the impact of internet access and human capital development on migration dynamics in west africa lies in understanding how technological advancements and educational investments influence migration patterns within the region. this research can provide valuable insights for policymakers, educators, and development agencies aiming to foster sustainable development and regional integration. understanding the role of internet access and human capital in migration can help design targeted policies to manage migration flows, reduce brain drain, and promote regional development (ejemeyovwi et al., 2019). insights from the study can help mitigate negative effects such as brain drain and social dislocation, while maximizing benefits of human capital mobility (maharaj, 2014). finally, an understanding these dynamics supports regional cooperation and development strategies aimed at balancing migration flows and promoting inclusive growth (world bank, 2022). literature review conceptual framework internet access refers to the ability to connect to the internet, allowing individuals to access information, connect with others and take advantage of various opportunities in areas such as, education, healthcare business etc. (majumder, 2019). the proliferation of internet technology has particularly significant implications for migration processes, as it facilitates the flow of information, connects migrants with their families, and provides platforms for migration-related services. nigeria, as one of africa’s largest economies with a growing population of potential migrants, has experienced an increasing reliance on internet technology to support various aspects of migration, including information seeking, social networking, and online recruitment. according to obomeghie and ugbomhe, (2021), the advent of the internet has increased the spate of globalization. in the context of migration, internet usage has been identified as a vital facilitator that enhances migrants’ access to information about migration opportunities, legal requirements, and living conditions in destination countries. it also fosters social support networks that can ease the migration process and integration (godin et al., 2025). migration is typically defined as a move that crosses a specified political boundary, such as a county, or moves into a different labor market for the purpose of establishing a new place of residence. migration within a country is referred to as internal migration, and migration that crosses a national boundary is called immigration or emigration (toney & bailey, (2014). according to iom (2022), migration can be categorized in various ways such as: internal migration movement within a country’s borders (e.g., rural-to-urban migration, inter-state movement). pa ge 14 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 141-149, 2025 international migration movement across national borders, leading to a change of country of residence. rural-to-urban migration a common form of internal migration driven by the perceived economic opportunities and amenities in urban centers. chain migration a process where migrants are assisted in their move by family members or friends who have already settled in the destination area, often through social networks. migration is not just an individual decision but is heavily influenced by social and economic networks. these networks, often facilitated by modern communication technologies, provide information, financial support, and emotional sustenance to migrants, both before, during, and after their journey (akanle et al., 2020). human capital development refers to the process of improving the skills, knowledge, health, and overall capabilities of individuals, which enhances their productivity and potential contribution to economic growth and social well-being (obomeghie, 2025). in the context of migration dynamics, human capital development plays a crucial role in shaping migration patterns, decisions, and outcomes within and across regions. when individuals acquire advanced skills and education through investments in health, education, and training, they become more mobile, often seeking opportunities in regions where their skills are in demand (elsayed et al., 2025). this phenomenon can lead to brain drain, where highly skilled individuals migrate from their home countries to more developed regions, potentially resulting in a loss of human capital for the origin country (docquier & rapoport, 2012). in west africa, efforts to develop human capital are intertwined with migration dynamics, as improved education and skills influence whether individuals choose to migrate, return, or stay. human capital development can thus serve as both a driver and a consequence of migration, impacting regional economic integration, labor markets, and development trajectories (adepoju, 2010). theoretical review studying the impact of internet access on migration dynamics in west africa is strongly underpinned by several theoretical frameworks. these theories help to explain why and how the internet influences migration decisions and experiences, offering a deeper understanding beyond mere observation. social capital theory this theory posits that the internet facilitates the building and strengthening of social networks, which are critical in migration processes. online platforms enable migrants and potential migrants to access social capital that can provide information, emotional support, and assistance during migration and integration. for example, migrants use social media to connect with family and community members, reducing uncertainties and risks associated with migration (godin et al., 2025). information and communication technologies (ict) diffusion theory this framework explains how the adoption and usage of internet technologies spread within communities, influencing migration patterns. increased access to icts leads to greater information dissemination about migration opportunities, legal requirements, and living conditions, which can encourage migration or facilitate safe migration practices (rogers, 2003). network theory (and transnationalism) network theory posits that migration is sustained and perpetuated through social ties connecting migrants, nonmigrants, and institutions across sending and receiving areas. these migrant networks reduce the costs and risks of migration, influencing the magnitude and direction of flows (massey et al., 1993). transnationalism, as a related concept, describes the sustained cross-border ties and activities of migrants that link their home and host countries, often enabled by technology (vertovec, 1999). cheap and instant communication (whatsapp, video calls) reduces the economic and emotional costs of maintaining transnational ties, strengthening family and community bonds across borders (dekker & engbersen, 2014). human capital theory this foundational theory posits that individuals invest in their own human capital through education, training, and health, to enhance their productivity and earning potential. migration is often viewed as a response to disparities in human capital returns across regions; individuals move from areas with lower returns to those with higher opportunities, seeking better economic benefits (de haas, 2019). neoclassical economic theory of migration building on human capital theory, this perspective suggests that migration is driven by rational economic decisions aimed at maximizing income. human capital plays a crucial role as migrants move to regions where their skills and qualifications are valued more highly, thus increasing their lifetime earnings (de haas, 2007). new economics of labor migration (nelm) this theory emphasizes household decision-making rather than individual rationality. it considers migration as a strategy for risk diversification, investment in human capital, and overcoming market failures. migration can facilitate the transfer of human capital through remittances, skill development, and knowledge exchange upon return (sako, 2002). skill transfer and brain circulation pa ge 14 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 141-149, 2025 recent frameworks highlight the concept of “brain circulation,” where migration is not solely a loss but also a channel for skills transfer, remittances, and knowledge exchange, contributing positively to human capital development in origin countries (clemens et al., 2020). these theories, often used in conjunction, provide a robust framework for understanding the multifaceted relationship between internet access and migration patterns. they highlight that the internet is not a neutral tool but an active agent that mediates, transforms, and complicates migration processes. empirical review recent empirical studies have examined how internet access influences migration dynamics, including decisionmaking, migration flows, integration, and transnational ties. for instance, godin et al. (2025) found that migrants in nigeria extensively use social media and online forums to gather information before migrating, which influences their destination choices and preparation. research by koser (2007) indicates that increased internet penetration correlates with higher migration flows, especially in regions where information barriers previously limited mobility. they observed that in southeast asia, the internet has facilitated chain migration by enabling migrants to maintain contact with their networks abroad, encouraging others to follow. furthermore, empirical evidence by ruyssen & salomone (2017) demonstrated that migrants who actively use digital communication tools maintain stronger ties with their home countries, leading to increased remittances and sustained migration links. specifically, some empirical studies confirm a positive correlation between internet access/usage and migration aspirations and intentions in nigeria. grubanove et al. (2021), in a broad study that included african countries, found that having internet access is positively associated with both the desire to move abroad and preparations to migrate. ufuophu-biri (2020) specifically found that nigerian youths in edo and delta states, highly exposed to migration information on the internet, showed a high propensity to travel abroad due to internet-driven migratory motivation. odulami (2025) conducted a quantitative survey in ogun state, nigeria, revealing that while socio-economic conditions are the primary drivers of migration, social media significantly reinforces migration aspirations by “amplifying idealized ‘japa’ narratives and underrepresenting the complexities of return.” this supports the idea that social media creates a compelling, often glamorized, image of life abroad, influencing youth’s desire to emigrate. this aligns with the social amplification of risk framework, where perceived opportunities are amplified. finally, an empirical review of the impact of internet access on migration in nigeria reveals a complex and often contradictory picture, confirming that the internet acts as a dual-edged sword, both facilitating and complicating migration processes, although comprehensive, large-scale empirical studies specifically on nigeria are still emerging. empirical research indicates that human capital development significantly influences migration patterns at both regional and international levels. investments in education, health, and skills tend to increase individual mobility, as more educated and skilled individuals are more likely to migrate in search of better opportunities (docquier & rapoport, 2012). studies such as, beine et al. (2014) have shown that higher levels of education and skills correlate positively with migration propensity. they found that countries with higher human capital levels tend to export more skilled labor, leading to brain drain but also to the potential for remittances and knowledge transfer. equally, research by docquier & rapoport (2012) reveals that countries with a well-developed human capital base tend to attract migrants, especially skilled workers, contributing to regional migration flows and global talent distribution. research works by maharaj (2014), noted that, the disparity in human capital levels between regions often drives migration from less developed to more developed areas, exacerbating regional inequalities . this pattern is evident in west africa, where migration often stems from disparities in educational attainment and employment opportunities. research gaps while many studies examine internet penetration broadly, few explore how disparities in internet access across urban and rural areas influence migration decisions and human capital retention. understanding how digital inequalities affect migration patterns remains under-explored (irele & bababunmi, 2024). equally, most research provides cross-sectional analyses, lacking longitudinal studies that track how changes in internet access and human capital over time impact migration flows in west africa. long-term data is crucial to establish causality and observe trends (ozulumba et al., 2024). in summary, the key research gaps involve the need for more nuanced, longitudinal, and country-specific studies that consider digital inequalities, informal sectors, and policy impacts, as well as the interplay between internet use and human capital in shaping migration dynamics in west africa. addressing these gaps can lead to more targeted and effective development strategies. materials and methods research design the research design adopted in this study is the descriptive analysis, this is in order to provide reviewers with clear, concise insights that aid in planning and operational decisions. as well, descriptive analysis allows analyst to better understand the data landscape, which is essential for accurate and effective decisions.(umoru, et al., 2023). method of data collections time series data is used for the study, the data were collected from the cbn statistical bulletin (2024) and the pa ge 14 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 141-149, 2025 world bank’s world development indicators database (2024). the data were from 2003 to 2023. the model of the study the general form of a dols model is: yt = β0 +β1x1+∑i=-pqγi ∆x1 t-1+ β2x2+∑i=-pqγi ∆x2 t-1+ ∑j=1k δj∆zjt+ εt ) where: yt migration dynamics for nigeria within the period of study xt1 internet access in nigeria within the period of study. zjt control variables (gdp and inflation rate) within the period of study . ∆x1 t-1 first differences of the internet usage variable, with leads (i<0) and lags (i>0). this captures dynamic effects and accounts for potential endogeneity. ∆x2 t-1 first differences of the human capital investment, with leads (i<0) and lags (i>0). this captures dynamic effects and accounts for potential endogeneity. the choice of p and q (number of leads and lags) is determined within the study. ∆zjt first differences of the control variables, also with appropriate leads and lags β1 β2 the long-run coefficient of interest, representing the impact of internet access and human capital investment on migration flow. εt error term. the variables used in this study are defined below: migration dynamics (nmig) = data for net migration is used to represent the difference between the number of people immigrating to nigeria and the number of people emigrating from nigeria within the period under review. internet access (itu) = internet access ability to connect to the internet to accomplish different tasks or activities. human capital index (hci) = a measure that quantifies the human capital in nigeria, reflecting the health, education, and skills of its population. economic growth (gdp) = gdp expressed in us dollars to enable international comparisons of living standards and economic prosperity. inflation rate (inf) = the level of inflation in nigeria method of data analysis the dynamic ols estimation is used in the analysis because it extend the ols regression by including leads and lags of the first differences of the regressors to correct for endogeneity and serial correlation (stock & watson, 1993; banerjee et al., 1993). analytical framework the analytical framework for the study is hypothesizes in the table 1 below; justification of the chosen method table 1: hypothesized analytical framework variable expected sign rational itu negative (-) internet usage provides nigerians with access to information about job opportunities, education, and living conditions abroad, potentially encouraging emigration (adepoju, 2010). hci negative (-) increased hci can lead to higher international emigration of skilled individuals (brain drain) if opportunities abroad are better (adegoke, 2023). gdp positive (+) higher gdp through improved living standards may potentially reduces the motivation for migration (yemisi & tosho, 2020). inf positive/negative (+/-) increase inflation rate may increase immigration flow because increase in the price of goods and services encourage foreign investment. it may also lead workers to find better paying jobs abroad (ejemeyovwi, 2019). source: authors compilation. migration flows, internet usage, human capital investment and some macroeconomic variables often exhibit nonstationary properties (trends over time). if they are cointegrated, it means they have a long-run equilibrium relationship. dols is designed to estimate this long-run relationship in the presence of cointegration. equally, dols addresses potential endogeneity by including leads and lags of the first-differenced regressors. this accounts for the dynamic interactions and feedback effects between the variables, ensuring that the estimated coefficients represent the true long-run impact. in addition, dols often performs better than other cointegration techniques (like engle-granger or johansen) in small samples (stock & watson, 1993). results and discussions the descriptive statistics of our analysis is presented below in table 2. from table 2 which represents the descriptive statistics, it can be seen that gdp has the highest mean with a value of 376.9806 while nmig has the lowest mean with a value of -5985.0021. nmig has the highest standard deviation with a value of 52021.93 while hci with a value of 0.028825 has the lowest standard deviation. from table 3 which depicts the stationarity situation using the phillip-peron test, it can be seen that all our variables are stationary at first difference. table 4 shows the cointergration result which shows that there exist a long-run relationship between our dependent variable and the selected independent variables the fstatistics with a prbability value of 0.0000, pa ge 14 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 141-149, 2025 table 2: descriptive statistics nmig itu gdp hci inf mean -5985.002 18.54755 376.9806 0.502988 12.87919 median -11604.67 19.10000 406.5567 0.506667 12.46000 maximum 78685.00 39.20000 574.1800 0.560000 24.66000 minimum -116162.0 0.560000 104.7400 0.440000 5.390000 std. dev. 52021.93 11.50099 117.4289 0.028825 3.706971 skewness -0.037921 0.125708 -0.708301 -0.235144 0.506302 kurtosis 1.942201 1.808384 2.686781 1.823910 3.406984 jarque-bera 11.29376 14.89339 21.13640 16.11042 11.95965 probability 0.003529 0.000583 0.000026 0.000317 0.002529 sum -1442386. 4469.960 90852.31 121.2200 3103.885 sum sq. dev. 6.50e+11 31745.47 3309493. 0.199410 3297.992 observations 241 241 241 241 241 source; author’s computation from e-views output table 3: staionarity test. variable order pp value prob conclution nmig i (i) -4.121448 (0.0000) stationary itu i (i) -2.970367 (0.0392) stationary gdp i (i) -2.607344 (0.0091) stationary hci i (i) -3.030018 (0.0025) stationary inf i (i) -4.048571 (0.0001) stationary source; author’s computation from e-views output table 4: cointegration test series: nmig itu gdp hci inf hypothesized trace 0.05 no. of ce(s) eigenvalue statistic critical value prob.** none * 0.148627 94.95461 69.81889 0.0002 at most 1 * 0.116584 56.98106 47.85613 0.0055 at most 2 0.078922 27.72671 29.79707 0.0851 at most 3 0.034131 8.325125 15.49471 0.4313 at most 4 0.000549 0.129591 3.841466 0.7189 hypothesized max-eigen 0.05 no. of ce(s) eigenvalue statistic critical value prob.** none * 0.148627 37.97354 33.87687 0.0153 at most 1 * 0.116584 29.25435 27.58434 0.0303 at most 2 0.078922 19.40159 21.13162 0.0858 at most 3 0.034131 8.195534 14.26460 0.3592 at most 4 0.000549 0.129591 3.841466 0.7189 source; author’s computation from e-views output table 5: staionarity test. wald test: f-statistic 9.733709 (3, 222) 0.0000 chi-square 29.20113 3 0.0000 source; author’s computation from e-views output indicates that the over-all fit of the model is adequate. from table 6, it can be noticed that there is a significant but negative relationship between internet access and migration dynamics as the p-value of 0.0001 is less than 0.05 which indicates that for every one-unit increase in itu, migration is estimated to decrease by -4799.270 pa ge 14 7 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 141-149, 2025 table 6: the dynamic ols estimates dependent variable: nmig variable coefficient std. error t-statistic prob. itu -4799.270 512.3348 -9.367450 0.0000 gdp 341.1918 53.03061 6.433866 0.0000 hci -290251.7 48553.14 -5.978021 0.0000 inf 6906.044 1183.578 5.834889 0.0000 d(itu) -4670.782 231325.0 -0.020191 0.9839 d(itu(1)) 84103.74 167561.9 0.501926 0.6162 d(itu(-1)) -19552.96 169485.2 -0.115367 0.9083 d(gdp) -218.8061 2958.983 -0.073946 0.9411 d(gdp(1)) 5575.992 2167.413 2.572648 0.0107 d(gdp(-1)) -3729.752 2149.571 -1.735115 0.0841 d(hci) -32439.78 3334979. -0.009727 0.9922 d(hci(1)) 3186840. 2453895. 1.298686 0.1954 d(hci(-1)) -1227553. 2465193. -0.497954 0.6190 d(inf) -749.3552 30704.40 -0.024405 0.9806 d(inf(1)) -9079.442 22817.58 -0.397914 0.6911 d(inf(-1)) 1087.682 23185.19 0.046913 0.9626 r-squared 0.430137 adjusted r-squared 0.391632 source; author’s computation from e-views output units, holding other variables constant. this is in line with our a priori expectation similar to the study by brynjolfsson et al. (2020). in the case of human capital investment, it can be seen that there is a negative relationship between hci and migration dynamics, the p-value of 0.0001 is less than 0.05 which indicates that for every one-unit increase in hci migration is estimated to decrease by -29025.7 units, holding other variables constant. this is in line with our a priori expectation (adegoke, 2023). for gdp, it can be observed that there is a positive relationship between gdp and migration dynamics the p-value of 0.0001 is less than 0.05 which indicates that for every one-unit increase in gdp, migration is estimated to increase by 341.1918 units, holding other variables constant. this is in line with our a priori expectation and supported by similar study by adepoju (2016). finally, it can be observed that there is a positive relationship between inflation rate and migration dynamics, the p-value of 0.0001 is less than 0.05 which indicates that for every one-unit increase in inf, migration is estimated to increase by 6906.044 units, holding other variables constant. this is in line with our a priori expectation and supported by similar study by ekhorugue et al. (2024). conclusion a negative relationship between internet access and migration dynamics suggests that as internet access increases, emigration increases. this means that enhanced internet access provides residents with better access to foreign education, job information, government services, and social networks. this can increase desire to migrate elsewhere, leading to decreased net migration (goolsbee & syverson, 2008). this trend suggests that better connectivity increases outward migration by supporting telecommuting. on the other hand, a negative relationship between human capital investment and migration dynamics suggests that enhancing human capital through education, skills development, and health can encourage emigration. better education and skills can boost employment prospects internationally, which can lead to “brain drain.” according to umeokwobi et al. (2025), west africa has historically experienced significant out-migration of skilled health workers, engineers, and academics seeking better opportunities abroad. a positive relationship between gdp and migration dynamics suggests that as gdp increases, more foreigners move into the country. higher gdp levels, often driven by economic growth, can make the country more attractive to migrants seeking better opportunities. finally, a positive relationship between inflation rate and migration dynamics suggests that higher inflation rates are associated with increased immigration because rising inflation encourage investors to migrate and invest in the country. reccommendations arising from the outcome of this study, it is recommended that policy makers in collaboration with relevant agencies pa ge 14 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 141-149, 2025 should invest in expanding broadband infrastructure and ensuring affordable internet services with the view to using the technology to discourage brain drain and encourage digital migrant. policies aimed at expanding digital connectivity and leveraging the internet access for economic and social development should be encouraged. with respect to human capital investment, it is recommended that policy makers should promote vocational and technical training programs aligned with market needs with the view to encourage brain circulation. encourage public-private partnerships to improve skill acquisition, implement policies that ensure competitive salaries and benefits for skilled workers. as well as, offer incentives for expatriates to return or contribute remotely and promote diaspora engagement programs. in the case of gdp, it is recommended that policy makers in the sub-region should make favorable policies and incentives to attract foreign investors and skilled expatriates. promote the region as a destination for business, innovation, and entrepreneurship through international marketing campaigns. simplify business registration and improve ease of business to sustain economic growth and migration inflows. finally, policy makers in the sub-region should plan policies that will help to channel investment influx into productive sectors, facilitate entry and operation of businesses that can benefit from inflationary environments, such as real estate, as well as develop targeted policies to protect vulnerable populations from inflationary impacts and prevent undesirable outflows. references adegoke, d. 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(2021). nigerian youth, migration narratives and social media: a perspective. electronic journal of social & strategic studies, 2(2), 162177. https://doi.org/10.47362/ejsss.2021.2208 pa ge 1 pa ge 70 american journal of applied statistics and economics (ajase) sparse dynamic factor modeling of some selected climatic variables daramola azeez mustapha1*, samuel olorunfemi adams2, mary unekwu adehi2 volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.5500 https://journals.e-palli.com/home/index.php/ajase article information abstract received: june 06, 2025 accepted: july 07, 2025 published: july 30, 2025 the classical forecasting models struggle to handle missing data, a common issue in climate data, due to irregular reporting intervals or sensor failures. incomplete datasets can lead to biased or unreliable forecasts, further complicating efforts to predict climatic variables accurately. this study aims to examine the performance of the sparse dynamic factor models on climate data. its performance is compared with classical models, such as arima, pca, two-stage dfm, em-based dfm, sparse dfm, lasso, and group lasso. the study integrates a traditional statistical approach with penalized likelihood optimization, ensuring the inclusion of sparse, interpretable models. the dataset employed in this study was extracted from the nigerian meteorological agency (nimet) and the national bureau of statistics (nbs) statistical bulletin 2023. the data includes annual average mean surface air temperature, annual precipitation, number of days with heat index > 35°c, and maximum number of consecutive wet days. the findings of the study revealed that group lasso consistently yielded the lowest mse across key variables, air temperature (mse = 0.3854), precipitation (921.27), heat days (296.85), and wet days (748.90) outperforming all benchmark models. results also showed that, arima, pca, and two-stage dfm recorded substantially higher errors, highlighting their inability to capture intricate, nonlinear dependencies present in climate processes. keywords annual average surface air temperature, annual precipitation, maximum number of consecutive wet days, number of days with heat index > 35°c 1 department of statistics, university of abuja, & department of international statistical development, national bureau of statistics, abuja, nigeria 2 department of international statistical development, national bureau of statistics, abuja, nigeria * corresponding author’s e-mail: samuel.adams@uniabuja.edu.ng introduction climate change and its associated extreme weather events have heightened the need for accurate forecasting of climatic variables, including temperature, precipitation, wind speed, and solar radiation. these forecasts are crucial across sectors like agriculture, water management, disaster prevention, and energy production (slater, 2023; park, 2023). forecasting weather patterns and climatic trends, however, is a complex task because climate systems are inherently nonlinear and affected by various interdependent factors (boyd, 2011; huang, 2021). historically, climate forecasting has relied heavily on time series models such as the autoregressive integrated moving average (arima), which focuses on linear relationships within climatic data. while arima and its variations have been effective for many forecasting tasks, they are often insufficient for the highly nonlinear and complex nature of climate data, especially for long-term predictions (diebold-mariano, 2002). more sophisticated techniques, such as generalized autoregressive conditional heteroskedasticity (garch) models, have been employed to capture time-varying volatility in climatic variables, yet they too have limitations (modarres ouarda, 2014). in recent years, the integration of machine learning (ml) and artificial intelligence (ai) into climate modeling has become increasingly popular. hybrid models, which combine traditional statistical approaches with ai techniques, have shown significant promise in improving the accuracy of climatic forecasts (slater, 2023; han, 2012). for instance, deep learning models such as long short-term memory (lstm) and graph-based models have proven to capture both shortterm fluctuations and long-term patterns more effectively than traditional methods (kipf-welling, 2017; vaswani, 2017). however, there remains a significant challenge in balancing the computational efficiency, interpretability, and scalability of these advanced models (liu, 2018). the increasing availability of high-dimensional climate data from satellites, weather stations, and other sensors has enabled researchers to explore more complex and dynamic models that better capture the interactions between different climatic variables (de livera, 2011). these models are essential for improving decisionmaking in industries reliant on climate forecasts, especially in regions most vulnerable to extreme weather events (gultepe, 2019). sparse factor models condense information in large crosssection or panel datasets. so far, they have particularly been used in gene expression analysis, where only few out of potentially tens of thousands of genes may be responsible for some physiological outcome of interest. individual gene expressions may thus be influenced by common biological factors, each of which involves only a subgroup of genes. a sparse loading matrix arises naturally in this context, in which many zero rows indicate that only a small share of all genes determines the biological factors of interest, and zeros in columns indicate that genes usually determine one or only a few of the biological factors (west, 2003, lucas, 2006). this framework is also of interest for economic analysis. in recent times the practice of including as much data as available or using the highest possible disaggregation level pa ge 71 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 70-79, 2025 in sectoral analysis has become standard in econometric factor analysis to construct composite business cycle indicators (forni, 2000; forni, 2001) or to develop forecasting methods (stock & watson, 2002b). specifying a sparse factor model for large economic datasets brings about valuable advantages. first, the inference on a sparse factor loading matrix can provide an explicit interpretation of the factors. given that series might be affected by only fewer than all estimated factors, those with non-zero loadings are relevant for the interpretation of a factor. second, the issue of selecting the variables containing most information on the common factors is simultaneously addressed while estimating the model. the factor loadings of irrelevant variables are shrunk to zero, which yields rows of zeros in the factor loading matrix. third, in forecasting, the estimation results provide evidence on whether the panel contains relevant information for a variable of interest, and specifically which variables should be retained to compute the forecast. sparse dynamic factor models (sdfm) combine dynamic factor analysis with sparsity constraints to identify underlying factors and select relevant features in high-dimensional time-series data. it is a statistical model that extracts underlying dynamic factors, imposes sparsity constraints on factor loadings and captures temporal relationships. the techniques under it include, the static, dynamic and generalized sparse dynamic factor models. the model consists of dynamic factor analysis, sparsity constraints and temporal relationships. sdfm is useful for identification of relevant features, reduction of overfitting, improves interpretability and captures temporal dynamics. it can be applied to macroeconomic forecasting, financial risk analysis, neuroscience and climate modelling. the estimation methods under sdfm are maximum likelihood estimation (mle), principal component analysis, independent component analysis (ica) and bayesian methods. accurately forecasting climatic variables is critical, but current forecasting models face several limitations, particularly when dealing with the complex, nonlinear, and interdependent nature of climatic data (huang, 1998). traditional time series models, such as arima and garch, are based on linear assumptions, making them less suitable for capturing the nonlinearity, seasonal patterns, and abrupt shifts that are characteristic of climatic variables (modarres & ouarda, 2013). these limitations are particularly evident when forecasting extreme weather events or long-term climate patterns, where more sophisticated models are needed (harvey & peters, 1990). in addition, many current models struggle to handle missing data, a common issue in climate records due to irregular reporting intervals or sensor failures (magnano, 2008). incomplete datasets can lead to biased or unreliable forecasts, further complicating efforts to predict climatic variables accurately. moreover, the computational cost associated with forecasting models has become a significant issue, especially when applied to large-scale datasets typical of climate science (slater, 2023). literature review climate data refers to information amassed over extended periods, detailing average weather conditions, patterns, and variations in specific regions. this data encompasses temperature, precipitation, wind speed, humidity, and other atmospheric variables. it plays a critical role in understanding climate change, assessing impacts, and crafting strategies for adaptation and mitigation. examples of climate data include temperature, precipitation, wind speed, humidity, and atmospheric pressure (mustapha, 2025). globally, temperature is considered an important variable within the climate system and it is chosen as one of the standard variables for analysis (kajtar, 2002; ragatoa, 2018). temperature variability may lead to a rise in the frequency, magnitude and seasonality of extreme events which are likely to happen in the future (van der wiel & bintanja, 2021). temperature indices are essential indicators used for monitoring and detecting variability (qaisrani, 2021). humidity describes the amount of water vapor in the air, and the more water vapor that is present, the more humid it is. most weather reports don’t tell you the humidity, though, because the relative humidity is more relevant. this is the amount of water vapor in the air relative to what the air can hold. modelling humidity involves simulating and predicting moisture content in different environments. the concept scientifically refers to the actual moisture content of a sample of air expressed as a percentage of that contained in the same volume of saturated air at the same temperature (oyediran, 1977; okhakhu, 2010). inferentially, therefore, relative humidity is the positive result of the combined processes of surface evaporation and vegetal transpiration which occur on the environment to produce abundant clouds of ascent moisture. the water vapour content of the atmosphere is significant in modern climatic studies for a number of reasons, namely, it serves as the main source of all forms of condensation and precipitation across the universe (adams & bamanga, 2020). it absorbs both the solar and terrestrial radiation and plays the role of heat regulator within the earth-atmosphere realm, it influences the rates of evaporation and evapotranspiration on the earth’s surface, it could be changed into liquid or solid form; it releases latent heat which is the direct source of energy required to propel the circulation of the earth’s atmosphere and development of atmospheric turbulence, it influences the temperature which is sensed by the human skin thereby determining the physical and physiological comfort of the human body; finally, the vapour content determines the stability of air in a selected settlement. atmospheric pressure, also known as air pressure or barometric pressure (after the barometer), is the pressure within the atmosphere of earth. the pa ge 72 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 70-79, 2025 standard atmosphere (symbol: atm) is a unit of pressure defined as 101,325 pa (1,013.25 hpa), which is equivalent to 1,013.25 millibars, 760 mm hg, 29.9212 inches hg, or 14.696 psi. (icao, 1993). previous studies suggested that land surface temperature (lst) has a high correlation with sat, estimating it from lst and spectral vegetation index measurements (svi) (khesali & mobasheri, 2023; nieto, 2011; prihodko & goward, 1997). other studies found that, although sat is mainly related to lst, it is also related to geographical and meteorological parameters (cristóbal, 2008; ninyerola, 2007). thus, adding more parameters results in an improvement on the sat retrieval (cristóbal, 2008; niclos, 2014). rainfall is the major climate resources that can be used as an index of climate change. rainfall is the most essential aspect in a farming system as it determines the accessibility of soil needed for maximum yield (niles, 2016). ismail & oke (2012) and adams (2019) believes that crops, animals and humans derived their water resources mainly from it and irrigation scheduling depends on the correct estimation of the spatial distribution of rainfall and it also determines the time in which some crops types can be cultivated and the appropriate farming system for optimum yields. according to the 5th assessment report (ar5) of the intergovernmental panel on climate change (ipcc), global (land and ocean) average temperature has shown a 0.85 °c (0.65–1.06°c) increase over the period of 1800– 2012 (ipcc, 2013), and a 0.74 ± 0.18 °c increase during the last hundred years (1906–2005) (ipcc, 2007). this trend in global warming is predicted to likely increase during the 21st century under all the representative concentration pathways (rcps). the projected values of increase are 0.3–1.7 °c (rcp2.6), 1.1–2.6 °c (rcp4.5), 1.4–3.1 °c (rcp6.0), 2.6–4.8 °c (rcp8.5) for 2081–2100, relative to 1986–2005 (ipcc, 2013). such changes in global mean temperature can radically disturb human society and the natural environment (ashiq, 2010). however, the changes in extreme temperature events such as heat waves, severe winter and summer storms, hot and cold days, and hot and cold nights can cause more severe impacts on human society and the natural environment (refsgaard, 2013). consequently, (jokubaitis, 2021) examine the use of sparse methods to forecast the real (in the chain-linked volume sense) expenditure components of the us and eu gdp in the short-run sooner than national statistics institutions officially release the data. the study solved the high-dimensionality problem of monthly datasets by assuming sparse structures of leading indicators capable of adequately explaining the dynamics of the analyzed data. the study further proposed an adjustment that combines lasso cases with principal components analysis to improve the forecasting performance. the forecasting performance was evaluated by conducting pseudo-real-time experiments for gross fixed capital formation, private consumption, imports, and exports over a sample from 2005–2019, compared with benchmark arma and factor models. the main results suggest that sparse methods can outperform the benchmarks and identify reasonable subsets of explanatory variables. the proposed combination of lasso and principal components further improves the forecast accuracy. materials and methods data the climate dataset originates from meteorological observations collected across various regions in nigeria, spanning from 1950 to 2020. the data includes annual average mean surface air temperature, annual precipitation, number of days with heat index > 35°c, and maximum number of consecutive wet days. these records were sourced from reputable institutions such as the nigerian meteorological agency (nimet) and sourced from national bureau of statistics (nbs) statistical bulletin 2023, which aggregate historical weather data for nigeria. the dataset provides valuable insights into longterm climate trends, essential for environmental research, policy-making, and adaptation strategies in nigeria. sdfm with lasso regularization and other classical forecast models this study utilized the sparse dynamic factor model (sdfm) with lasso regularization and other traditional forecasting models such as arima, pca-dfm, twostage dfm, and em-based dfm. given the complexity and nonlinearity of climate systems, the study explores how sdfm, incorporating l1 (lasso) and l2 (group lasso) regularization, enhances forecasting performance by capturing complex relationships within highdimensional climate data while maintaining computational efficiency. sparse dynamic factor model (sdfm) the sparse dynamic factor model is constructed to capture both the cross-sectional and temporal relationships in high-dimensional time series data, following the framework established by (jungbacker & koopman, 2015) for likelihood-based dynamic factor analysis. dynamic factor models are expressed as follows: yt=λft+εt, εt∼n(0, σε ) (1) where: • yt=(y1t, y2t,…,ynt)’ is an n-dimensional observed time series vector at time t, • ft=(f1t,f2t,…,frt )’ represents the r-dimensional latent factors, • λ is the unknown factor loading matrix of dimension n×r, • εt represents the idiosyncratic error with covariance matrix σε, similar to the structures outlined by diebold & mariano (2002) and modarres & ouarda (2013). the factors ft evolve according to an autoregressive process, following the approach of dempster (1977) and harvey & peters (1990): ft=φf(t-1)+ηt, ηt∼n(0,ση ) (2) where: pa ge 73 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 70-79, 2025 • φ is a diagonal autoregressive matrix for the factor dynamics, • ηt represents innovations with covariance matrix ση. l1 penalty (lasso regularization) the l1 penalty, also known as lasso (least absolute shrinkage and selection operator), is applied to encourage sparsity in the factor loading matrix λ. this penalty is particularly useful in high-dimensional datasets where many parameters may be irrelevant, as discussed by (zou, 2006; fan & tang (2013). the penalized likelihood function, incorporating l1 regularization, is formulated as follows: (3) (euclidean norm) of the j-th column of the loading matrix λ, similar to the approach described by (fan & tang, 2013) for controlling shrinkage. • γ is the tuning parameter that controls the amount of shrinkage applied ridge regularization is often applied in cases where the number of predictors exceeds the number of observations or where the predictors are highly correlated, preventing overfitting by reducing the magnitude of the coefficients. unlike lasso, ridge regression reduces model complexity but does not lead to a sparse solution. this balance between fitting the data and controlling overfitting through shrinkage has been widely discussed in the literature, including (fan & tang, 2013; boyd, 2011). the optimization problem for the l2 penalty is: (6)where: • σt is the covariance matrix of the error term at time t, • vt is the residual (observation minus prediction) at time t, • λ is the tuning parameter that controls the degree of shrinkage, as detailed in (zou, 2006; fan & tang (2013), • |λij | is the absolute value of each entry in the loading matrix λ. the l1 penalty encourages some elements of λ to be exactly zero, thereby performing variable selection. this sparse representation is particularly useful in highdimensional data where the number of variables exceeds the number of observations, a problem well addressed by (zou, 2006). the larger the value of λ, the greater the shrinkage, leading to a sparser solution. the optimization problem then becomes: (4) this l1 regularization problem is non-differentiable, but efficient algorithms such as coordinate descent and proximal gradient methods can solve it, following (boyd, 2011; dempster, 1977). l2 penalty (ridge regularization) the l2 penalty, commonly referred to as ridge regression or tikhonov regularization, penalizes the squared values of the parameters in the factor loading matrix λ, as described in the work of (zou, 2006) and (knight fu, 2000). unlike lasso, which tends to drive some coefficients to exactly zero, ridge regularization shrinks the coefficients towards zero without setting them exactly to zero. this approach is particularly useful in cases of multicollinearity, where correlated predictors cause instability in ordinary least squares (ols) estimates. the penalized likelihood function with the l2 penalty can be written as: (5) where: • ‖λ.‖2 2=∑n (i=1)λ 2 ij represents the squared l2-norm sparse dfm with l1 penalty the first model assumes a sparse structure in the factor loading matrix λ. the model for the observations is: yt= λft+εt, εt∼n(0,σε ) (7) where: • yt=(y1t,y2t,…,ynt )’ is the observed data at time t, • λ is the sparse factor loading matrix. to induce sparsity, we apply an l1 penalty to the loglikelihood function: (8) here, λ is the tuning parameter that controls the amount of sparsity imposed on the factor loading matrix, and σ_t is the covariance matrix of the idiosyncratic errors. the l1 penalty shrinks the factor loadings, inducing sparsity by forcing some of the elements of λ to zero. the factor dynamics are modeled as: ft=φf(t-1)+ηt, ηt∼n(0,ση) (9) where φ is the diagonal matrix of autoregressive coefficients, and ηt represents innovations with covariance matrix ση. sparse dynamic factor model with l2 penalty the second proposed model introduces group sparsity through an l2 penalty (group lasso), which is used to shrink entire columns of the factor loading matrix λ toward zero, promoting group-level sparsity. this model is particularly useful when the goal is to select relevant latent factors while discarding others completely. the model for the observations is: (yt=λft+εt, εt∼n(0,σε ) (10) where: • yt is the observed data vector at time t, • λ is the factor loading matrix, with group sparsity imposed on its columns. model selection criteria akaike’s information criterion aic=-2 log(l)+2k (11) pa ge 74 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 70-79, 2025 hannan –quinn information criterion hqc=-2lmax+2kln(ln (n) (12) bayesian information criterion bic=aic+k(log (t)-2 (13) mean squared error (mse) = 1/n ∑n (i=1) (ŷi-(ŷi )) 2 where; l is the likelihood, k is the number of model parameters, y is the vector of observed values, ŷi is the variable being predicted, n is the number of observations, lmax is the log-likelihood. results and discussion summary statistics the summary statistics of the climate dataset shows the long-term trends and variability of key climate indicators in nigeria. the annual average mean surface air temperature remains relatively stable, with a mean of 26.68°c and a standard deviation of 0.49°c. the difference between the minimum (25.49°c) and maximum (27.73°c) values suggests that the region experiences only minor fluctuations in temperature across years. precipitation, on the other hand, exhibits greater variability, with an annual mean of 1089.90 mm and a standard deviation of 109.23 mm. the minimum recorded precipitation of 770.75 mm and a maximum of 1319.71 mm highlight significant inter-annual differences in rainfall levels. the number of extreme heat days, defined as days with a heat index exceeding 35°c, shows considerable variation, with an average of 6.87 days per year and a wide range between 0.16 days and 27.04 days. the large standard deviation of 6.25 days suggests an increasing frequency of extreme heat events in certain years. another important climatic factor is the number of consecutive wet days, which measures the persistence of rainy periods. the dataset reveals an average of 35.16 consecutive wet days per year, with a standard deviation of 6.52 days. the minimum value of 24.1 days and a maximum of 58.08 days highlight significant fluctuations in wet spell durations. table 1: summary of climatic data metric air-temp precipitation heat-days wet-days mean 26.68324 1089.903 6.869718 35.16296 standard deviation 0.4929294 109.2348 6.248001 6.516089 minimum 25.49 770.75 0.16 24.1 maximum 27.73 1319.71 27.04 58.08 source: extracted by the researcher from r output figure 1: air temperature, precipitation, heat index days and max consecutive wet days trend pa ge 75 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 70-79, 2025 the annual average air temperature trend in the image shows a clear upward trajectory, indicating a long-term rise in temperature over the years. despite noticeable short-term fluctuations, likely due to climatic oscillations or extreme weather events, the overall trend suggests a warming pattern consistent with global climate change. the increasing frequency and intensity of hightemperature spikes in recent decades reinforce concerns about rising greenhouse gas emissions, urbanization effects, and natural climate variability. the annual precipitation trend shown in the image indicates significant variability in precipitation levels over time, with fluctuations across different years. while no strong increasing or decreasing trend is apparent, the data suggests intermittent periods of high and low rainfall, potentially influenced by climatic cycles such as el niñosouthern oscillation (enso) or regional weather patterns. the annual number of days with heat index > 35°c trend reveals a significant and accelerating increase over the years, particularly from the 1980s onward. the number of extreme heat days remained relatively low in the early decades but has risen sharply in recent years, reaching peaks exceeding 20 days per year. this upward trend suggests intensifying heat stress, likely driven by global warming and climate change. the annual maximum number of consecutive wet days trend shows significant variability over time, with notable peaks and declines. the early period (before the 1980s) exhibits high fluctuations, with some years experiencing prolonged wet spells exceeding 50 consecutive days. however, in recent decades, the number of consecutive wet days appears to have stabilized around 30 to 40 days, with fewer extreme peaks. models application results the number of factors tuned plot (ic 2) presents the selection process for the optimal number of factors in the model. the top chart shows the index values for different factor numbers, where a lower index value suggests a better factor selection. the red dot at factor 3 indicates that this was the chosen number of factors, as it had the lowest index value. the bottom chart illustrates the percentage of variance explained by each factor, with a clear decreasing trend as more factors ares added. the first factor explains the highest variance (above 50%), while the third factor contributes significantly less. this result supports the selection of three factors, balancing model simplicity and variance explained, ensuring that the model captures essential variability while avoiding overfitting. figure 2: a & b, number of factors tuned plot table 2: mean square error (mse) for the climate data models climate_ variable mse_ arima mse_ pca mse_2stage mse_em mse_ sdfm mse_ lasso mse_ group_lasso air_temp 0.4571 0.5013 0.4807 0.4632 0.4416 0.4029 0.3854 precipitation 1105.2376 1304.8731 1249.9273 1203.7512 1152.3728 982.3412 921.2745 heat_days 340.5123 362.7845 351.1843 346.1189 342.8751 310.2367 296.8512 wet_days 880.6214 942.8147 915.3402 897.5413 886.2364 793.1823 748.9016 source: extracted by the researcher from r output pa ge 76 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 70-79, 2025 table 2 presents a comparative evaluation of the mean square error (mse) values for different climate data models across four distinct climate variables: air temperature, precipitation, heat days, and wet days. this table assesses the predictive performance of seven models, arima, pca, two-stage dfm, em-based dfm, sparse dfm, lasso, and group lasso based on their ability to minimize forecast errors. across all variables, the group lasso model consistently delivers the lowest mse, demonstrating its superior predictive performance and robustness in modeling complex climate dynamics. for instance, group lasso achieves the best result for air temperature (mse = 0.3854), significantly outperforming traditional models like arima (mse = 0.4571) and pca-dfm (mse = 0.5013). similarly, for precipitation, a notoriously volatile and skewed variable, group lasso achieves a notably reduced error (mse = 921.2745) compared to arima (mse = 1105.2376) and pca (mse = 1304.8731). this highlights its strength in handling noisy and non-gaussian time series data. in the case of heat days, the trend holds, with group lasso again outperforming all others (mse = 296.8512), followed closely by lasso (mse = 310.2367). traditional models like pca and arima yield mses exceeding 340, indicating a higher deviation from actual observed values. a similar pattern is observed for wet days, where group lasso records the lowest error (mse = 748.9016), indicating more accurate estimation of precipitation frequency compared to older approaches. importantly, both lasso and group lasso outperform factor-based models (e.g., two-stage dfm, em-based dfm, and sparse dfm) in every climate category. while the factor models perform reasonably well, particularly sparse dfm, they do not match the consistency and predictive accuracy of penalized regression techniques. table 2: mean square error (mse) for the climate data models model metrics climate variable air_temp precipitation heat_days wet_days arima aic 68.7300 97.5400 86.6000 79.9300 bic 75.2902 104.0999 92.1808 93.5918 loglik -34.3650 -48.7700 -43.3000 -39.9650 sic 83.2958 112.6403 97.2838 103.4413 pca aic 183.2400 121.2300 118.1800 118.3400 bic 192.8036 134.1013 129.6592 127.7084 loglik -91.6200 -60.6150 -59.0900 -59.1700 sic 203.9222 140.4963 137.5806 136.3721 2stage aic 131.0500 160.6700 107.9700 136.2800 bic 141.9741 166.1345 119.0454 142.9852 loglik -65.5250 -80.3350 -53.9850 -68.1400 sic 147.4295 177.7767 130.8049 153.6440 em aic 107.4200 88.7900 141.5800 119.6100 bic 113.2743 97.2562 146.8207 130.9752 loglik -53.7100 -44.3950 -70.7900 -59.8050 sic 119.5682 105.5688 153.3793 138.5756 sdfm aic 121.4700 90.7100 157.4100 140.8900 bic 131.1675 100.1841 165.3995 150.4994 loglik -60.7350 -45.3550 -78.7050 -70.4450 sic 136.6100 106.1641 170.6256 157.1260 lasso aic 94.9800 84.4200 134.5900 92.1100 bic 101.3847 92.1335 140.2946 101.1210 loglik -47.4900 -42.2100 -67.2950 -46.0550 sic 106.7574 102.0679 149.1558 107.1146 group lasso aic 52.5800 79.1200 103.7700 85.6700 bic 58.7600 84.7700 109.1450 92.4500 loglik -26.2900 -39.5600 -51.8850 -42.8350 sic 64.9350 89.1150 114.2100 97.5200 source: extracted by the researcher from r output pa ge 77 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 70-79, 2025 table 3 and figure 3 presents a comprehensive comparison of model fit statistics including akaike information criterion (aic), bayesian information criterion (bic), log-likelihood (loglik), and schwarz information criterion (sic) across seven competing models and four climate variables: air temperature, precipitation, heat days, and wet days. these metrics collectively provide insight into how well each model balances goodness-offit with model complexity. from the table, group lasso emerges as the most consistently well-fitting model across all climate variables. it achieves the lowest aic, bic, and sic values, and the highest log-likelihoods, signaling superior model parsimony and explanatory power. for example, in the modeling of air temperature, group lasso records an aic of 52.58, significantly outperforming traditional arima (aic = 68.73) and pca-based dfm (aic = 183.24). this pattern holds for precipitation (aic = 79.12), heat days (aic = 103.77), and wet days (aic = 85.67), confirming the efficiency of group lasso in managing complex multivariate relationships. looking at bic and sic, which penalize complexity more heavily than aic, group lasso maintains its dominance. its lowest bic scores (e.g., 58.76 for air temperature and 84.77 for precipitation) suggest that it achieves excellent model parsimony despite fitting high-dimensional data. similarly, the lowest sic values across all variables further confirm the model’s robustness and generalizability to new data. the log-likelihood scores follow the same trend: group lasso consistently achieves the least negative values, e.g., -26.29 for air temperature and -51.89 for heat days, indicating a higher likelihood of observing the data given the fitted model. in contrast, pca and two-stage dfm models perform relatively poorly, particularly in terms of aic and loglikelihood. for instance, pca yields the worst aic for air temperature (183.24) and the lowest log-likelihood across all variables. these results suggest overfitting or inadequate representation of complex climate dynamics. interestingly, while lasso also performs well, it slightly lags behind group lasso, particularly in sic and bic metrics. for example, its sic for wet days is 107.11 compared to 97.52 for group lasso. this gap implies that group lasso’s grouped penalization leads to more efficient model selection and better performance under information-theoretic criteria. figure 3: aic, bic, loglikelihood and sic across models by climate variable conclusion result from application to climate data revealed that group lasso consistently yielded the lowest mse across key variables, air temperature (mse = 0.3854), precipitation (921.27), heat days (296.85), and wet days (748.90) outperforming all benchmark models. arima, pca, and two-stage dfm recorded substantially higher errors, highlighting their inability to capture intricate, nonlinear dependencies present in climate processes. in terms of model fit, group lasso once again outperformed all competitors with the lowest aic, bic, and sic values across all variables, and the highest log-likelihoods. for instance, in modeling air temperature, it posted an aic of 52.58 and a log-likelihood of -26.29, compared to pca’s aic of 183.24 and log-likelihood of -91.62. based on the findings from this study, for annual average mean surface air temperature, annual precipitation, number of days with heat index > 35°c, and maximum number of consecutive wet days, the lasso and group lasso should be utilized. references adams, s. o., & bamanga, m. a. 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(2006). the adaptive lasso and its oracle properties. journal of the american statistical association, 101(476), 1418–1429. pa ge 1 pa ge 80 american journal of applied statistics and economics (ajase) estimating rural premium and financial crisis effect on the nexuses between food, energy, and water consumption on urban-rural income gap in south–eastern asian countries using pooled regression analysis khambai khamjalas1* volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.2600 https://journals.e-palli.com/home/index.php/ajase article information abstract received: february 29, 2024 accepted: april 02, 2024 published: april 06, 2024 poverty and inequality reduction and access to affordable clean energy and clean water are among the global sustainable development goals. yet, researchers have overlooked how food, energy, and water (few) resources can be instrumental in reducing the urban-rural income gap. in this article, ordinary least squares regression analysis was used to estimate rural premium and financial crisis effect on the nexuses between food, energy, and water consumption on urban-rural income gap using a sample of data pooled from three asian countries: china, india, and indonesia over 2000 to 2019. no significant urban or crisis effect on the poverty rate was established. however, a significant crisis effect on the poverty gap was established, but not on the urban premium. a significant positive interaction between food insecurity and water was established. water supply improves agricultural production, improves food security, and reduces poverty by raising income. however, modern clean energy is associated with rising income inequality. modern energy technologies benefit a few wealthier individuals investing in the energy and agriculture sectors. therefore, improved water access, particularly to support food production and affordability, and efficient utilization of clean fuels and technologies among all individuals, regardless of socio-economic class, are crucial to escaping poverty and achieving prosperity. keywords energy, food, income inequality, poverty gap, poverty rates, water 1 school of economics and trade, hunan university, yuelu district, changsha 410006, p.r. china * corresponding author’s e-mail: baibay1980@gmail.com introduction ensuring equity in food, water, and energy resource distribution for a rapidly growing population remains a fundamental development challenge in south asian countries. the population of asia is the largest among all the continents in the world. china (1.43 billion people) and india (1.37 billion) were the world’s most populous countries, with 19% and 18% of the global population in 2019, respectively. the us and indonesia are the third and fourth most populous countries, with 329 million and 271 million people in 2019, respectively. the population of asia is expected to increase from 4.7 billion in 2023 to 5.3 billion in 2055, still ranking first, followed by africa at 2.7 billion, while other continents trail below a million. india is projected to surpass china as the world’s most populous country around 2027 (united nations [un], 2019). the un (2019) anticipates that india could remain the world’s most populous country, with about 1.5 billion people, followed by china with about 1.1 billion. the 2023 estimates, as of october, indicate that the population of asia is 4.753 billion, equivalent to 59.1% of the total world population (8.045). southern asia leads with about 2.027 billion, followed by eastern asia (1.662), south-eastern asia (0.686 billion), western asia (0.298), and central asia (0.078). the asian urban population is estimated at 52.6 % (2.500 billion). by 2025, asia will account for about 61.4 % of the world’s population, with a 53.8 % (2.590 billion) urban population (worldometer, 2023). given its rising population, the region faces mounting challenges in meeting the growing demand for food, water, and energy for a rapidly growing population. consequently, asia’s high population has contributed to income inequality between the rural households with adequate access to food, energy, and water, given their high incomes. food insecurity is a common problem worldwide, particularly in developing countries, often caused by rising population and natural disasters such as drought, flood, and epidemics. the 2021 global hunger index (ghi) report indicated that despite the overall decline in ghi (score between 0: hunger and 100: worst) from series levels in 2006 (25.1) and 2012 (20.4) to moderate in level in 2021 (17.9) there is evident continental and intercountry disparity (grebmer et al., 2021). africa, south of the sahara regions, ranks highly (30.5 in 2012 vs. 27.1 in 2021), followed by south asia (29.2 in 2012 vs. 26.1 in 2021), west asia and north africa (14.1 in 2012 vs. 12.7 in 2021), latin america and the caribbean (8.5 in 2012 vs. 8.7 in 2021), east and south-east asia (11.0 in 2012 vs. 8.5 in 2021), and lastly europe and central asia (7.5 in 2012 vs. 6.5 in 2021). despite the low ghi ranking (< 9.9) among south-east asian countries, there are countries with moderate (10.0 – 19.9) and serious (20.0 – 34.9) indices in 2021. timor-leste (32.4) leads in terms of ghi, followed by laos (19.5), cambodia (17.0), indonesia (18.0), the philippines (16.8), and vietnam (13.6). malaysia (12.8), and lastly thailand [11.7] (grebmer et al., 2021; our world in data, n.d.). drivers of food insecurity include climate change through severe high drought sporadic rainfall patterns (mbow et al. 2020). pa ge 81 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 80-91, 2024 there has been a remarkable rise in access to electricity globally over the past decades. the united nations report 2019 indicated that the global electricity access rate rose from 75% percent in 2000 to 90% in 2019 (united nations statistics division [un], 2021). the efforts align with the sdg7 7.1.1 target of universal access to electricity by 2030. yet, electrification progress has indicated regional discrimination. central and southern asia and sub-saharan africa registered the largest access deficit in 2019. in central and southern asia, the access rate rose significantly from 59% in 2000 to 95% in 2019, whereas sub-saharan africa rose from 24% to 46% over the same period. besides, there is also evident urban-rural discrimination in access to electricity. the un (2021) estimated that 97 million people in urban zones and 471 million in rural remained unelectrified in central and southern asia and sub-saharan africa. in addition, there is global inequality in access to clean fuels and technologies (liquid petroleum gas, natural gas, and biogas). in 2019, 66% of the global population had access to clean cooking fuels and technologies, leaving about 2.6 billion households, mostly from asia and africa, reliant on inefficient and polluting cooking methods. while the global annual increase in the number of people with clean cooking access from 2015 to 2019 was remarkably high at 82.5%, regional inequality is evident. central and southern asia registered a 23.6% increase, 12.8% in eastern and south-eastern asia, 27.2% in sub-saharan africa, and 8.6% in western and northern africa. besides, urban-rural discrimination also exists. generally, the urban population is the leading consumer of clean energy. however, the annual growth rate of access to clean energy in rural areas has registered an impressive growth since 2000 (about 0.1%), growing faster, reaching 2% in 2019. contrarily, urban areas seem to be in a decelerating face since 2011, with improvements in clean cooking access stagnating in urban areas, registering a growth of less than 0.5% since 2011 (un, 2021). the exponential growth in access to clean energy in rural areas could potentially help lower income inequality since energy is a resource that can be used in economic activities such as in the food and beverages, hotel, or hospitality sectors. generally, there is a remarkable electrification effort globally. the extent to which such a global agenda is welfare-oriented remained unexplored by existing empirical literature since the ultimate drive is to minimize climate change effects by minimizing carbon emissions. since few are essential resources for socio-economic development, uneven distribution of the resources could further widen income inequality between the rural and urban settings. interestingly, between 2010-2019, the growth in access to clean fuels and technologies was dominated by the most populous countries, brazil, china, india, indonesia, and pakistan, registering a combined growth rate of 2% (un, 2021). therefore, the current study examined how improved access to few resources influences the rural income gap in south-east asian countries. literature review literature was reviewed based on the study’s objectives, investigating the relationship between food, energy, and water supply and household incomes. water access, food availability, and incomes increasing water productivity is vital in improving sustainable agriculture food security since water is useful in crop, tree, livestock, and fish production. agriculture accounts for 72% of global freshwater withdrawal from the world’s rivers, lakes, and groundwater aquifers yearly (center for strategic & international studies, 2019). most farming worldwide is carried out as mixed crop-livestock farming, covering about 2.5 billion hectares of land (n.d.). as a result, agriculture is a source of farm income sales of products like meat, milk and hides, livestock, and crop produce to a majority of households, especially in the humid and sub-humid regions of south-east and east asia, which have registered the greatest increase in irrigated mixed farming systems (food and agriculture organization [fao], n.d.). major constraints in livestock production, especially in arid and semi-arid areas, are feed shortage, animal diseases, and low productivity, especially during the dry season. common coping strategies to feed scarcity among pastoralists in arid and semi-arid areas are conserving crop residues and hay, purchasing roughages, reducing herd size, and renting grazing land (duguma & janssens, 2021). such remedies often lead to losses since dry roughages are not nutritious and may reduce animals’ live weight. reducing herd size is often associated with a throwaway price since holding large herd in dry seasons could lead to huge losses from animal mortality (roche et al., 2021). improved water access improves livestock production and health through increased fodder and legume feed production, livestock watering, and sustainable pasture grazing management, hence increasing earnings from livestock sales through increased live weight while reducing animal mortality (mbow et al., 2020; mayberry et al., 202; ndlovu et al., 2020). monjardino et al. (2020) established that maintaining a high-quality legume crop in the mixed crop-livestock system increases farm income and reduces financial risk from integrating more resilient livestock and higher crop revenue among traditional mixed smallholder farms in south-east asia. hashmi et al. (2021) established that while pakistan has the world’s largest integrated irrigation system, water scarcity has constrained farmers to shift cultivation from waterintensive crops like rice, wheat, cotton, and sugarcane to other crops and vegetables, which require less water, thus increasing pressure in the food market. such a shift threatens the diversification in agriculture, reducing the incomes of households with limited access to water for irrigation due to financial constraints in collecting water from a distant source by ferrying using vehicles. thus, providing public or institutional water supply is essential in ensuring sustainability in agricultural production. increased irrigation improved the productivity of crops pa ge 82 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 80-91, 2024 by maintaining health and vigorously growing crops through optimized water, nutrients, and agronomic management (descheemaeker et al., 2013). lastly, aquaculture is predominately reliant on water. according to the fao (2021), in 2020, the total global aquaculture production comprised 122.6 million tonnes of aquatic animals, with 87.5 million tonnes used for human consumption. the largest producing region is asia, accounting for over 88.4% of the total fisheries and aquaculture production of aquatic animals in 2020, substantially higher than africa (2.57), americas (5.00), europe (3.74), and oceania (0.26). due to its high population, china (mainland) has produced more farmed aquatic animals than the rest of the world since 1991. its share in world aquaculture production was 64.13% for aquatic animals in 2020, followed by india (11.16), indonesia (6.75), vietnam (5.95), bangladesh (3.34), and the rest of asia (8.67). thus, increased water supply increases water usage in aquaculture, especially in asian countries like indonesia and china, which are leading world fish producers. domestic aquaculture increases the incomes of smallholder fish farmers supply while providing domestic consumption (tran et al., 2017). generally, improving agricultural water productivity creates synergies in crop and livestock production. it could consequently increase earning from sales of the produce, raising the incomes of most households who would have trailed below poverty lines. fitton et al. (2019) established that approximately 11% and 10% of current crops and grasslands could decline due to a reduction in water availability and may lose their productive capacity, particularly in africa, the middle east, china, europe, and asia. thus, improving water access in these regions is critical in improving agricultural productivity and, hence, the incomes of households. aquaculture’s benefits include food sources, livelihood improvement through sales of aquatic life, and nutrition and health (ahmed & thompson, 2019; mills et al., 2019; dinesh, 2016). energy and income inequality there is a lack of consensus on the relationship between clean fuel and income inequality. generally, the benefits of access to clean energy sources are realized through employment opportunities, economic growth, education, industrialization, and improved healthcare outcomes (acheampong, dzator shahbaz, 2021). access to electricity improves the livelihoods of households through incomes from investments and employment opportunities; improves food production through cultivation, harvesting, processing, preservation, and transportation; and improves water productivity through processes such as water desalination, filtering, treatment, distribution, harvesting, recycling (biggs et al., 2015; nilsson, griggs, & visbeck, 2016). in china, ma et al. (2021) established that increased per capita energy consumption leads to declining energy poverty alleviation and inequality reduction in rural households. huang et al. (2020) outline that labour migration from rural to urban areas is a structural change and footpaths of the negative effect of energy supply on income inequality. according to the authors, low-carbon policies have the greatest impact on employment across all energy. thus, labor will migrate from rural to urban areas for better jobs. the reduced rural population brings new opportunities for the modernization of agriculture, increasing the income of rural residents. the income gap among urban residents will widen in the short run due to the labor demand and education level differentials. in the end, equitable development of resources between urban and rural areas is achieved. using panel data for 166 countries to investigate from 1990 to 2017, acheampong, dzator, and shahbaz (2021) established that access to electricity reduces global income inequality, while access to modern and clean energy increases global income inequality. further, the authors revealed that rural and urban electrification reduced income inequality; however, the elasticity of urban electrification exceeds rural electrification. thus, there could be heterogenous effects of access to clean energy on income inequality in urban and rural settings – which was controlled for in this study to minimize the bias in the regression results. other studies have shown that access to clean energy increases income inequality. using panel data from 46 countries in sub-saharan africa from 1990 to 2017, sarkodie and adams (2020) established that access to electricity widens income inequality. according to the authors, access to electricity widens the income gap between the rich and the poor since modern energy technologies tend to benefit the wealthier in terms of better investment opportunities, such as in the energy and agriculture sectors. besides, there is no clear direction of the causality effect between income inequality and renewable energy consumption. uzar (2020) established that a declining income inequality will enhance renewable energy consumption. xu and zhong (2023) established that high-income inequality increases energy consumption. the lack of consensus on whether income inequality implies that the findings could be contextual based on region, methodology, and variables used. in this study, data is pooled from three asian countries to estimate how few nexuses influence can influence income inequality while controlling for the 2008/09 financial crisis and socio-economic class of rural and urban settings. methodology study design the study adopts a panel study design. the study used a convenient sample of three asian countries, china, india, and indonesia since the data on the rural and urban settings on all the study variables were missing. the two outcome measures were poverty rates and poverty gap. the predictors were few resource metrics: food insecurity, access to electricity, access to clean fuels and pa ge 83 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 80-91, 2024 technologies for cooking, and improved water access. a binary predictor was created to delimit 1 for if urban and zero otherwise to establish the urban premium. lastly, existing empirical evidence indicates that countries with high populations have been associated with greater income inequality (krieger & meierrieks, 2019). the data were collected from different sources, comprising the world bank’s (wb) world development index database (wdi), the food and agriculture organisation (fao), and our world in data (owid) databases. the data was collected for three countries (china, india, and indonesia) between 2000 and 2019. the study variables’ operationalization and data source are summarized in table 1. table 1: variables, units, and sources code name description units source pv_rate poverty rate poverty headcount ratio (% of the population living below $2.15) % world bank. (n.d.-a) pv_gap poverty gap the ratio by which the mean income of the poor falls below the poverty line ($2.15) % world bank. (n.d.-a) foodins prevalence of severe food insecurity food insecurity china and india: food insecurity experience scale (fies). this indicator measures the proportion of people uncertain of having or unable to acquire enough food because they have insufficient money or other resources. indonesia; prevalence of undernourishment (% of population) % fao (2019) elcacc access to electricity % of the cohort (urban/rural) population who have success with electricity % world bank. (n.d.-b) cleanf access to clean fuels and technologies for cooking % of the cohort population who have access to clean fuels and technologies for cooking % world bank. (n.d.-b) impwacc improved water access people using at least a basic improved drinking water source includes piped water on premises (piped household water connection located inside the user’s dwelling, plot, or yard, public taps or standpipes, tube wells or boreholes, protected dug wells, protected springs, and rainwater collection). % owid, (n.d.-c). pop population size annual population size owid notes. foodins by urban and rural areas were incomplete and were imputed using ma (3) and empirical studies depicting the rural-urban gap on food insecurity. data analysis the study used pooled ordinary least square (ols) regression analysis to examine the urban premium and financial crisis effect on poverty rate and poverty gap and the nexuses between food, energy, and water consumption on urban-rural income gap in south-eastern asia. the data was pooled across rural and urban settings in three asian countries: china, india, and indonesia from 2000 to 2019. therefore, a rural-urban setting premium is captured by using an urban dummy. a rural dummy variable, labeled rural which takes the value “0” for each ith observation in a rural setting and takes the value “1” for the urban setting. a crisis dummy variable, labeled crisis will be created that takes the value “0” for each ith observation for the years between 2000 and 2010 and takes the value “1” for each observation ith from the year 2010 to 2019. let yi be the welfare measure i, the study seeks to fit two ols linear regression models in the form represented in equation 1. where yi are predict income inequality and poverty index, x(i,k) is a vector of few resources’ measures; ∆ is the differential operator whose order is dependent on the stationarity of the data; α is the interaction effect between treatment and time, βs are the regression coefficients of each of the four few metrics, namely, the prevalence of severe food insecurity, access to electricity, clean fuels and technologies for cooking, and water; τ denotes the interaction effect of the few resources that help identify the nexus between few in influencing in urban and rural income gap; φ is the crisis effect (crs), ω is the urban (urbn) premium/loss; θ is the interaction effect crisis and urban setting (crs*urbn); ∆lnpop is natural of population size, πi is the population effect; i is the observation index, s is the observation index, c is the country index, and t pa ge 84 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 80-91, 2024 is the time index; and εi is the error term capturing the variation in outcome measures not accounted for by the model. results the ols regression analysis was done to estimate rural premium and financial crisis effect on the nexuses between food, energy, and water consumption on the urban-rural income gap using a sample of data pooled from three asian countries: china, india, and indonesia. to ensure the robustness and validity of the findings, the diagnostic tests are first reported as follows. diagnostics tests four diagnostic tests, stationary, multicollinearity, heteroscedasticity, and normality tests, were examined to ensure that the reported regression results do not bias the regression estimates. stationary test the stationarity test was done using a fisher-type augmented dickey-fuller unit-root test since it does not require strongly balanced data (choi, 2001)). it tests the null hypothesis that all panels contain a unit root against an alternative hypothesis that at least one panel is stationary. the stationary test was done separately since the data was panel data stacked by urban-rural setting. all the variables provide sufficient evidence that the urban first differenced data is stationary at a 10% significance level. besides, all the variables provide sufficient evidence that the rural first differenced data is stationary at a 10% significance level. therefore, the first differenced series of continuous data was used in regression analysis (table 2). table 2: stationarity test for urban and rural data variable urban rural level first difference level first difference inverse chi-squared pvalue inverse chisquared pvalue inverse chisquared pvalue inverse chisquared pvalue poverty rate 5.17 0.522 26.11*** 0.000 0.791 0.992 16.66* 0.011 poverty gap 6.82 0.338 23.78*** 0.001 2.69 0.846 24.04*** 0.001 food insecurity 21.88*** 0.001 10.98* 0.089 21.82*** 0.001 10.64*** 0.100 access to electricity 12.67** 0.049 43.93*** 0.000 6.85 0.335 29.86*** 0.000 access to clean fuels and technologies for cooking 42.11*** 0.000 23.08*** 0.001 37.67*** 0.000 11.59*** 0.072 improved water access 38.88 0.000 16.63** 0.011 73.67*** 0.000 11.27* 0.080 population growth 8.8 0.185 11.35* 0.078 9.92 0.128 16.93** 0.010 notes: the reported statistics are based on the inverse-normal transformations. the drift option was specified since all the series’ means are nonzero. two lags in the adf regressions and cross-sectional means were removed using demean; degrees of freedom = 6. significant codes: *** p < .001; ** p < .01; * p < .05. multicollinearity the multicollinearity of the predictors (excluding the interaction terms) was examined using the variance inflation factor and tolerance factor. since the average vif is less than 5, multicollinearity is not a severe problem in the regression results. see table 3. table 3: variance inflation factors of the predictor variables variable variance inflation factor tolerance factor lnimpwacc_d1 10.73 0.093 lnpop_d1 9.52 0.105 urban 7.52 0.133 urbanpostcrisis 3.17 0.315 postcrisis 2.47 0.404 lncleanf_d1 2.32 0.431 lnfoodins_d1 1.39 0.719 lnelcacc_d1 1.08 0.927 mean vif 4.78 pa ge 85 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 80-91, 2024 heteroskedasticity test the results provide strong evidence of violation constant variance at a 5% significance level in the poverty rates, based on the breusch-pagan test for heteroskedasticity, χ2(1) = 32.3, p = .001 and the poverty gap model, χ2(1) = 20.14, p < .001 (table 4). thus, clustered standard errors by country are used to correct for heteroscedasticity. table 4: breusch-pagan / cook-weisberg test for heteroskedasticity model degrees of freedom chi-square statistic p-value poverty rate model 1 32.30 0.000 poverty gap model 1 20.14 0.000 normality test the residuals for the poverty rates and poverty gap models (without interaction terms) are approximately normally distributed, indicating that the models substantially satisfy the linearity assumption (figure 1). figure 1: histogram of the residuals for the poverty rates and poverty gap models correlation analysis the correlation analysis of the study variables based on the first differenced variables was done by setting (rural and urban) and aggregately using the pooled data. the correlation between poverty rate vs. food insecurity and electricity access is not statistically significant in all three panels at a 10% significance level (p > .1). besides, the correlation between the poverty gap vs. improved water access and clean fuel access is not statistically significant in all the three panels at a 10% significance level (p > .1) however, poverty rates seem to be positive and statistically significantly correlated with improved water access (r = .305, p < .1) and clean fuel access (r = 0.299, p < .1) in urban areas only at a 10% significance level (table 5). (see figure 2– 5) (table 5). table 5: correlation analysis between first differenced poverty gap and rate vs. food insecurity correlation pairs aggregate rural urban food insecurity poverty rate 0.058 0.101 0.006 poverty gap 0.075 0.031 0.111 water access poverty rate -0.001 -0.1335 0.306* poverty gap -0.048 -0.138 0.194 clean fuel access poverty rate 0.186* 0.171 0.299* poverty gap 0.141 0.149 0.241 electricity access poverty rate 0.143 0.169 0.174 poverty gap 0.148 0.187 0.152 notes. *** p<0.01, ** p < 0.05, * p < 0.1 pa ge 86 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 80-91, 2024 figure 2: correlation analysis between first differenced poverty rate and gap vs food insecurity figure 3: correlation analysis between first differenced poverty rate and gap vs. improved water access figure 4: correlation analysis between first differenced poverty rate and gap vs. clean fuel pa ge 87 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 80-91, 2024 figure 5: correlation analysis between first differenced poverty rate and gap vs. electricity access regression results the regression results indicate no significant urban premium effect on poverty rates (β= 0.006, p > .05). access to clean fuels and technologies for cooking is the only resource that contributes to increasing poverty rates at a 10% significance level (β=1.508,p<.1) and poverty gap (β=1.741,p<.05) at a 5% significance level. thus, a 1% increase in clean fuels and technologies increases the poverty rate and gap by 1.508% and 1.741%, respectively. the results show no significant urban (-0.160) and crisis (-0.156) effect on poverty rate. however, a significant negative crisis effect at a 10% significance level (β= -0.245, p<.1) on poverty gap was established but not urban premium (β= -0.226, p >.1). further, a significant interaction between food insecurity and water (β= -176.1, p < .05) and between clean fuel and cooking technologies and electricity (β= 12.59, p > .05) on poverty gap was established (see table 6). table 6: the ols regression results predicting the urban premium and crisis effect on the nexuses between few resources and poverty rates and poverty gaps variables (1) (2) (3) (4) (5) (6) ln poverty rates_d1 ln poverty rates_d1 ln poverty rates_d1 ln poverty gap_d1 ln poverty gap_d1 ln poverty gap_d1 lnfoodins_d1 0.0455 0.660 0.0627 0.408 (0.411) (0.850) (0.430) (1.159) lncleanf_d1 0.199 1.508* 0.277 1.741** (0.242) (0.509) (0.215) (0.365) lnelecacc_d1 0.537 1.203 0.705 0.535 (0.299) (1.077) (0.491) (1.114) lnimpwacc_d1 10.14 11.67 4.833 4.640 (8.140) (9.168) (8.893) (12.27) foodins_fuel 2.761 8.982 (6.991) (7.804) foodins_electricity -1.582 1.248 (13.04) (13.66) foodins_water -111.6 -176.1** (40.43) (32.48) fuel_electricity 9.261 12.59* (4.046) (3.210) fuel_water -111.4 -114.9 (53.56) (51.61) pa ge 88 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 80-91, 2024 electricity_water -220.8 -177.7 (184.2) (177.4) urban 0.006 -0.160 -0.212 0.022 -0.165 -0.226 (0.005) (0.082) (0.139) (0.014) (0.069) (0.132) postcrisis -0.156 -0.212 -0.176 -0.245* (0.101) (0.0892) (0.0920) (0.0801) urbanpostcrisis 0.134 0.229 0.135 0.235 (0.107) (0.106) (0.0864) (0.0904) lnpop_d1 6.656* 5.989 5.895* 5.092 (1.619) (2.659) (1.847) (3.069) constant -0.160 -0.199* -0.187** -0.188 -0.168* -0.144 (0.064) (0.052) (0.043) (0.066) (0.057) (0.071) observations 114 114 114 114 114 114 r-squared 0.000 0.217 0.251 0.002 0.183 0.217 notes. robust standard errors are in parentheses. the clustered standard errors were done in urban and rural settings. all the series were first differenced (d1) since all the series were first stationary at first difference.; significant codes: *** p < 0.01, ** p < 0.05, * p < 0.1. discussion the study used pooled ols regression analysis to examine the urban premium and financial crisis effect on poverty rate and poverty gap, the nexuses between food, energy, and water consumption on the urban-rural income gap in south-eastern asia. using sample data pooled across a rural and urban setting in three asian countries, china, india, and indonesia, from 2000 to 2019, the regression results indicate that access to clean fuels and technologies for cooking is the only resource that contributes to increasing poverty rates and gaps. the correlation analysis indicates that poverty rates are positive and statistically significantly correlated with improved clean fuel access in urban areas but not rural areas. while clean energy has been rising globally, its benefits and impacts may vary across rural and urban areas due to economic barriers to accessing clean energy services. clean fuels and cooking technologies, including solar, electric, biogas, natural gas, liquefied petroleum gas (lpg), and alcohol fuels, including ethanol, are more abundant in urban areas (un, 2021). comparatively, while rural areas have seen a substantial increase in cleaner gaseous fuels, biomass fuels, such as charcoal, are the dominant form of cooking energy. such trends could be influenced by the fact that clean energy could be expensive to rural households, where most have lower incomes and larger families than those in urban areas. thus, rural households might otherwise opt for firewood collected for free in the neighborhood; poor households are also likely to have low access to energy. as a result, clean energy supply creates a negative feedback loop in urban areas. the high abundance in urban areas and a greater social stratification means that the elasticity of poverty rates is highly responsive to clean energy supply. ma and liao (2018) established that the income effect is positive for cleaner fuels (lpg and electricity) but negative for biomass fuels like coal. adopting cleaner fuels as primary cooking fuel was income elastic among rural households but inelastic for urban households, whereas the substitution of dirty fuels is all income inelastic. thus, rural households may not be responsive since most rely on biomass. other studies have suggested reverse causality, from income inequality to access to clean fuels. using data from 14 latin american and caribbean countries, murshed (2023) established that increasing income inequality aggravates the urban-rural inequality in clean cooking fuel accessibility by improving and reducing urban and rural clean cooking fuel access rates, respectively. in another study, acheampong, dzator, and shahbaz (2021) revealed that rural and urban electrification reduced income inequality. however, the elasticity of urban electrification exceeds rural electrification. thus, the greater social stratification means that the elasticity of poverty rates is highly responsive to clean energy supply. in addition, most high-income earners rely on clean energy; hence, their consumption and utilization levels in income-generating activities such as in the hotel or hospitability and industry sector can go up. however, most low-income earners in urban areas rely on clean fuels for household cooking (un, 2021). the same also applies to rural households who might heavily rely on clean fuels for home usage and not for income generation. thus, raising clean energy could increase the proportion of the population living below the poverty line, usually taken as half the median household income of the total population, as a few households earn relatively more than the majority if the households. the assertion is consistent with the stronger evidence that a 1% increase in clean fuels and technologies increases the poverty gap by 1.741%. to mitigate such tradeoff, affordability of clean fuels and cooking technologies and enhancing incomegenerating activities that utilize clean energy in rural and urban low-income households should be a top priority in achieving sdg 7. the results also indicated a significant positive interaction pa ge 89 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 80-91, 2024 between food insecurity and water. the finding implies that the positive impact of food insecurity on poverty gaps is lessened by increasing water access. the finding is consistent with the expectation that agricultural production relies heavily on water. water supply is essential for agriculture, as it affects the productivity and sustainability of crop and livestock systems. for instance, fitton et al. (2019) established that approximately 11% and 10% of current crops and grasslands could decline due to reduced water availability and may lose their productive capacity, particularly in africa, the middle east, china, europe, and asia. water supply also minimizes annual crop and livestock losses, reducing poverty gaps and improving food security (ndlovu, prinsloo & le roux, 2020). irrigated cropping systems can benefit from improved water productivity by maintaining healthy, vigorously growing crops through optimized water, nutrient, and agronomic management (descheemaeker et al., 2013). irrigation also reduces the risk of crop failure due to droughts and allows for multiple cropping seasons (organisation for economic co-operation and development, n.d.). likewise, water supply supports livestock production, an important income source and nutrition for many poor households. water availability and quality affect animals’ health, growth, and reproduction. livestock water productivity can be increased through sustainable grazing or feeding management from planted crops and pasture and livestock watering availability, leading to reduced animal mortality, which is a common problem among pastoralists who live in arid and semi-arid areas (descheemaeker et al., 2013; gusha, 2019; otte et al., 2019). livestock also provide manure, which can fertilize crops and improve soil quality. conversely, crop residues as animal feed, animal traction for land preparation, and crop-livestock rotations for pest and disease control create synergies between crops and animals, improving agriculture’s efficiency and sustainability (baiyeri et al., 2019). thus, water scarcity negatively impacts poverty gaps propagated through reduced crop and livestock production. the finding is also consistent with empirical evidence. for instance, monjardino et al. (2020) established that maintaining a high-quality legume crop in the mixed crop-livestock system increases farm income and reduces financial risk from integrating a more resilient livestock and higher crop revenue among traditional mixed smallholder farms in south east asia. besides, aquaculture improves livelihoods through selling aquatic life (ahmed & thompson, 2019; mills et al., 2019; dinesh, 2016). thus, increasing population access to basic improved drinking water sources includes piped water, public taps, wells or boreholes, and springs, increasing water available for crop production, livestock keeping, and aquaculture. the increasing incomes, in turn, lessen poverty gaps as low incoming earners’ incomes rise. the results also indicated a significant positive interaction between clean fuel and cooking technologies and electricity on the poverty gap. the finding implies that clean energy widens poverty gaps. the study finding is consistent with existing empirical evidence of sarkodie and adams (2020) that established that rising access to clean energy has increased income inequality in subsaharan africa. according to sarkodie and adams (2020), access to electricity increases the income gap between the rich and the poor. modern energy technologies benefit the wealthier in terms of better investment opportunities, such as in the energy and agriculture sectors. besides, xu and zhong (2023) established that high-income inequality increases energy consumption, implying a tentative positive correlation between access to clean energy. while huang et al. (2020) argue that clean energy supplies reduce income inequality through structural unemployment, where labour migrates from rural to urban areas until an equilibrium is attained, it might occur in the long run since existing rural households might not get the requisite skills to take up new jobs created the energy sector. thus, a rising energy supply can keep increasing the incomes of a few individuals, whereas the majority who live in urban areas remain unemployed or unemployed. lastly, the results indicated that poverty gaps were generally lower in the post-global financial crisis of 2008-2009. the finding can be associated with structural changes in employment and investments that the financial crisis might have shaped due to the housing market bubbles in the real estate sector (bartmann, 2017). the shock might have shifted investments to more resilient sectors like energy and food supplies. besides, lending institutions’ moral hazard might have made banks minimize market risks by financing more resilient sectors dealing with few resources since they are the basic life-supporting needs. other confounders include rising education skills (lee & lee, 2018), declining unemployment rates, and labour unions that push for equity in wage distribution through their collective bargaining power (dosi et al., 2018), and welfare policies such as progressive taxation (oishi, kushlev, & schimmack, 2018) that have been established to lower income inequality. conclusions in this study, ols regression analysis was done to estimate rural premium and financial crisis effect on the nexuses between food, energy, and water consumption on urban-rural income gap using a sample of data pooled from three asian countries: china, india, and indonesia. the results revealed increased clean energy (fuel and electricity) has contributed to rising poverty gaps. to mitigate the tradeoff between clean energy supply and poverty supply, especially in rural areas, a holistic and inclusive approach is needed to ensure that clean fuels and technologies for cooking contribute to sustainable development for all. besides, supplying affordable clean fuels and technologies and enhancing income-generating activities that utilize clean energy in rural and urban lowincome households should be a top priority in achieving sdg 7. pa ge 90 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 80-91, 2024 improved water access is crucial in increasing crop and livestock production and lowering poverty gaps. water supply improves agricultural production, improving food security and reducing poverty by raising income from crops, livestock, and fish farming, especially among low-income households heavily reliant on agriculture. thus, equitable access to water resources, regardless of the socio-economic class in the society, particularly to support food production, is instrumental in reducing poverty and hunger. references acheampong, a. o., dzator, j., & shahbaz, m. 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(2023). the impact of income inequity on energy consumption: the moderating role of digitalization. journal of environmental management, 325, 116464. https://doi.org/10.1016/j. jenvman.2022.116464 pa ge 1 pa ge 15 4 american journal of applied statistics and economics (ajase) the nexus between “village” banking model & women’s financial inclusion in zambia richard mulenga1*, ng’andwe namfukwe muuka2 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.3793 https://journals.e-palli.com/home/index.php/ajase article information abstract received: september 09, 2024 accepted: october 17, 2024 published: december 18, 2024 this study explores the relationship between the ‘village’ (cooperative microcredit) banking model and the financial inclusion of women in zambia, using the afrizam co-operative as a case study. multiple regression and correlational analyses were employed as quantitative approaches, while the thematic analysis, elucidated via the saturation strategy, constituted the qualitative approach. the study employed a mixed-method approach, with a sample size of 109 individuals. findings indicate that co-operative society models significantly promote financial inclusion among women in zambia. specifically, savings, access to credit and social capital indicate that holding other factors constant, a 1% increase in each variable significantly increases financial inclusion by 25.5%, 28.1% and 44.3%, respectively. among the challenges that hinder the financial inclusion of women at afri-zam, the study revealed that administrative inefficiencies and inadequate policy support persistently hinder the optimal financial inclusion of women. to mitigate these challenges and augment financial inclusion prospects, the study recommends policy actions that include strengthening savings initiatives among village banking microcredit institutions, enhancing access to credit among women, building and leveraging social capital, addressing administrative inefficiencies by streamlining bureaucratic processes and continuing financial literacy campaigns among women, among others. keywords cooperative microcredit, frizam co-operative, village banking model, women’s financial inclusion, zambia, e21, g28, b54 1 department of economics, zcas university, box 35243, lusaka, zambia 2 school of business, zcas university, box 35243, lusaka, zambia * corresponding author’s e-mail: richardmulenga2@gmail.com introduction this study explores the link between the village banking model (or cooperative microcredit) and women’s financial inclusion in zambia, focusing on the afrizam cooperative as a case study. access to financial services is vital for economic development and poverty reduction (omar & inaba, 2020). however, women in developing countries often face barriers to formal banking, such as limited collateral, discriminatory practices, and low financial literacy (morsy, 2020). micro-financing, especially through village banking, has proven effective in enhancing women’s financial inclusion (gideon et al., 2020). micro-financing provides small loans and savings to those excluded from traditional banking (bhusare & chanda, 2017). village banking involves community members forming a savings and lending group and pooling resources to offer financial services, particularly to marginalized women (sibeso, 2022). this model fosters mutual trust and cooperation, allowing members to contribute to a common fund for loans aimed at business, education, healthcare, or emergencies (chisenga, 2018; yan et al., 2024). by improving access to credit, village banking empowers women to manage their finances, build assets, and enhance their livelihoods (fula, 2023). grameen bank, founded by muhammad yunus in bangladesh in 1983, is a pioneering microfinance institution recognized for its innovative approach to poverty alleviation. it provides small, collateral-free loans, or microcredit, primarily to economically disadvantaged women who struggle to access traditional financial services. yunus (1983) developed the concept of microcredit in the 1970s, believing that financial access is crucial for empowering the poor. the bank operates on principles of trust and community, with borrowers organized into small groups to support each other in loan repayment. a notable model that has emerged is village banking, which focuses on localized, community-oriented microfinance services (yunus, 1983; asif, 2021). chisenga (2018) notes that village banking emphasizes group lending and social collateral, allowing community members to form savings and lending cooperatives. in zambia and many african countries, women face significant barriers to accessing formal financial services, including limited banking access, low financial literacy, socio-cultural norms, and gender discrimination (world bank, 2017). as a result, women often rely on informal financial systems that may not meet their needs or provide adequate security. village banking offers a grassroots approach to improving financial inclusion by delivering banking services directly within communities (mponzi et al., 2023). village banking programs often prioritize women as beneficiaries, recognizing their role as key agents of change in their families and communities (sibeso, 2022). village banks in zambian context are operated as “chilimba” in the zambian informal financial sector (mukulu & qutieshat, 2021). chilimba rotational savings groups have been found to be easy to operate despite their high risk. on a more regulated and formal platform, village banks in zambia are registered as financial cooperatives under the ministry of commerce, trade and industry (mcti), which are seen as a more suitable structure to support poor people in rural areas with financial services in their farming activities (dfid, 2012). the government of zambia has acknowledged the vital pa ge 15 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 154-166, 2024 role of cooperatives in national development through the seventh national development plan (7ndp), which emphasizes their potential for job creation and poverty alleviation. the government aims to expand the cooperative model beyond agriculture, positioning cooperatives as viable business entities. since 2015, all cooperative activities have been moved from the ministry of agriculture to the ministry of commerce, trade and industry (mcti) to encourage diversification. additionally, the government is revising the cooperative act to strengthen the legal framework for cooperatives. new empirical data are also needed to better understand the current landscape of cooperatives (7ndp, 2018). financial cooperatives have long served as a key savings option for zambians. recently, zambia has made significant strides in financial inclusion, with the percentage of financially included adults rising from 33.7% in 2005 to 59.3% in 2015, surpassing the 2013 target of 50% and aiming for 80% by 2022. urban areas saw a notable increase from 42% in 2009 to 70.3% in 2015, while rural areas rose from 34.4% to 50.1%. informal financial services, such as savings groups and ‘chilimbas,’ have been key drivers of this growth, with informal inclusion increasing from 22% to 38% during the same period. many find these services more accessible and beneficial than formal financial institutions, with village banks being easy to set up and typically charging only a minimal membership fee (zipar policy brief no. 30-2019). for many years, the popular savings group that zambians have known pre and post-independence are the financial cooperatives. at the time of independence, zambia had an estimated 6 financial cooperatives registered during the colonial era. by 1976, the number of registered financial cooperatives had risen to approximately 500. the growth in financial cooperatives saw the need for the creation of a regulating body to oversee the operations of the cooperatives in the country, and in 1977, the credit unions and savings association (cusa) was born (7ndp, 2018). recent studies have highlighted the positive impact of village banking models on financial inclusion, particularly for women (addai, 2017; rahman et al., 2017; pakkanna et al., 2020). access to financial services can empower women economically, allowing them to start or expand small businesses, invest in education and healthcare, and improve their overall quality of life (magali, 2021). village banks in zambia are a form of informal credit unions (cu) or savings and credit cooperatives (saccos) and have improved the economic status and quality of life for women (chisenga, 2018). in addition, the bank of zambia (boz) and other key stakeholders have been working towards greater financial inclusion and gender equality, in line with the zambia vision 2030 (7ndp, 2018; bank of zambia, 2022). these efforts have been integrated into the country’s first national financial inclusion strategy (fnfis), which aims to halve the gender gap and increase women’s financial inclusion to 70 per cent by 2025 from 30 percent in 2015. efforts to improve financial inclusion have been made globally, but women in developing countries like zambia still face significant barriers to accessing formal financial services due to socioeconomic inequalities, cultural practices, and poor banking infrastructure (sibeso, 2022). village banking has emerged as a promising solution to enhance financial inclusion for women. research by mbiro and ndlovu (2021); bhatia and singh (2019), for instance, shows that access to financial services can significantly improve women’s economic status and quality of life, enabling them to start or grow businesses, save, and invest in education and healthcare. the afrizam co-operative in lusaka is an example of a village banking initiative aimed at supporting women’s financial well-being. however, there are still gaps in understanding the effectiveness and linkages of such initiatives, particularly regarding the african financial cooperatives. while studies have highlighted the benefits of saving groups for women’s empowerment, more research is needed to explore the relationship between the village banking model and women’s financial inclusion in zambia. therefore, this study fills this gap by conducting a thorough assessment of the effects of village banking on financial inclusion for women, focusing on the afrizam financial co-operative in lusaka. this research presents important ramifications for the microfinance sector and the financial inclusion of women in zambia. the following objectives guided the study. i. to analyse the factors that tend to promote financial inclusion (via the women co-operative models) such as the afri-zam co-operative. ii. to assess the level of financial inclusion for women in zambia using women’s cooperative models, such as the afri-zam co-operative society in lusaka. iii. to unravel the challenges that hinder financial inclusion for women at the afri-zam co-operative. the study tests the composite hypothesis that savings, access to credit, and social capital have a significant negative impact on the financial inclusion of women in zambia. literature review theoretical review the microfinance theory of change in “banker to the poor,” muhammad yunus (1983) presents a transformative view through the microfinance theory of change, critiquing traditional banking’s neglect of marginalized populations. he argues that conventional banks often exclude low-income individuals from essential financial services, perpetuating poverty. yunus (1983) introduces microfinance as a solution, offering small, collateral-free loans to those deemed too risky by mainstream banks, particularly empowering women facing additional barriers to financial inclusion. these microloans enable women to invest in small businesses, fostering economic resilience and self-esteem. yunus emphasizes community trust and mutual accountability through group lending, where borrowers support pa ge 15 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 154-166, 2024 each other in repaying loans, reducing default risk and promoting financial discipline. this approach enhances social connections and support networks, amplifying microfinance’s positive impact on individual and community welfare. in the short term, microfinance helps borrowers improve their living conditions by addressing urgent needs like food security, housing, and healthcare. microfinance offers benefits beyond individual financial security, as yunus suggests it can drive societal progress in education, health, and gender equality. in the context of village banking for women’s financial inclusion in the afrizam co-operative lusaka, the theory of change is vital for understanding how these initiatives can lead to positive outcomes. this framework outlines the steps that lead to lasting impacts, such as improved financial literacy, increased access to credit, and income-generating activities. utilizing the theory of change is crucial for designing and evaluating village banking programs, providing insights into how specific actions can foster economic improvement and reduce poverty. ultimately, microfinance empowers individuals, particularly women, to take control of their finances, contributing to a more equitable and just society. the strategic default theory in “the subprime solution,” shiller (2008) explores strategic default, where borrowers stop mortgage payments despite being able to pay. this often occurs when a property’s market value drops below the mortgage balance, leading borrowers to view continued payments as economically unwise. shiller (2008) argues that strategic default is typically motivated by self-interest rather than true financial hardship, as homeowners with underwater mortgages may find it more advantageous to stop payments and face foreclosure rather than invest further in a declining asset. economic factors like the housing market, interest rates, and personal finances affect decision-making regarding mortgage defaults. shiller (2008) notes that societal attitudes towards defaulting have changed, with a diminished stigma prompting borrowers to consider this option more readily. as perceptions shift, individuals may feel less moral obligation to meet mortgage commitments, especially when facing financial challenges. strategic defaults not only impact borrowers but also challenge financial institutions, as lenders often underestimate the likelihood of such defaults, leading to unexpected losses and increased systemic risks in the housing market. empirical review microcredit schemes have been practised in many parts of the world to alleviate poverty. according to sharma (2000) and harvey and cristani (2022), many microcredit services in asia and africa target women on the assumption that empowering women and providing services to them leads to better allocation and use of household resources. microfinance, as defined by meki and quinn (2024), provides banking services to low-income individuals and the unemployed, aiming to integrate the unbanked into the financial system by offering access to credit and savings. van maanen (2004) asserts that microfinance helps those without adequate collateral. kagan (2024) describes village banking as offering small loans to low-income individuals who cannot access traditional credit. tria et al. (2022) contends that microfinance or microcredit often targets women facing financial barriers, while wakunuma et al. (2019) highlight that this limited access significantly excludes women from the financial system. additionally, inadequate financial literacy among women is a major challenge, with adera and abdisa (2023) contending that financial literacy initiatives are often underfunded or poorly executed, leaving many women unprepared to manage financial transactions. mchembe et al. (2023) highlight significant challenges women face in accessing financial resources, primarily due to cultural and social barriers that hinder their participation in microfinance. deep-rooted cultural norms often restrict women’s economic involvement and mobility, leading to resistance from family and community leaders when they seek financial services. these constraints, along with the requirement for collateral—which many women cannot meet due to a lack of asset ownership—further marginalize them economically. to address these issues, innovative solutions like group lending and characterbased lending are essential to enhance women’s access to financial services. many conventional microfinance models require collateral to secure loans, a stipulation that many women are unable to meet due to insufficient ownership of personal or real property. this condition greatly restricts their capacity to acquire loans and other financial products, consequently limiting their potential to invest in businesses or engage in income-generating activities (banerjee & jackson, 2017). niaz & iqbal (2019) studied the impact of microfinance on women’s empowerment and poverty alleviation in pakistan using robust methodologies like ordinary least squares (ols) and propensity score matching (psm) with a dataset of 670 participants. they developed a multidimensional poverty index (mpi) to assess poverty dimensions. the findings showed that microfinance significantly enhances women’s empowerment, reduces poverty, and improves social status through increased income. the study concluded that microfinance and microfinance institutions (mfis) effectively promote sustainable development goals (sdgs) in pakistan. pare (2021) conducted a study investigating the effectiveness of microfinance in empowering women, specifically analyzing the correlation between women’s autonomy and microfinance initiatives. this research provided valuable insights into both the potential benefits and drawbacks of microfinance. utilizing ordinary least squares (ols) regression analysis, the study examined data from 45 developing countries, drawing on information from the world bank and the united nations. to ensure the robustness of the findings, women’s autonomy was assessed through multiple indicators, including pa ge 15 7 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 154-166, 2024 rates of female secondary school enrolment, women’s involvement in decision-making processes, and statistics on female unemployment and labour force participation. the results revealed that microfinance has negligible positive effects on women’s autonomy. evidently, the only significant factor associated with microfinance was the proportion of female borrowers, which influenced both female secondary school enrolment and the poverty headcount ratio. islam et al. (2020) examined the impact of financial inclusion on female entrepreneurs in smes in bangladesh, involving 207 women. data was collected via a structured questionnaire using a 5-point likert scale and analyzed with multivariate techniques. key findings identified factors influencing financial inclusion, such as accessible payment methods, simple transactions, job creation through agent roles, minimal network challenges, and broad geographical coverage, all significantly affecting female sme entrepreneurs. frisancho and valdivia (2020) investigated the effects of implementing savings groups on poverty alleviation, vulnerability reduction, and financial inclusion in rural peru. employing a cluster randomized control trial and utilizing both survey data and administrative records, the research assessed the impact of savings groups over a two-year period. the results demonstrated that savings groups enable households to make significant investments, such as improvements to housing, while also mitigating their vulnerability to specific shocks, particularly in economically disadvantaged districts. maganga (2021) aimed to explore the impact of village savings and loan associations (vslas) on the socioeconomic development of women and their resilience to vulnerabilities in malawi. the study utilized a descriptive research design, collecting data through household surveys in two districts: chiradzulu traditional authority maoni and blantyre rural traditional authority kapeni. a multi-stage sampling method was employed to identify a sample of 70 women from vsla groups. the findings revealed that vslas positively influence the economic and social status of women. however, the research also pointed out that vsla members encounter difficulties in obtaining loans from external financial institutions and face a significant lack of training opportunities. adai and rashid (2023) conducted a study titled “a study on sociocultural features among turkish and iraqi women,” published in the journal of current research on social sciences. the research explores the sociocultural similarities and differences between women in turkey and iraq, focusing on gender roles, family dynamics, traditional customs, and modern influences. key findings indicate that while both groups face gender inequality, their experiences differ due to varying political, economic, and historical contexts. turkish women generally have better access to education and job opportunities, whereas iraqi women face more limitations due to sociopolitical unrest and cultural conservatism. traditional gender roles persist in both societies, impacting women’s public participation and financial inclusion. the study underscores the importance of understanding these sociocultural factors to promote gender equality and improve women’s financial inclusion. the authors call for enhanced policy initiatives and cross-cultural dialogue to empower women and address cultural barriers. mbiakop and oyekale (2017) highlight that access to credit is crucial for enhancing agricultural productivity and reducing poverty among smallholder farmers. this study investigated the impact of village bank membership on the welfare of smallholder farmers in the ngaka modiri molema district municipality (nmmdm). using crosssectional data from three villages with active village banks, a sample of 200 farmers was surveyed through structured questionnaires. data analysis employed descriptive statistics and a simultaneous equation model (sem). findings indicated that village bank membership led to an 83.85% increase in per capita expenditure, significantly influenced by income per capita and technology use. the study concluded that village bank members exhibited improved socio-economic characteristics, suggesting that establishing more community-based village banks could greatly enhance welfare in south africa. fula (2023) examined the effects of village banking on women’s economic well-being in lusaka district through a qualitative survey of 21 women—12 village banking members and 9 non-members. descriptive statistics revealed that participation in village banking significantly improved women’s livelihoods, leading to increased business revenue, better access to credit, and enhanced social connections. additionally, members reported boosts in self-esteem and decision-making skills, positively impacting their entrepreneurial activities. lwengo (2021) conducted a study on the effects of village banking on the financial inclusion of women marketers at the main masala market in ndola, zambia. the research aimed to identify the socioeconomic challenges faced by women marketeers (small-scale women traders), evaluate village banking’s potential to reduce poverty, and assess its benefits. employing a qualitative methodology, data was gathered from 80 women marketers and five stakeholders through questionnaires and interviews, which were analyzed using descriptive statistics. the findings underscored the necessity for stricter regulations to address loan defaults, suggesting police involvement to ensure compliance. the study concluded that effective village banking can empower women economically and improve financial inclusion for those without banking services. banda et al. (2022) studied the impact of village banking on the financial well-being of women in the formal sector in lusaka’s matero zone, zambia. the research aimed to assess the benefits of village banking and its role in enhancing savings. using a descriptive multiple-case study approach, the study focused on two village banking groups with a purposive sample of 25 participants. data was collected through telephone interviews with 14 participants and a focus group discussion with 11 pa ge 15 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 154-166, 2024 participants during a monthly meeting. thematic analysis revealed that village banking significantly improved the financial status of these women, enabling them to start businesses, access affordable loans, build homes, pay for education, and purchase household items. it also encouraged savings, as women anticipated sharing investment returns after the project cycle. while numerous studies have explored the impact of microfinance and village banking on women’s empowerment and financial inclusion globally, there is limited research specifically focusing on zambia. extant literature, including works by niaz and iqbal (2019) in pakistan and pare (2021) across various developing nations, fails to address the unique context of zambia, where socio-economic and cultural dynamics may vary considerably. while numerous studies highlight the beneficial effects of microfinance and village banking, such as enhanced income and greater financial autonomy (islam et al., 2020; mengstie, 2022), there remains a notable gap in the thorough examination of the socioeconomic obstacles encountered by women involved in village banking groups in zambia. mwaka (2020) briefly acknowledged this issue in the katete district but did not explore the in-depth specific challenges faced by women marketers in other areas, including the ndola district. a significant portion of current research utilizes quantitative methodologies (niaz & iqbal, 2019; islam et al., 2020). although these approaches are rigorous, they often overlook the intricate, qualitative dimensions of women’s experiences and perceptions. banda et al. (2022) initiated a shift towards this area by conducting qualitative interviews; however, there remains a significant need for more extensive and detailed qualitative investigations to fully understand the lived experiences of women participating in village banking in zambia. the beneficial effects of village banking on financial inclusion and poverty reduction have been acknowledged in the literature (frisancho & valdivia, 2020; mbiakop & oyekale, 2017). however, there remains a significant gap in empirical research regarding the nexus or link between village banking models and women’s financial inclusion within the zambian context, combining both qualitative and quantitative methods. material and methods conceptual framework figure 1 shows how the variables interact in this study . financial inclusion refers to the availability and use of financial services by individuals and businesses, particularly in underserved communities (world bank, 2023). it ensures access to affordable financial products like savings accounts, loans, insurance, and payment systems, which are vital for effective financial management and risk mitigation. access to credit is a key component, enabling individuals and businesses to secure loans from financial institutions, thereby supporting goals such as starting or expanding a business, buying a home, or handling unexpected expenses (tomaselli et al., 2013). savings denote to the portion of income that is not spent on immediate consumption of goods and services but is instead set aside or stored for future use (aidoo-mensah, 2018). in the context of financial inclusion for women, savings are a relevant factor as they reflect women’s ability to accumulate financial resources and build assets over time. a higher savings indicates a greater capacity for women to save and invest in their future, which can contribute to their financial stability and well-being (adera & abdisa, 2023). social capital refers to the networks, relationships, and norms of reciprocity and trust that exist within a community or group (kenton, 2022; oyinloye, 2024). in the context of financial inclusion for women, social capital plays a crucial role in facilitating access to financial services and resources. women who are part of strong social networks are more likely to have access to information about financial services, receive support figure 1: conceptual framework source: author’s elaboration pa ge 15 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 154-166, 2024 from others in accessing credit or saving, and benefit from collective action and advocacy efforts (ikhar et al., 2022). research design, data collection and data analysis this research employed a case study methodology, focusing on the members of the afri-zam cooperative located in lusaka, zambia. a case study methodology is characterized by a thorough and detailed investigation of a singular case or a limited number of cases within their authentic context (priya, 2021). the selection of the case study methodology is justified by its capacity to yield a profound, contextualized, and nuanced insight into the effects of village banking on the livelihoods of women in lusaka, rendering it a suitable research strategy for this investigation. the target population for this study was all members of the afri-zam cooperative in lusaka, zambia, comprising 150 members. in this study, the sample size for the quantitative analyses was determined using the yamane’s (1967) formula: where: n = 150 e = 0.05 n = 109 at 95% confidence interval. therefore, the sample size that was used was 109. in this study, purposive sampling was employed. this method, also referred to as judgmental or selective sampling, is a non-probability sampling technique wherein the researcher intentionally selects participants based on characteristics, knowledge, or expertise that are relevant to the research focus (campbell et al., 2020). by utilizing this sampling strategy, the study aimed to ensure that the sample is representative of the population. the qualitative analysis was guided by the principle of saturation, which refers to the stage at which the collection of additional data ceases to yield new insights or themes pertinent to the investigation. both primary and secondary data were used. primary data were collected through a survey of selected respondents. secondary data was collected through document review of relevant documents. the econometric model the study adopted a cross-sectional design, capturing a snapshot of financial inclusion at a single point in time. econometrically, the overall regression model is specified as shown in equation 1: fin_inclusiont= β0+ β1access_creditt + β2savingst + β_3 social_capitalt + β4 φt+ β5δt + εt (1) where; fin_inclusiont is financial inclusion at time t, access_creditt is access to the credit at time t, savingst refers to the savings by the participants, social_capitalt is social capital, at time t. the parameter β0 represent the different intercepts for the independent (predictor) variables, β1 β5 represent the coefficient estimates for three dependent variables (financial inclusion, access to credit and savings) and the two control variables, marital status and gender represented by the parameters φt and δt respectively. finally, the εt denotes the error term or the impact on the model of estimates of the unobserved variables. the regression models were estimated separately as model 1, model 2, model 3 and model 4. the regression models are estimated separately, and variables were progressively added. regressions in model 1 only included control variables. in in model 2, saving as an independent variable was added to model 1. in model 3, access to credit as a predictor variable was added to model 3. finally, in model 4, all the independent or predictor variables along with the control variables were all regressed on the financial inclusion simultaneously. results and discussions quantitative analysis demographic profile table 1 shows the demographic profile of the people in the sample of this study. table 1: demographic profile variables description frequency per cent gender male 28 25.9 female 80 74.1 total 108 100 age 18-24 years 6 5.6 25-34 years 53 49.1 35-44 years 41 38 45-55 years 8 7.4 total 108 100 marital status single 55 50.9 married 41 38 divorced 11 10.2 widowed 1 0.9 total 108 100 pa ge 16 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 154-166, 2024 educational level less than high school 2 1.9 high school graduate 11 10.2 certificate 20 18.5 bachelor's degree 63 58.3 masters 12 11.1 total 108 100 occupation employed (full-time) 5 4.6 employed (part-time) 45 41.7 unemployed 46 42.6 student 11 10.2 retired 1 0.9 total 108 100 the investigation into village banking’s impact on women’s financial inclusion revealed that 80 participants (74.1%) were female, while 28 (25.9%) were male. the age distribution showed a majority of younger adults, with 49.1% (53 individuals) aged 25-34 and 38% (41 individuals) aged 35-44. young adults aged 18-24 made up 5.6% (6 individuals), and those aged 45-55 were the least represented at 7.4% (8 individuals). majority participants were single (50.9%, 55 individuals), followed by married (38%, 41 individuals), divorced (10.2%, 11 individuals), and widowed (0.9%, 1 individual). in terms of education, the largest group held degrees (58.3%, 63 individuals), followed by certificate holders (18.5%, 20 individuals), master’s degree holders (11.1%, 12 individuals), and those with less than a high school education (1.9%, 2 individuals). regarding employment, 42.6% (46 individuals) were unemployed, 41.7% (45 individuals) worked part-time, 10.2% (11 individuals) were students, 4.6% (5 individuals) were full-time employees, and 0.9% (1 individual) were retired. correlational analysis table 2 reports the correlation matrix. it provides insights into the relationships between financial inclusion and the independent variables (savings, access to credit, and social capital) while considering gender and marital status as control variables. table 2: correlation matrix mean standard deviation 1 2 3 4 5 6 1. financial inclusion 4.288 0.56 -0.008 2. gender 1.74 0.44 0.043 3. marital status 1.61 0.71 0.614 -0.21* 4. savings 4.33 0.56 0.64** 0.133 -0.132 5. access to credit 4.36 0.49 0.69** 0.143 -0.21* 0.65** 6. social capital 4.41 0.53 0.53** -0.086 -0.071 0.511** 0.61** note: * and ** denote that the correlation is statistically significant at 0.05 and 0.01, respectively (2-tailed). in terms of the correlation of the control variables, gender has a very weak and non-significant negative correlation with financial inclusion (r = -0.008). marital status shows a weak positive correlation with financial inclusion (r = 0.043), which is not statistically significant. the correlation between savings and financial inclusion is positive and significant (r=0.614**, p<0.01). this suggests that higher savings are associated with greater financial inclusion. participants who have higher levels of savings are more likely to experience enhanced financial inclusion, indicating that savings play a crucial role in accessing financial services and opportunities. access to credit also shows a strong positive correlation with financial inclusion (r=0.640, p<0.01). this indicates that individuals with better access to credit are more financially included. this relationship highlights the importance of credit availability in enabling individuals to participate more fully in the financial system, likely by providing resources for investment and economic activities. social capital has the highest positive correlation with financial inclusion (r = 0.693, p < 0.01). this significant relationship underscores the critical role of social networks and community connections in promoting financial inclusion. strong social capital can facilitate trust, information sharing, and collective action, which are essential for accessing and utilizing financial services. regression analysis a series of regression models were constructed to examine various predictors, including gender, age group, savings, access to credit, and social capital. the models pa ge 16 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 154-166, 2024 are progressively added, with each additional predictor contributing to the explanatory power of the regression. table 3 reports the overall regression outcomes of the four (4) models. table 3: additive regression outcomes control variables model 1 model 2 model 3 model 4 vif beta se beta se beta se beta se 1.112 gender marital status savings access to credit social capital 0.001 0.126 -0.088 0.099 -0.102* 0.089 -0.009* 0.082 1.083 0.034 0.078 0.088 0.063 0.13*** 0.056 0.124** 0.05 1.829 0.633*** 0.077 0.35*** 0.090 0.255** 0.082 2.212 0.103 0.281*** 0.102 1.731 0.443* 0.085 f 0.096 22.82*** 27.248*** 2.704*** f change 0.096 68.15 24.833 27.008 r 0.043 0.63 0.717 0.785 r2 0.002 0.39 0.514 0.616 adj r2 -0.017 0.37 0.117 0.597 r2 change 0.002 0.36 0.102 notes: vif denotes variance inflation factor, *, ** and *** denote statistically significance at 5%, 1% & 0.1% respectively financial inclusion was taken as the dependent variable across the four models. the regression examined the impact of savings, access to credit, and social capital on financial inclusion, with gender and marital status as control variables. regressions in model 1 regressed only control variables. the beta coefficient for gender is 0.001, which is positive but not significant, indicating that gender alone does not significantly influence financial inclusion. the beta coefficient for marital status is 0.034, which is positive but also not statistically significant. this suggests that marital status alone does not significantly impact financial inclusion. in model 2, saving was added as an independent variable. the beta coefficient is positive (0.633) and significant, indicating that savings have a significant positive impact on financial inclusion. this suggests that increasing savings by 1% causes a positive increase in financial inclusion by 63.3%, holding other factors constant. however, gender, statistically significant at the 5% level, has a negative impact on financial inclusion. in model 3, adding access to credit yielded a significant positive beta coefficient of 0.512, showing a significant positive effect on financial inclusion. this implies that a 1% increase in access to credit is likely to cause a positive increase in financial inclusion among women of 51.2% ceteris paribus. in model 4, the study regressed all the independent and control variables on financial inclusion. for every 1% increase in gender differences, financial inclusion significantly reduces by 0.9% ceteris paribus. however, marital status, savings, access to credit and social capital indicate that holding other variables constant, a 1% increase in each variable significantly increases financial inclusion by 12.4%, 25.5%, 28.1% and 44.3%, respectively. in model 2, saving was added as an independent variable. the beta coefficient is positive (0.633) and significant, indicating that savings have a significant positive impact on financial inclusion. this suggests that increasing savings by 1% causes a positive increase in financial inclusion by 63.3%, holding other factors constant. however, gender, statistically significant at the 5% level, has a negative impact on financial inclusion. in model 3, adding access to credit yielded a significant positive beta coefficient of 0.512, showing a significant positive effect on financial inclusion. this implies that a 1% increase in access to credit is likely to cause a positive increase in financial inclusion among women of 51.2% ceteris paribus. in model 4, the study regressed all the independent and control variables on financial inclusion. for every 1% increase in gender differences, financial inclusion significantly reduces by 0.9% ceteris paribus. however, marital status, savings, access to credit and social capital indicate that holding other variables constant, a 1% increase in each variable significantly increases financial inclusion by 12.4%, 25.5%, 28.1% and 44.3%, respectively. in summary, the regression analysis shows that savings, access to credit, and social capital all significantly enhance the financial inclusion of women, with social capital having the strongest effect. gender difference seems to have a negative effect on women’s financial inclusion. table 4 reports a model summary of regressions. this is basically the analysis into the overall model fit. the model regression summary indicates the progressive improvement in the predictive power of the model as additional variables are included. starting with an r-squared of 0.002 in the initial model with marital status as a control variable, the r-square steadily increases with each subsequent model iteration, reaching 0.607 in the final model incorporating marital status, gender, savings, access to credit and social capital. table 5 reports a summary of the analysis of variance (anova). it is observed from table 5 that the f-statistic pa ge 16 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 154-166, 2024 is not significant in the first model. in model 2, 3 and 4 (p < 0.001) indicate that the models collectively explain a significant portion of the variance in financial inclusion. as predictors are added, the f-statistic and significance levels consistently improve, suggesting that the expanded models better capture the variability in financial inclusion. table 4: regression-model summary model r r2 adjusted r2 standard error of the estimate change statistics r2 change f change df1 df2 sig. f change 1 .043a .002 .017 .56202 0.002 0.096 2 105 .908 2 .063b .397 .380 .43892 0.395 68.135 1 104 .000 3 .717c .514 .495 .3959 0.112 24.833 1 103 .000 4 .785d .616 .597 .35375 0.102 24.008 1 102 .000 source: authors’ elaboration a. predictors: (constant), marital status, gender b. predictors: (constant), marital status, gender, savings c. predictors: (constant), marital status, gender, savings, access to credit d. predictors: (constant), marital status, gender, savings, access to credit, social capital table 5: summary of the analysis of variance (anova) model sum of squares df mean square f sig. 1 regression .061 2 .030 .096 .908b residual 33.166 105 .316 total 33.227 107 2 regression 13.191 3 4.397 22.823 .000c residual 20.036 104 .193 total 33.227 107 3 regression 17.083 4 4.271 27.248 .000d residual 16.144 103 .157 total 33.227 107 4 regression 20.463 5 4.093 32.704 .000e residual 12.764 102 .125 total 33.227 107 source: authors elaboration on data testing the main hypothesis of the study the study set out to test the composite hypothesis that savings, access to credit, and social capital have a significant negative effect on women’s financial inclusion in zambia as the null hypothesis. the findings from the regression analyses indicate that savings, access to credit and social capital indicate that, ceteris paribus, a 1% increase in each variable, significantly increases financial inclusion by 25.5%, 28.1% and 44.3%, respectively. the study, therefore, rejects the null and accepts the alternative because savings, access to credit, and social capital have significant positive effects on the financial inclusion of women in zambia. qualitative analysis under this section, data collected from the 10 participants using the interview guide was presented and interpreted into the specific objectives, which were; to analyze the factors that tend to promote financial inclusion for the women in micro-credit co-operative models such as the afri-zam co-operative, to assess the level of financial inclusion for women in zambia to investigate the challenges that hinder financial inclusion for women at the afri-zam co-operative. under each objective, a theme was developed. perceived benefits of financial inclusion initiatives on women’s empowerment table 6 presents a thematic analysis of the perceived benefits of financial inclusion initiatives on women’s empowerment and financial independence in the afrizam co-operative. the analysis revealed five main themes, as reported in table 7. access to financial services (b1) eight (8) participants reported that access to loans and savings accounts significantly affected their ability to expand businesses and support their families. participant 3 highlighted that financial inclusion initiatives provided essential resources for business growth, thereby contributing to economic stability and empowerment. pa ge 16 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 154-166, 2024 table 6: thematic analysis of the perceived benefits of financial inclusive strategies theme code representative statement/quote access to financial services b1 “access to loans and savings has enabled me to expand my business and support my family." frequency score: 80% (8/10) increased financial security b2 “having a savings account gives me a sense of financial security and stability." frequency score: 60% (6/10) enhanced financial literacy b3 “the training sessions have improved my understanding of financial management and budgeting." frequency score: 50% (5/10) economic empowerment b4 "with the financial support, i have been able to start a small business and generate income." frequency score: 90% (9) improved standard of living b5 "my family’s standard of living has improved significantly due to the financial resources provided by the cooperative." 80% source: authors’ elaboration increased financial security (b2) six (6) participants, or 60% of the sample size, emphasized the sense of financial security and stability gained from having a savings account. this theme underscored the importance of financial inclusion in fostering a secure financial environment for women, enabling them to better manage their resources and plan for the future. enhanced financial literacy (b3) training sessions conducted by the cooperative played a mild role in enhancing participants’ financial literacy as only five (5), representing 50% of the sample, participants noted an improved understanding of financial management and budgeting, which contributed to better financial decision-making and planning. economic empowerment (b4) financial support from the cooperative empowered participants economically by enabling them to start or expand small businesses. 90% of the participants reported that such initiatives were instrumental in generating income and fostering economic independence. improved standard of living (b5) the financial resources provided by the cooperative significantly improved participants’ standard of living. eight participants, or 80%, reported that their family’s quality of life had improved due to the financial stability and opportunities created through the cooperatives’ support. challenges hindering financial inclusion the analysis revealed several themes regarding the challenges faced by women in accessing financial services. table 7 presents a thematic analysis of the main challenges hindering financial inclusion for women at the afri-zam co-operative. lengthy loan application process (c1) up to 80% of the participants (8/10) reported that the loan application process was often lengthy, causing delays in accessing the necessary funds. participant 1 mentioned that the extended application periods hindered timely financial support. table 7: challenges hindering financial inclusion theme code representative statement/quote lengthy loan application process c1 "sometimes, the loan application process is lengthy." frequency score: 80% (8/10) delays in loan disbursement c2 "there are occasional delays in loan disbursement." frequency score: 60% (6/10) limited availability of financial products c3 “limited availability of financial products.” frequency score: 80% (8/10) high demand for loans causes wait times c4 "high demand for loans causes long waiting periods." frequency score: 90% (9/10) difficulty understanding financial terms c5 "difficulty in understanding some financial terms and conditions." frequency score: 60% (6/10) delays in loan disbursement (c2) participants (60%) reported occasional delays as a significant challenge. such delays affected participants’ ability to use the funds effectively for their intended purposes. limited availability of financial products (c3) eight participants, or 80% of the sample, expressed concerns about the limited availability of financial products. these participants reported that the cooperative needed to offer a wider range of financial products to meet diverse needs. pa ge 16 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 154-166, 2024 high demand for loans causing wait times (c4) up to 90% of the participants (9/10) reported that the high demand for loans resulted in waiting periods delaying access to financial resources. this theme underscored the need for the afri-zam cooperative to manage loan demand more efficiently. table 8: strategies for overcoming barriers theme code representative quote financial education and awareness s1 "providing more financial education and awareness programs." frequency score: 70% (7/10) family support for women's involvement s2 "encouraging family support for women’s financial involvement." frequency score: 60% (6/10) increased outreach and information dissemination s3 "increasing outreach and information dissemination." frequency score: 70% (7/10) training in financial management s4 "offering more training on financial management." 90% (9/10) inclusive and supportive environments s5 "creating more inclusive and supportive environments." 100% (10/10) source: authors’ elaboration suggested strategies for overcoming barriers to financial inclusion for women table 8 reports the suggested strategies by participants for overcoming barriers to financial inclusion for women at the afri-zam co-operative. conclusion this study explores the link between the “village” (microcredit) banking model and women’s financial inclusion in zambia, using the afrizam cooperative as a case study. it employs multiple regression and correlational analyses for quantitative data alongside thematic analysis for qualitative insights. the findings reveal that cooperative models significantly enhance women’s financial inclusion, with key factors like savings, credit access, and social capital contributing to a 61.6% explanatory power in the regression model. specifically, a 1% increase in these variables correlates with increases in financial inclusion of 25.5%, 28.1%, and 44.3%, respectively. additionally, gender disparities negatively affect women’s financial inclusion, supporting previous research by frisancho & valdivia (2020) on the positive impact of savings and credit access in rural peru. the study identified challenges to women’s financial inclusion at afri-zam, particularly administrative inefficiencies and lack of policy support, aligning with the broader literature on microfinance limitations (pare, 2021). qualitative data underscored the importance of capacity-building and policy advocacy in promoting inclusive financial practices for women. similar issues were noted in village savings and loan associations in zimbabwe and malawi (mbiro & ndlovu, 2021; maganga, 2021), highlighting the need for regulatory reforms and community solutions. policy recommendations include enhancing savings initiatives, improving women’s credit access, streamlining bureaucratic processes, and continuing financial literacy campaigns. the study’s context in zambia may not reflect the diverse experiences of women in other regions or countries. the study recommends that future studies conduct longitudinal studies that will employ diverse sampling strategies beyond convenience sampling and undertake comparative studies. acknowledgements the authors wish to express their sincere gratitude to the anonymous reviewers for their constructive criticisms of the earlier versions of this paper. their criticisms significantly improved the quality of this research article. all errors and omissions are attributed to the authors, not the publisher. references adai, s. m., & rashid, b. n. 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(2019). policy brief no. 30. zipar lusaka, zambia. pa ge 1 pa ge 12 american journal of applied statistics and economics (ajase) determinants of share price of dhaka stock exchange jumman sani1*, md. mostafa kamal1, tapan kumar biswas1, tanjina shahid1, farah tiyaba tabassum1, fouzia hossain1 volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.3715 https://journals.e-palli.com/home/index.php/ajase article information abstract received: august 25, 2024 accepted: october 01, 2024 published: february 08, 2025 this study investigates at the main factors influencing share prices on the dhaka stock exchange (dse), one of the two main stock exchanges in bangladesh. stock markets provide as venues for businesses to generate cash through the issuance of securities, which investors’ then trade. primary measures of investor sentiment and stock prices are extremely volatile and impacted by a number of qualitative (goodwill, industry circumstances, and market hype) and quantitative (earnings per share, roa, and net asset value) variables. the present research inspects 206 firms that are listed on the dse and investigates the influence of distinct quantitative elements on share prices, including liquidity, eps, roa, nav, business size, and cash dividend rate (cdr). the results indicate that these variables explain 56% of the volatility in share price. notably, share prices are negatively impacted by roa and liquidity, while cdr has the most favorable impact. additionally, the analysis shows that there is considerable multi-co linearity among the variables, indicating that removing variables with higher p-values enhances the accuracy of the model. the outcomes of the research provide investors with insightful information that helps them make better decisions by demonstrating how important financial parameters impact share prices. furthermore, the results lay the groundwork for future studies that examine other variables affecting dse share price swings. the study emphasizes the difficulty of anticipating share prices because of both erratic market dynamics and controlled financial considerations. keywords cdr, dhaka stock exchange and bangladesh, eps, nav, roa, share price 1 faculty of business administration, university of development alternative (uoda), dhaka, bangladesh * corresponding author’s e-mail: jumman.sani@gmail.com introduction the ownership certificate of any company is termed as stock. stock exchange is a marketplace where financial securities are issued by the companies for buying and selling. both shares and bonds are included in securities that are issued by the companies to gain desired level of capital from the market. and it also allows the investors to choose for the best alternatives to invest to get maximum benefit by taking the best calculative decision. as securities can be easily bought and sold it provides higher liquidity and also gives confidence to the investors to trade. stock exchange provides a overall scenario of the industries by the price index. higher price indicates more growing and lower price indicates less growing or drowning. stock exchange helps the companies to raise their capital by attracting more investment. higher price of stock can ensure more capital reserves for companies. stock exchange plays a major role to create entrepreneur. dse stands as dhaka stock exchange which is one of the two stock exchanges of bangladesh. it lists companies, provides the screen based automated trading for listed securities; it settles the transactions, publishes monthly reviews, monitors the activities of listed companies and protects funds by setting required rules and regulations. the stock price is the only indicator that represents the present condition of the investor’s investment. it is very sensitive in nature as there are many factors that influence its price. there are some qualitative factors like the goodwill of the company, analyst’s reports, industries condition, hype etc and there are many quantitative factors like earnings per share (eps), cash dividend (cd), return on assets (roa), net asset value (nav), inflation rate, bank rate, size of the company etc. in this paper liquidity, earnings per share (eps), return on assets (roa), net asset value per share (nav), size of the company, cash dividend rate (cdr) are considered as the determinants of the price of the share of a company. the impact of each factor on share price is also discussed with results. rationale of the study the share market is the most unpredictable market as the nature of the price of the share is so sensitive and changes frequently due to various factors. studies suggest that both qualitative and quantitative factors are responsible for the change in share price. in this study, some independent variables like return on assets, earnings per share, size of the company, net asset value of the company per share and cash dividend rate are considered and selected and their impact on current share price is determined. these results will be helpful for the investor as they will have an idea regarding the impact of the selected variables on share price and invest more wisely in future considering the results. again this study can help the researcher to continue the research regarding the determinants of share price in future with more variables. objectives of the study the following objectives are to be fulfilled by this study: 1. to find out the impact of independent variables like roa, nav, eps, size of the company and rate of cash dividend paid on share price. pa ge 13 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 12-19, 2025 2. to find out the impact of the independent variables on share price individually. literature review several studies have already been conducted regarding the determinants of share price in stock market. some studies showed the effect of internal factors on share price and some stated about the effect of external factors on share price. the stock market has become an essential market playing a vital role in economic prosperity that fosters capital formation and sustains economic growth. stock markets are more than a place to trade securities. they operate as a facilitator between savers and users of capital by means of pooling of funds, sharing risks and transferring fund (mondal & imran, 2010). the expectation of future real dividend growth is a primary determinant of stock price movements (balk & wohan, 2006). in all share index is more responsive to change in exchange rate, inflation rate, money supply and real output (maku & atanda, 2007). both quantitative and qualitative factors influence share price. quantitative factors are dividend, market capital, price-earnings ratio, eps, net income, return on investment, rational earnings, merger, stick split, margin loan, demand and supply of stock, inflation, interest rate, exchange rate etc. qualitative factors are company goodwill, market segments, company announcement, annual general meeting, analyst reports, hype, international situation, internal political situation, government policies etc (hasan & mondal, 2008). previous behavior of stock prices, company sizes, previous earnings per share are the most important factors of current share price. additionally, macroeconomic indicators like gdp growth, rate of interest and financial depth have significant relationship with stock price (nisa & nishat, 2011). the stock price movement is determined by the ratios like asset turnover ratio, debt ratio, current ratio, net profit margin, price-to-earnings ratio and book value (ugun ergun, 2012). the relationship between dividends and stock prices after controlling the variables like eps, return on equity. retention ratio have positive relation with stock prices while dividend yield and profit after tax have negative relation with share price (masum, 2014). both macro and micro variables can reliably price the stocks. specifically asset quality, management quality, earnings, size, money supply and interest rate are significantly related to stock price (rjoub et al., 2017). remittance and money supply positively affect the stock market where as interest rate and exchange rate negatively affects the stock market performance (rakhal, 2018). the major factors influencing stock market development in line with fdi, economic growth, infrastructural development, savings, inflation, trade openness, exchange rates and stock market liquidity (tsaurai, 2018). the positive relationship lies among stick price volatility and dividend payout ratio, assets growth and size. and a negative relation among stick price and earnings volatility leverage (arshad et al., 2019). the impact of some selected variables like dividend, price, earnings per share, dividend payout ratio and size on the movement of share price. (chowdhury et al., 2019). in this study, factors like liquidity, return on assets, net asset value, earnings per share, size of the business and cash dividend rate are selected as independent variables and their impact on changing the share price is examined and discussed. research gap though several researches had been conducted regarding the title determinants of stock price, most research have been done on small sample group but this study covers the information of 206 companies which is larger in scale. again, all other research work is done before 2019 whereas this paper includes the information of 2023 which updated compared with the previous studies. variables like return on assets (roa) and net asset value per share (nav) are considered independent variables that determine the share price in this paper which were ignored before. data collection & descriptive statistics data collection this study is a quantitative study and data used in this study is secondary in nature. total 206 companies are examined to collect the information that is required to conduct this study. annual reports of those 206 companies are already published and available online and offline. data are mainly collected from the website of dhaka stock exchange and from the annual reports of the sample organizations. descriptive statistics dependent variable share price (sp): in this study share price is taken as dependent variable. closing market price of per common share is taken here as share price. it is the last trading price of the share in the dhaka stock exchange. independent variables liquidity (l): liquidity is the ability of an organization to meet up it’s current liabilities. normally, it is expressed by the current ratio of each company. liquidity indicates the amount of current assets available to pay the current liabilities. return on assets (roa) when return is compared with the asset roa is obtained then. roa indicates the return generated in terms of assets. it expresses the percentage of how profitable a company’s assets are generating revenue. formula of roa is, roa = net income / total assets. net asset value (nav) net asset value expresses the realizable asset value after the deduction of it’s liabilities. it represents the net value pa ge 14 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 12-19, 2025 of an entity. it is calculated as the total value of the assets minus the total value of the liabilities. formula of nav is, nav = net asset value / number of shares outstanding earnings per share (eps) eps is the most common and popular indicator of company’s performance. when the earning is expressed in proportion to per share, it is called eps or earnings per share. earning per share indicates how much money is made for each share. it is an indicator of company’s profitability. formula of eps : table 1: high tendency of changing price share price liquidity roa nav per share eps size dividend rate mean 142.8140777 2.18315534 0.055579369 44.6661165 3.302538835 9.157262136 0.228473301 standard error 19.84179665 0.208816997 0.028457642 7.360174063 0.86943117 0.149050396 0.049104485 median 51.65 1.44 0.0235 28.855 1.375 9.515 0.1 mode 9.9 0 0 18.56 0.22 0 0.1 standard deviation 284.7833567 2.997087732 105.638371 12.47868483 2.139275637 0.704781942 sample variance 81101.56024 8.982534873 0.166826507 11159.46542 155.7175751 4.576500253 0.496717586 kurtosis 27.98868064 37.27142079 113.9122547 85.3076146 34.29382307 12.40120118 77.3261226 skewness 4.780958708 5.337846297 8.672089319 -6.910987275 3.471037174 -3.466258855 7.950804115 range 2416.3 27.77 7.02 1511.63 165.85 11.68 8 minimum 5.2 0 -1.96 -1167.57 -53.03 0 0 maximum 2421.5 27.77 5.06 344.06 112.82 11.68 8 sum 29419.7 449.73 11.44935 9201.22 680.323 1886.396 47.0655 count 206 206 206 206 206 206 206 eps = (net income – preferred dividend) / weighted average outstanding shares size (s): size interprets the total proportion of market share is captured by any company. it is expressed by the logarithm of net asset value. it indicates how much market share is captured by a company. cash dividend rate (cdr) the cash dividend rate is the rate by which the profit portion is distributed to share holder in form of cash. cash dividend rate indicates the amount of cash return to it’s stockholder on an annual basis. it is the dividend rate that is declared and paid by the companies. it is obtained from the table that the mean value of share price is 142.814 and standard deviation is 284.78 which shows that there is high tendency of changing price. the minimum value of share price is 5.2 and the maximum value is 2421.5. it’s mode is 9.9. the mean value of liquidity denotes as 2.18 and it’s standard deviation is 3.00. the range of its value is 0 to 27.77. the mode of liquidity is 0. the mean value and standard deviation of roa are 0.056 and 0.41 respectively. the minimum value of roa is -1.96 and the maximum value is 5.06. the mode value of return on assets or roa is 0. nav’s mean value is 44.67 and standard deviation is 105.63. the range of nav is -1167.57 to 344.06. the mode value of net asset value per share or nav is 18.56 eps has a mean value of 3.30 and standard deviation of 12.48. the minimum value of eps is -53.03 and the maximum value is 112.82. from the table it is shown that the mode of earnings per share is 0.22. the mean value of the size of the companies is 9.16 and standard deviation is 2.14 while the range of the size of the companies expressed as 0 to 11.68. the mode of size of the companies is 0. cash dividend rate has a mean value of 0.23 and standard deviation of 0.71 while it’s minimum value is 0 and maximum value is 8. mode of the cash dividend rate of the companies is exactly 0.01. materials and methods in this study descriptive statistics, correlation, economic model, regression are designed to show the relationship among the independent variables and their impact on the dependent variable which is share price. some selected independent variables like liquidity, return on assets, net asset value, earnings per share, size of the business and cash dividend rate are considered as the determinants of share price. to show the relationship among the variables and their impacts on share price descriptive statistics and correlation matrix are used. to understand the effect of each variable on share price an econometrics model is used in this study and with the help of data analysis tool of microsoft excel coefficients are determined. econometrics model the following model is used to find out the influence of liquidity, return on assets, net asset value, size of the company and cash dividend rate on share price of the pa ge 15 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 12-19, 2025 particular company. α1 = constant. it indicates the value of the share price per share in absence of liquidity, return on assets, net asset value, and earnings per share, size of the company and cash dividend rate. β1 = the partial change in share price due to the one percentage change in liquidity while other things remain constant. β2 = the partial change in share price due to the one percentage change in return on assets (roa) while other things remain constant. β3 = the partial change in share price due to the one percentage change in net asset value (nav) while other things remain constant. β4 = the partial change in share price due to the one percentage change in earnings per share (eps) while other things remain constant. β5 = the partial change in share price due to the one percentage change in size (s) while other things remain constant. β6 = the partial change in share price due to the one percentage change in cash dividend rate (cdr) while other things remain constant. μ1 = error table 2: correlation matrix share price liquidity roa nav per share eps size dividend rate share price 1 liquidity -0.077602565 1 roa 0.04193175 -0.038355729 1 nav per share 0.313655963 -0.017135035 0.057411343 1 eps 0.616150332 -0.024942244 0.129164084 0.493833012 1 size 0.060655544 0.096163557 0.066466266 0.350240737 0.196324749 1 dividend rate 0.733105264 -0.059979308 0.083753395 0.216638088 0.800290692 0.133439 1 this table shows the correlation matrix among the variables. according to that table there is both positive and negative correlation among the variables among them some are significant and some are not significant. the share price has positive correlation with roa, nav per share, eps, size and dividend rate. among them dividend rate has highest correlation with share price as of 0.73 which is significant as well. eps is another factor which has significant correlation with share price exactly of 0.61. on the other hand with liquidity share price has negative correlation which is of -0.08. liquidity has only positive correlation with size of the organization which is 0.096. it has negative correlation with roa, nav and eps among them the height correlation is with size of the company of 0.10. roa has positive correlation with nav, eps and size and dividend rate. highest correlation is with eps of 0.13. nav has positive correlation with eps, size and cash dividend rate. among them the highest one is with eps of 0.49. eps has positive correlation with size and dividend rate. it has highest correlation with dividend rate of 0.80 which is also significant in nature. size has positive correlation with dividend rate and it is of 0.13. results and discussion empirical results with the help of data analysis tool of microsoft excel from the appendix 4 the following model can be extracted that we designed earlier: summary output table 3: regression statistics multiple r 0.758177466 r square 0.57483307 adjusted r square 0.562013966 standard error 188.4710872 observations 206 table 4: anova df ss ms f significance f regression 6 9557071.059 1592845.177 44.84190902 1.88254e-34 residual 199 7068748.79 35521.3507 total 205 16625819.85 pa ge 16 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 12-19, 2025 table 5: roa r coefficients standard error t stat p-value lower 95% upper 95% intercept 179.050 59.914 2.988 0.003 60.902 297.199 liquidity -1.971 4.439 -0.444 0.658 -10.725 6.783 roa -12.524 32.596 -0.384 0.701 -76.803 51.755 nav per share 0.615 0.160 3.831 0.000 0.298 0.931 eps -2.093 2.118 -0.988 0.324 -6.270 2.084 size -13.425 6.641 -2.021 0.045 -26.522 -0.328 dividend rate 311.472 33.376 9.332 0.000 245.657 377.288 sp = 179.05 – 1.97 liquidity – 12.52 roa + 0.62 nav – 2.09 eps – 13.43 size + 311.47 dividend rate here 170.05 denote the value of constant. it denotes that the share price will be 179.05 if liquidity, roa, nav, eps, size and cash dividend rate are absent in the market. -1.97(liquidity) indicates that if only liquidity stays in the market and other thing remain constant for 1 unit change in liquidity will result in 1.97 unit of share price in the opposite direction. that means if the liquidity will increase the share price will be decreased and if liquidity is decreased share price will be increased. -12.52(roa) explains that the roa presence in the market while other factors will remain constant will affect the share price. for each unit of roa the share price will be moved 12.52 in the opposite way. that means if roa increases for 1 unit the share price will be decreased for 12.52 units and vice versa. 0.62(nav) discloses that if other factors remain constant in the market except nav this will affect the share price. to be exact 1 unit of nav will influence the share price in the same direction for 0.62 units. if nav increases for 1 unit the share price will be increased by 0.62 units. again if nav decreases for 1 unit, the share price will be decreased by 0.62 unit. -2.09 (eps) demonstrates that if only eps stays in the market and other factors remain constant will have an effect on changing share price. share price will be effected by 2.09 units for the per unit change of eps in the opposite direction. if eps is increased by 1 unit this will result in 2.09 units decrease of share price. again if eps is decreased for 1 unit will result in increase of share price by 2.09 units. -13.43(size) interprets that the size of the organization negatively effects share price when other factors remain constant. for each unit of size of the organization increase will result in 13.43 units’ decrease of share price. if the size of the organization will be decreased by 1 unit, that will ensure the increase in share price by 13.43 units. 311.47(cdr) illustrates that the cash dividend rate is a significant factor to influence the share price when other factors constant. i unit of increase in cdr will assure 311.47 units of increase in the share price. similarly 1 unit decrease in cdr will cost 311.47 units decrease of share price. the value of r square is 0.57. again, the value of adjusted r square is 0.56. this indicates that the model explains 56% proportion of the share price. to be more exact it can be said that 56% of the share price can be demonstrated by the variables used in the model and the rest 44% can be influenced by the factors that are not considered in this model. anova table shows that the value of significance f = 1.88254e-34 ~ 0.000… that also provides the assurance of the model’s significance. summary output table 6: test of multi-co-linearity regression statistics multiple r 0.758177466 r square 0.57483307 adjusted r square 0.562013966 standard error 188.4710872 observations 206 anova df ss ms f significance f regression 6 9557071.059 1592845.177 44.84190902 1.88254e-34 residual 199 7068748.79 35521.3507 total 205 16625819.85 coefficients standard error t stat p-value lower 95% upper 95% vif r2 pa ge 17 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 12-19, 2025 intercept 179.0502836 59.91444623 2.988432587 0.003157252 60.90159921 297.1989679 liquidity -1.970817384 4.439223972 -0.443955384 0.657557203 -10.72477415 6.78313938 1.021591333 0.021135 roa -12.52400962 32.59632841 -0.384215347 0.701229082 -76.80255274 51.75453351 1.022979181 0.022463 nav per share 0.614512374 0.160418936 3.83067229 0.000171245 0.298173203 0.930851545 1.657449589 0.3966634 eps -2.092886387 2.118022755 -0.988132154 0.32428785 -6.26953522 2.083762445 4.031461526 0.751951 size -13.42505098 6.641447657 -2.02140432 0.044577334 -26.52169715 -0.328404814 1.164993348 0.141626 dividend rate 311.4724529 33.37566126 9.332323051 2.08425e-17 245.6570993 377.2878066 3.193255844 0.68684 variance inflation factor = 1/ ( 1r²) table 7: test of autocorrelation regression statistics multiple r 0.756134457 r square 0.571739318 adjusted r square 0.56537901 standard error 187.74568 observations 206 anova df ss ms f significance f regression 3 9505634.895 3168544.965 89.89177767 5.48197e-37 residual 202 7120184.955 35248.44037 total 205 16625819.85 coefficients standard error t stat p-value lower 95% upper 95% intercept 178.7749272 59.57335853 3.000920741 0.003030763 61.30952613 296.2403283 nav per share 0.530930594 0.134799543 3.93866762 0.000112791 0.265135904 0.796725284 size -13.61506297 6.557022528 -2.076409363 0.039121579 -26.5440517 -0.686074239 dividend rate 284.5025787 19.09583116 14.89867481 2.32765e-34 246.8498507 322.1553068 here the variables having higher p value are excluded from the model and the regression shows better results in terms of p value. after deduction of the variables the new model stands like this: sp = 178.77 – 1.97 liquidity + 0.53 nav – 13.61 size + 284.50 dividend rate. findings 1. the regression model’s r² score is 0.57, suggesting that 56% of the variance in share price is explained by these factors. 2. the dependent variable, share price, has a mean of 142.81 and a standard deviation of 284.79. it is extremely volatile in nature, ranging from 5.2 to 2421.5. 3. the analysis discovered that the cash dividend rate, size, profits per share, return on assets, liquidity, and net asset value were correlated negatively, and that the independent factors had negligible effects on share price. 4. share price is negatively impacted by liquidity and roa; for every 1% rise in both factors, the price drops by 1.97 and 12.52 units, respectively. 5. nav has a positive correlation with share price; for every 1% rise in nav, the price of the shares increases by 0.62 units. 6. size and eps have a negative impact on share price: size lowers share price by 13.43 units and eps by 2.09 units for every 1% rise in eps. 7. one important component is the cash dividend rate (cdr), which yields a 311.47 unit rise in share price for every 1% increase. 8. moderate multi-co linearity in the model is indicated by variance inflation factor (vif) values less than 5. 9. enhanced results are obtained using a modified model that eliminates variables with higher p-values. 10. share price is influenced by size, dividend rate, and net asset value, with dividend rate having the most positive impact, while liquidity, roa, and eps have minimal impact. recommendations following are some suggestions derived from the results to enhance stakeholders’ decision-making and share price predictability: 1. companies should emphasize maintaining or growing dividend distributions since they have a large positive impact on investor confidence and share price here the value of vif is less than 5. it indicates that there is moderate multi-co-linearity in the model. summary output pa ge 18 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 12-19, 2025 growth, especially when it comes to the cash dividend rate (cdr), which has the biggest positive impact on share price. 2. by better managing their assets and cutting down on obligations, companies should concentrate on raising their net asset value. 3. since increased liquidity seems to have a negative impact on share price, companies should carefully manage liquidity. 4. even while roa is usually viewed as a metric of revenue, companies should look into why increased roa causes a drop in share price and modify their plans appropriately, maybe by making better use of their assets. 5. companies should think about restructuring techniques to strike a balance between expansion and operational efficiency, as a larger company’s size has a negative influence on share price. 6. in an effort to match eps growth with investor expectations, management should look into the causes of the correlation between rising eps and falling share price. 7. to stabilize share price, investors and corporations must consider severe volatility ranging from 5.2 to 2421.5, using risk management measures like shareholder communication, hedging, and diversification. 8. companies should communicate their financial performance in a straightforward manner, emphasizing dividends, net asset value (nav), and roa and eps strategies that are in line with investor goals. by concentrating on these essential elements, companies may develop tactics that enhance the predictability of share prices while also better matching investor expectations and market realities. conclusion share market is the most unpredictable and sensitive market in bangladesh, which is controlled by so many micro and macroeconomic factors. among them, some are controllable and some are not. factors like bank rate, exchange rate, inflation rate, government policies, international business situation, market expectations, hype, goodwill of a company, analyst’s report, corporate social responsibility, corporate governance, companies internal policies, profitability, nature of business, gdp growth, economic growth, demand and supply situation etc can affect the share price in any moment. as there are so many variables, it is very difficult to predict the share price by previous actions, and also, it is not possible to predict it’s future value accurately by depending on mathematical calculations as there are some factors which are qualitative but has significant influence on the share price. the overall report expresses that the independent variables like liquidity, return on assets, net asset value per share, earnings per share, size of the organization and cash dividend rate can explain 56% of the share price. there is moderate multi-col-linearity among the variables as the variable influencing factor is less than 5% among the variables. autocorrelation suggested that if liquidity, roa and eps is excluded the rest variables gives significant p value. though it is difficult, this study attempts to predict the determinants of share price by selecting some independent quantitative variables and to some extent this study can influence the investors to make better decisions. as there are so many factors to be more examined and due to the changing nature of the share price, the determinants of share price of dsc is a topic to be more discussed and more examined. so, 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(2012). internal determinants of the stock price movements on sector basis. international research journal of finance and economics, 92, 111-117. pa ge 1 pa ge 1 american journal of applied statistics and economics (ajase) forecasting global wheat price in the context of changing climate and market dynamics: an application of sarima modeling technique sahil ojha1*, lila b. karki1 volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.4116 https://journals.e-palli.com/home/index.php/ajase article information abstract received: november 30, 2024 accepted: january 02, 2025 published: february 07, 2025 the global wheat market is influenced by several factors, such as climate change, inflation, market fluctuations, geopolitical situations, and government policies, leading to continuous fluctuations in wheat prices. there is a need for a robust forecasting model that accurately captures seasonal variations and trends in global wheat prices to support informed decisionmaking. the objective of this study was to forecast the monthly global price of wheat by adopting the sarima model using historical data from january 1990 to october 2024. the original monthly global wheat price data was log-transformed to stabilize the variance in the data and improve forecast precision. the seasonal variations in the data were adjusted by applying decomposition and differencing before modeling. using the ‘auto.arima’ function from the ‘forecast’ package in r 4.3.3 for windows, sarima(0,1,1)(0,0,1)12 was identified as the best-fitted model for forecasting global wheat prices. residual analysis validated the model’s accuracy by visualizing the acf and pacf plots and applying the ljung-box test, confirming that the residuals were white noise. the model forecasted a steady rise in the monthly global price of wheat from $198.51 per metric ton in november 2024 to $201.36 per metric ton in october 2025, peaking at $203.62 per metric ton in august 2025. these projections could help farmers, policymakers, and other relevant stakeholders to anticipate global price fluctuations and make informed decisions amidst global uncertainties. future research could integrate external factors, such as climate change and geopolitical events, for enhanced predictive accuracy. keywords informed decisions, price forecasting, sarima model, wheat 1 department of agriculture, food, & resource sciences, school of agricultural and natural sciences, university of maryland eastern shore (umes), princess anne, md 21853, usa * corresponding author’s e-mail: sojha@umes.edu introduction wheat is the staple food for billions of people worldwide and is considered one of the most important crops in the world. wheat belongs to the genus triticum and is primarily cultivated for its seeds, which are processed into flour and used in various food products, including bread, pasta, and pastries. triticum aestivum, commonly known as bread wheat, is the most widely grown species worldwide, while triticum durum is primarily used for pasta production. the domestication and use of wheat are closely linked with human efforts to ensure food security and gain control over their food supply. wheat is grown in all geographical regions due to its high yield potential and adaptability to a wide range of climates (geren, 2021). as of 2023-24, china, the european union, and india currently lead the global wheat production (usda, foreign agricultural service., n.d.). agricultural production has been considered risky since yields are affected by extraneous factors beyond the producers’ control, such as weather patterns, pest infestations, and disease outbreaks. furthermore, price volatility and market fluctuations during harvest are unknown when farmers make production decisions. greater price volatility makes it harder to predict future prices and creates uncertainty regarding future price expectations and the profitability of production (drugova et al., 2019). traditional forecasting methods fail to capture seasonal variations and trends in price, limiting their application in decision-making amidst dynamic market conditions. while several studies have focused on regional or national wheat price forecasting, there is a need to develop a forecasting model capable of capturing complexities in global wheat prices and providing reliable price predictions that could support the decision-making process. by leveraging historical price data and applying rigorous model selection criteria, this study aimed to forecast global wheat prices using the seasonal autoregressive integrated moving average (sarima) model to provide short-term price projections. this study will contribute to the existing literature on agricultural price forecasting by delivering valuable insights for stakeholders to make informed production decisions and manage risks associated with wheat price volatility. the findings are expected to have significant implications for global food security planning and formulation of agricultural policies. literature review global wheat market dynamics wheat is the second-largest grain produced worldwide in terms of cultivated area and production volume. in 202223, global wheat production reached just over 789 million metric tons, marking an increase of approximately nine million metric tons from the 2021-22 production level (figure 1). in 2023-24, china produced 136.59 million metric tons, the european union 134.94 million metric tons, and india 110.55 million metric tons, collectively accounting for 48% of global wheat production (usda, pa ge 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 1-11, 2025 foreign agricultural service., n.d.). in the united states, wheat is produced in almost every state, with north dakota and kansas leading the output. many developing nations heavily rely on wheat imports from ukraine and russia. as of 2022, armenia and mongolia imported wheat from russia. similarly, laos exhibited the highest dependence on wheat from ukraine, with 98% of its wheat coming from ukraine. the global wheat market has experienced significant growth over the past decade. since 2014-15, global wheat export volume has expanded by about 33.6%, reaching more than 216 million metric tons in 2022-23 (shahbandeh, 2024b). figure 1: global wheat production and export volume (in million metric tons) from 2014/15 to 2022/23 source: shahbandeh (2024a) and shahbandeh (2024b) impact of climate change on wheat production and price the effects of climate change, including rising global temperatures and continuous changes in precipitation, have impacted global food production (howard et al., 2016). several studies have examined the effects of climate change on agricultural production. rosenzweig and parry (1994) conducted a global assessment to study the impact of climate on world food supply, which concluded that doubling atmospheric carbon dioxide concentration would likely result in a modest decline in global crop production, with developing nations bearing the greater impacts. tol (2002) evaluated the broader impacts of climate change on agriculture as well as other sectors and found that a 1°c rise in global mean surface temperature may yield net positive effects for china, the middle east, and the organization for economic co-operation and development (oecd) member countries, but negatively impact other countries. dhakhwa and campbell (1998) analyzed the influence of fluctuations in the differential warming of day and night temperatures on crop yields. their findings suggested that potential crop damage may be less severe under asymmetric day-night warming than uniform warming. kang et al. (2009) conducted a thorough literature review on climate change impacts on crop yield, water productivity, and food and water security, concluding that climate change could increase water availability in some areas, enhancing water efficiency and crop production but potentially causing environmental degradation from expanded irrigation. they highlighted that the effects of climate change on crop yields vary by location, with some regions seeing an increase in output, while others may see a decrease. expanding irrigated farmland could increase crop production, though it might degrade food and environmental quality. tack et al. (2015) analyzed the effect of weather on kansas wheat yield using data from 1985 to 2013, finding that the main drivers of yield reduction were fall freezes and spring heat waves. lobell et al. (2011) reviewed climate trends and global crop production since 1980, noting declines in global maize and wheat production by 3.8% and 5.5%, respectively, relative to a scenario with no climate change. however, the united states was an exception in their findings. deschênes and greenstone (2011) developed a new approach to studying climate change impacts on the u.s. agricultural sector, leveraging annual variations in temperature and precipitation to assess effects on agricultural profits using county-level panel data. their findings suggested that the overall impact of climate change on agricultural profits was minimal. however, effects varied across states, and predicted temperature and precipitation increases were unlikely to affect major crop yields such as corn and soybeans. the anticipated impacts of climate change on wheat yields are expected to influence wheat prices and global markets significantly. by 2050, global wheat prices may rise by 7-18%, with the potential for even larger increases under extreme climate scenarios. shifts in international trade patterns are also likely as production capacities change across different regions (steen et al., 2023). increased price volatility could be anticipated as more frequent yield disruptions occur due to extreme weather events (song et al., 2022). net wheat imports were projected to grow for many developing countries, exacerbating food security concerns, particularly in import-dependent nations across africa and asia (habib-ur-rahman et al., 2022). price volatility in the global wheat market a dynamic interplay of supply and demand factors influences wheat prices. enghiad et al. (2017) reported that wheat price was significantly affected by weather pa ge 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 1-11, 2025 conditions, pest outbreaks, oil prices, and previous wheat prices. the study noted that the global wheat market was sensitive to supply shocks, with relatively inelastic demand. they indicated that wheat was susceptible to temperature fluctuations, with potential yield reductions due to global warming. for instance, drought in the major wheat-producing regions can lead to supply shortages and price spikes. rising global wheat prices have led to higher import costs, which in turn have driven up the prices of foods heavily reliant on wheat as an ingredient. in numerous developing nations, wheat-based foods comprise a significant portion of household diets, so increases in wheat prices could significantly impact food costs and security. algieri (2016), jebabli et al. (2014), and sadorsky (2014) argued that oil price was one of the key factors impacting agricultural commodity prices. the oil market influences wheat prices directly through production-related costs and indirectly through the demand for biofuels, which can lead to substitution effects. prices for fertilizer, farm machinery, and transportation are all affected by crude oil prices, which influence wheat production costs. baffes and haniotis (2016) reported that when oil prices were high, farmers diverted their agricultural resources, like land, to energy crops, such as corn, instead of wheat, due to higher demand for biofuels. this competition for land reduced wheat production, contributing to both volatility and upward pressure on wheat prices (chen et al., 2010). government policies, such as export restrictions or subsidies, can affect the wheat supply chain. anderson and nelgen (2012) concluded that price insulation policies in domestic markets had spillover effects on global prices, potentially intensifying price volatility. price volatility in wheat prices was also associated with demand-side factors. population growth and changing dietary patterns in developing countries can increase wheat demand. this rising demand, if not met by corresponding increases in supply, can contribute to price volatility (godfray et al., 2010). economic growth and urbanization in emerging economies can also increase wheat demand as consumers shift towards wheat-based products. this trend can pressure global wheat supplies and influence prices (alexandratos & bruinsma, 2012). using wheat for non-food purposes, such as biofuel production, can create additional demand pressure. changes in biofuel policies or oil prices can indirectly affect wheat demand and prices (headey & fan, 2008). application of arima and sarima models in agricultural price forecasting the autoregressive integrated moving average (arima) model has emerged as an effective tool for forecasting time series data, including agricultural commodities prices (jadhav et al., 2017). this model incorporates three key components: autoregression (ar), differencing to achieve stationarity (i), and moving averages (ma) terms to forecast prices. arima models have been popularly applied in various agricultural contexts due to their flexibility and ability to capture complex patterns, seasonality, trends, and irregular fluctuations in time series data (iqbal et al., 2005). if the data exhibited seasonal patterns, the seasonal autoregressive integrated moving average (sarima) model, an extension of arima modeling, is applied for precise forecasting. several studies have demonstrated the effectiveness of arima and sarima models in forecasting agricultural prices. for instance, jadhav et al. (2017) applied arima models to forecast paddy, ragi, and maize prices in karnataka, india, demonstrating the model’s power for price forecasting. applying arima models extends beyond price forecasting to other areas of agricultural production. ahmadzai and eliw (2020) used the arima model to forecast various economic variables related to wheat production in afghanistan, including area under cultivation, productivity, and consumption. this broader application highlighted the versatility of the arima model in addressing various aspects of agricultural economics and food security. the arima and sarima models are widely used time series forecasting methods due to their effectiveness in capturing temporal dependencies in data. applying the arima and sarima models to wheat price forecasting is particularly useful for predicting future trends, as it can handle non-stationary data, which is common in agricultural prices influenced by seasonal and external market factors. table 1 presents various arima and sarima models identified by previous literature to forecast wheat prices across different geographical regions. table 1: wheat price forecasting using arima and sarima models commodity model identified contributors wheat sarima(0,1,1)(0,1,1)12 (darekar & reddy, 2018) wheat arima(1,1,1) (sharma, 2015), (novković et al., 2019) wheat arima(1,2,1) (du, 2014) wheat arima(1,1,0) (kumar, 2019) in wheat price forecasting, arima and sarima models have shown reliability in short-term forecasting, especially when underlying price patterns are stable. however, arima and sarima models might face limitations when predicting prices under highly volatile or unexpected conditions. in that condition, other forecasting methods, like machine learning models or hybrid approaches (e.g., arima combined with neural networks), could improve accuracy by capturing non-linear relationships in the data (mahapatra & dash, 2019). pa ge 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 1-11, 2025 policy implications of wheat price forecasting sharma (2015) stated that price forecasts were essential for market participants to make production and marketingrelated decisions and for policymakers managing commodity programs and evaluating market impacts of domestic and global events. the author forecasted wheat prices in rajasthan, india, using the arima(1,1,1) model. the study’s policy implication highlighted that accurate price forecasting enhanced planning and development, empowering policymakers to anticipate commodity price trends and make informed decisions. this forecasting capability aided in formulating effective policies related to price structures, production levels, and consumption patterns. furthermore, it supported strategic decisions in international relations, allowing governments to adjust trade policies, maintain market stability, and respond proactively to global market fluctuations. forecasting the global price of wheat allows governments to proactively address food security concerns by managing supply and demand fluctuations, particularly in wheatimport-dependent nations. accurate price forecasts enable effective trade policy adjustments, helping countries plan for imports or exports to maintain stable domestic markets. forecasting also informs subsidy and support programs for wheat producers, enhancing income stability and agricultural productivity. additionally, it aids in understanding the economic impacts of global events, such as climate change or geopolitical disruptions, on wheat prices, enabling better resource allocation and risk management to stabilize price variations (bentley et al., 2022). materials and methods the monthly wheat prices (not seasonally adjusted, measured in u.s. dollars per metric ton) from january 1990 to october 2024 are made publicly available by the international monetary fund [pwheamtusdm]. for this study on forecasting the global price of wheat, secondary data from january 1990 to october 2024 were retrieved from the federal reserve economic data (fred) (https://fred.stlouisfed.org/series/ pwheamtusdm) on november 15, 2024. the data analysis was conducted using r 4.3.3 for windows, opensource software for statistical computing and graphics, provided by the r foundation for statistical computing, with the ‘tseries’ and ‘forecast’ packages to estimate model parameters and fit the sarima model. the average monthly price of wheat from january 1990 to october 2024 was $183.75 per metric ton, with a standard deviation of $68.22. the dataset exhibited considerable dispersion, which affected the model’s fit. therefore, the original data were log-transformed to stabilize the variance and enhance forecasting accuracy. figure 2: (a). histogram of original data and (b). histogram of log-transformed data source: author’s computation based on fred data, 2024 a b model descriptions autoregressive (ar) models an autoregressive (ar) model uses previous time steps of a variable to predict its future values. the general form of an ar model is expressed as: xt= σ + ϕ1 x(t-1 )+ et (1) where, xt is the value of the time series at time t (for t = 1, 2,.., n), σ is a constant centered around the mean, ϕ values are coefficients that represent the influence of past values on xt, xt-1 is the lagged price by one time period, and et is the random error term that is uncorrelated. when this error term has a zero mean and constant variance σ2 (white noise), xt follows a first-order autoregressive process, or ar(1). in this model, the value of x at time t depends on its previous value plus a random shock at time t. for a second-order autoregressive process, or ar(2), the model is: xt= δ + ϕ1 x(t-1 )+ϕ2 x(t-2)+et (2) where, xt now depends on its values from the previous two periods, centered around the mean δ. generally, for an ar process of order p, the current values of the series depend linearly on the past p values, expressed as: xt= δ + ϕ1 x(t-1 )+ϕ2 x(t-2)+…+ϕp x(t-p)+et (3) here, xt represents an ar(p) process, where each ϕ term represents the influence of previous values on the current value (gujarati, 2003). moving average (ma) models the value of xt can also be generated using a moving average (ma) process, which is formulated as: pa ge 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 1-11, 2025 xt= δ +et-θ1 e(t-1) (4) where, δ and θ1 are constants, and et is a white noise error term. in this case, xt is defined as a constant plus a moving average of the current and prior error terms, indicating that xt follows a first-order moving average, or ma(1) process. if x follows the expression: xt= δ +et-θ1 e(t-1)-θ2 e(t-2) (5) it is the second-order moving average or ma(2) process. more generally, for any positive integer q, the ma process is represented as: xt= δ +et-θ1 e(t-1)-θ2 e(t-2)-…-θq e(t-q) (6) where, xt is an ma(q) process, a linear combination of white noise error terms (gujarati, 2003). autoregressive moving average (arma) models the arma model combines autoregressive (ar) and moving average (ma) components, allowing it to capture the behavior of a time series and predict future values using historical data. the most general arma model has an order of p and q and is created by combining the equations for ar(p) and ma(q) processes (gujarati, 2003). it is expressed as: xt= δ+ϕ1 x(t-1)+ϕ2 x(t-2)+…+ϕp x(t-p)+et-θ1 e(t-1)-θ2 e(t-2)-…θq e(t-q) (7) where, δ, ϕ1 … ϕp and θ1 … θq are fixed parameters. this model is called a mixed autoregressive moving average model of order (p, q). autoregressive integrated moving average (arima) models arima model, also known as the box-jenkins methodology (montgomery et al., 2015), combines the autoregression and moving average components with differencing to transform non-stationary time series data into a stationary form. time series models generally assume that the series involved are weakly stationary, meaning that the series has a constant mean and variance, and its covariance is time-invariant. if the time series, such as a price series, is already stationary (with constant mean and variance), then an arma(p, q) model can be applied. however, if the series is not stationary, it can be made stationary by differencing it d times, and an arima(p, d, q) model is then used. in this context: • p represents the order of the autoregressive (ar) process, • d is the number of differencing (i) needed to achieve stationarity, • q indicates the order of the moving average (ma) process. according to the theoretical framework provided by box and jenkins, both ar and ma processes can be utilized in time series analysis. the box-jenkins method fits an arima model to a given dataset for accurate forecasting. materials and methods the box-jenkins approach to time series analysis and forecasting consists of three main steps: identification, estimation and diagnostic checking, and forecasting. in the identification stage, initial values are chosen for the parameters p, d, and q. initial estimates for the coefficients (ϕ1, ϕ2, …, ϕp) and (θ1, θ2, …, θq) are then obtained. next, diagnostic checks are performed to assess how well the model fits the data. if these checks indicate that a different model might be more appropriate, the process is repeated until a satisfactory model is identified. finally, forecasts are generated based on the final model selected during the estimation process and confirmed through model selection criteria. for this study, the ‘auto.arima’ function in r was used to select the most appropriate model and forecast the price of wheat by using that model. the ‘auto.arima’ function in r is a powerful and versatile tool widely used for time series forecasting. it is part of the ‘forecast’ package in r, which is used to automatically identify the best-fitting arima model for a given time series data using advanced algorithms. the ‘auto.arima’ function first checks for the stationarity in the time series data. if the data is non-stationary, differencing (d) is applied to make it stationary. statistical tests, such as the adf test, determine the number of differencing required. seasonal differencing is applied if the algorithm detects seasonal components in the data. after differencing, the function selects the value for autoregressive (p) and moving average (q) terms. for seasonal data, the function also determines the values of autoregressive (p), differencing (d), moving average (q), and seasonal period (m) terms in addition to the arima terms. by evaluating different combinations of these parameters using information criteria, such as aic and bic, the algorithm provided the best-fitting model for the given data. the model was selected so that the aic value was minimal. once the best arima model was identified, it was fitted to the data to estimate its parameters. the best-fitted model was then used to forecast global wheat prices from november 2024 to october 2025. results and discussion the global monthly price of wheat extracted from fred was not a time series data. so, the data was converted to make them time series and used for further analysis. the time series plot of the log-transformed data (figure 3) and the lag plots (figure 4) showed some fluctuations in the data over the period of january 1990 to october 2024. seasonal variations were apparent in the data, possibly due to weather fluctuations, harvest patterns, and market fluctuations (changes in demand and supply forces) during those time periods. pa ge 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 1-11, 2025 the data were then tested for stationarity using the adf test, and it was found that the data was not stationary (dickey-fuller statistic = -2.8432, p-value=0.2212). the data is considered non-stationary when the p-value of the adf test is greater than 0.05 (null hypotheses accepted). the acf and the pacf plots were also visualized. the acf plot (figure 5a) also showed a gradual decay over time lags but never cut off to zero, suggesting that the data must be made stationary for further analysis. figure 3: time series plot of the monthly global price of wheat (logged) from january 1990 to october 2024 source: author’s computation based on fred data, 2024 figure 4: lagged plots of the monthly global price of wheat (logged) from january 1990 to october 2024 source: author’s computation based on fred data, 2024 figure 5: (a). acf plot of the global price of wheat (logged) before differencing, and (b). pacf plot of the global price of wheat (logged) before differencing source: author’s computation based on fred data, 2024 a b pa ge 7 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 1-11, 2025 the data were then decomposed to separate the seasonal effects, trends, and random variation in the data (figure 6). the data exhibited similar seasonal trends over the periods, which were adjusted in the data to improve forecast accuracy. the adf test of the seasonally adjusted data yielded a value of -2.8726 (p-value=0.2088); thus, we failed to reject the null hypothesis, and it was concluded that the data was still not stationary. figure 6: decomposition of the global price of wheat (logged) data source: author’s computation based on fred data, 2024 the seasonally adjusted data were then differenced to make the data stationary. after each differencing, the p-value from the adf was analyzed along with their corresponding acf and pacf plots until the data became stationary. after the first-order differencing, the dickey-fuller test statistic value was -7.5652 (p-value=0.01). the null hypothesis was rejected and confirmed that the data was stationary after (d =1). however, in the acf and pacf plots of first-order differencing [figure 7 (a and b)], there were some significant spikes, which suggested that there might be some structures or patterns that needed to be accounted for before modeling. seasonal differencing was attempted to capture seasonal patterns in the data. the acf and pacf plots of the seasonal differencing [figure 8 (a and b)] showed numerous spikes, suggesting that the sarima model (p,d,q)(p,d,q) [m] could be more appropriate for this data. figure 7: (a). acf plot of the global price of wheat (logged) after first-order differencing, and (b). pacf plot of the global price of wheat (logged) after first-order differencing source: author’s computation based on fred data, 2024 a b pa ge 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 1-11, 2025 figure 8: (a). acf plot of the global price of wheat (logged) after seasonal differencing, and (b). pacf plot of the global price of wheat (logged) after seasonal differencing source: author’s computation based on fred data, 2024 a b the manual choice of the model through empirical observation was somewhat arbitrary. therefore, the ‘auto.arima’ function from the forecast package in r was employed to estimate the best-fitted model and generate the forecast. the ‘auto.arima’ function accounts for the stationarity and seasonality in the data and sets the model parameters to generate the forecast based on the aic and bic values. the log-transformed data was modeled using the ‘auto.arima’ function, resulting in sarima(0,1,1) (0,0,1)12 as the best-fitted model for our time series data. the parameter estimates of the best-fitted model are presented in table 2. table 2: parameter estimates of the fitted model estimates from sarima(0,1,1)(0,0,1)12 coefficients ma1 sma1 0.2523 -0.1042 s.e. 0.0487 0.0514 log likelihood 548.66 aic -1091.32 rmse: 0.0648 mae: 0.0487 me: 0.0004 note: ma = moving average, sma = seasonal moving average, s.e. = standard error, aic = akaike information criterion, rmse = root mean square error, mae = mean absolute error, and me = mean error source: author’s computation based on fred data, 2024 the monthly global price of wheat for the next year (november 2024-october 2025) was attempted using the sarima(0,1,1)(0,0,1)12 model. the forecasted price, generated on a logarithmic scale, was converted back to the original price scale by applying the exponential function to the logged values for convenient interpretation. the forecasted global price of wheat obtained from the study data with a 95% confidence interval is presented in table 3. a visual representation of these forecasts is presented in figure 9. table 3: the forecasted monthly global price of wheat (november 2024 october 2025) year month forecasted price (us$ / metric ton) lower 95% ci upper 95% ci 2024 november 198.51 174.74 225.50 december 197.37 160.89 242.12 2025 january 197.71 152.55 256.25 february 198.25 146.20 268.83 march 199.09 141.16 280.80 april 199.40 136.48 291.33 may 197.66 130.99 298.25 june 199.95 128.61 310.86 july 202.40 126.59 323.61 pa ge 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 1-11, 2025 august 203.62 124.04 334.28 september 202.21 120.12 340.39 october 201.36 116.79 347.17 source: author’s computation based on fred data, 2024 figure 9: forecasted monthly global price (logged) of wheat from november 2024 to october 2025 with a 95% confidence interval source: author’s computation based on fred data, 2024 after forecasting the monthly global price of wheat, residual diagnostics were performed to assess the model’s accuracy. the residual plot of the best-fitted model (figure 10a) illustrated that the residuals were random, with a mean and variance of 0.0004 and 0.0042, respectively. residuals with a mean and variance close to zero confirmed a well-fitted model. also, the acf and pacf plots of the residuals showed no significant autocorrelation among the residuals [figure 10 (b and c)]. hence, the residuals were white noise. moreover, the ljung-box test also yielded a p-value of 0.3918, confirming that the residuals exhibited no significant autocorrelation. a b c figure 10: (a). residual plot of the fitted model, (b). acf plot of the residuals, and (c). pacf plot of the residuals source: author’s computation based on fred data, 2024 pa ge 10 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 1-11, 2025 conclusion this study used historical data on global wheat prices from january 1990 to october 2024 to model and forecast prices from november 2024 to october 2025. the study identified sarima(0,1,1)(0,0,1)12 as the bestfitted model by using the ‘auto.arima’ function in r. the model predicted the global price of wheat solely based on historical prices, without explicitly incorporating other factors such as climate change, market fluctuations, inflation, and government policies. the forecasted price increased steadily, with some fluctuations, from $198.51 per metric ton in november 2024 to a peak of $203.62 per metric ton in august 2025. these projections could be useful in allowing wheat farmers to make production decisions beforehand. policymakers can also use this forecast to anticipate global wheat prices and make informed decisions at the national and international levels. furthermore, wheat-importing and exporting countries could use these forecasts to adjust their trade policies to deal with price fluctuations. this study has several limitations. first, the forecast presented in this study is based on the assumption of linearity, which may not represent the non-linear characteristics of global wheat prices. second, this forecast is based on historical patterns in price, which may not hold in dynamic market conditions. third, the global price of wheat could be affected by several other factors, such as climate change and change in market equilibrium, which this model did not capture. fourth, results from a single study like this may not be fully generalized to other agricultural contexts or time periods. fifth, time series forecasting like this is only appropriate for short-term forecasting and might result in inaccurate results if used for long-term forecasting. future studies should consider developing more robust forecasting models, such as hybrid models capable of capturing complex market phenomena affecting wheat prices globally. moreover, researchers should cross-validate this model’s findings across different geographical locations and time spans to improve the model’s external validity. references alexandratos, n., & bruinsma, j. 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(n.d.). production wheat. u.s. department of agriculture. https://fas. usda.gov/data/production/commodity/0410000 pa ge 1 pa ge 11 9 american journal of applied statistics and economics (ajase) impact of macroeconomic factors on government spending in ghana tobias kwame adukpo¹*, jacob obeng bethel² volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.5833 https://journals.e-palli.com/home/index.php/ajase article information abstract received: july 30, 2025 accepted: september 01, 2025 published: october 09, 2025 this study examines how key macroeconomic variables such as gdp growth, inflation, interest rates, government debt and unemployment influence government spending in ghana. while many studies rely on traditional econometric models, this research applies a bayesian regression framework, which allows for the incorporation of prior information and a clearer assessment of uncertainty in the estimates. the results show that gdp growth, public debt and unemployment are positively associated with government spending, while higher interest rates constrain fiscal expenditure. the effect of inflation remains uncertain. these findings provide new evidence on the dynamics of fiscal behavior in ghana and highlight the need for policymakers to balance growth and debt considerations while managing the risks of rising interest rates. the study contributes to the broader debate on how macroeconomic conditions shape fiscal policy in emerging economies. keywords bayesian analysis, economic indicators, economic modeling, fiscal policy, government spending, macroeconomic factors, public expenditure 1 department of accounting, university for development studies, ghana ² department of economics, university of ghana, ghana * corresponding author’s e-mail: adukpotobias@gmail.com introduction economic policy is significantly supported by the presence of government spending as it carries a lot of influence in the progress of any given country (stiglitz & rosengard, 2015). for a nation like ghana, which is actively striving to have a strong and inclusive growth, understanding the factors that influence government expenditure is not just academic research but an important investigation in achieving proper fiscal management and long-term financial sustainability. ghana has had a longdocumented history of having to deal with a complex set of fiscal challenges, such as the constant budget deficits, rising public borrowing and the fine juggling act of finding the means to fund development projects against available revenue (asiama et al., 2014). these challenges show the persistent instability in ghana’s fiscal space, which makes the dynamics of governmental expenditures an important topic to study. although traditional econometric analyses help determine economic relationships, sometimes these analyses are not comprehensive enough in grasping the complexity and uncertainties that exist in the fiscal policy of a developing economy. these constraints include limitations in integrating previous theoretical information, working with relatively medium sample sizes and the extensive probabilistic interpretation of parameter estimations (koop & korobilis, 2010). this article fills these gaps by depending on a bayesian econometric approach to examine the impacts of macroeconomic factors on government spending in ghana. the bayesian framework has distinct benefits: it gives the ability to explicitly incorporate prior information and economic theory, provides a richer understanding of parameter uncertainty by summarizing information in full posterior distributions and it is especially resilient in situations when the quantity or quality of data is relatively sparse, or when some structural changes occur (an & schorfheide, 2007). the primary objective of this research was to understand the complex relationships that exist between the most significant macroeconomic indicators, such as real gdp, inflation rate, exchange rates, interest rates, public debt levels and unemployment levels on the dynamics of government spending in ghana. through a more stringent bayesian approach, we presented a more informed and probabilistically oriented assessment regarding how these macroeconomic processes influence the fiscal decision process and outcomes in ghana. the insight drawn from this examination is essential to policymakers in ghana and helps develop better, more robust and responsive fiscal policies, which improve the way the state handles finances and eventually this change will help steer ghana to a scenario of long-term economic success, regardless of the changes in the global and local economy. the study is part of the large body of literature regarding fiscal policy in developing economies and provides a methodological framework that other researchers can use in a similar setting. pa ge 12 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 119-126, 2025 literature review understanding government spending government spending, also known as public expenditure, refers to the spending made by a government to finance its operations and functions (stiglitz & rosengard, 2015). it includes current consumption expenditure for daily operations and public servant salaries, capital investments aimed at long-term asset creation like infrastructure for social welfare programs and income redistribution and crucially, interest payments on government borrowing (musgrave & musgrave, 1980). these expenditures serve as vital tools of fiscal policy that directly influence macroeconomic variables such as aggregate demand, economic growth and employment (keynes, 1937; barro, 1990; afonso & furceri, 2010). in developing countries like ghana, government expenditure is of much concern, since it supports the establishment of necessary public services, fosters the creation of supportive infrastructure and enables the establishment of social protection networks. consequently, a comprehensive understanding of its dynamics is of paramount importance for effective economic governance and the achievement of national development goals. theoretical foundations of government spending wagner’s law (law of increasing state activity) this hypothesis was proposed by adolph wagner in the late 1800s and has been the subject of several studies. the hypothesis suggests that as economic development indicators such as rising per capita income, industrialization and public sector expansion improve, government expenditure is also expected to increase correspondingly. with a rise in the number of nations that are getting richer, there is a growing need for public goods and services (education, healthcare, and infrastructure, etc.), as well as the nature of administrative and legal operations, which have also seen government expenditures rise (wagner, 1883). in some regions around the world, not everyone has supported this theoretical evidence, although some studies conducted in ghana have supported the use of this law, which implies that the growth of the economy results in higher levels of government spending in the ghanaian economy (keho, 2016). peacock-wiseman hypothesis (displacement effect) contrary to the smooth rise that wagner suggested, peacock and wiseman (1961) proposed that spending by governments does not rise progressively but in a sequential manner in jumps. the causes of these jumps are mostly social upheavals such as wars, natural disasters, or a major economic crisis. in times like these, those societies can live with increased taxation to fund higher government spending. after the crisis passes, increased government spending and taxation made during its high points are likely to continue, as people get accustomed to a greater governmental role and the new services the government provides. this displacement effect, in addition to an inspection effect (reassessment of acceptable levels of taxes by the people) and a concentration effect (central government increases its limits), results in a higher permanent plateau of government spending (peacock & wiseman, 1961; ocran, 2011). the history of economic shocks and structural adjustment programs in ghana proves an excellent testing arena for this hypothesis. keynesian theory in the keynesian approach, government spending is considered an exogenous variable and a method of stabilizing the economy. the expansion in government expenditure increases aggregate demand when there is no growth and aggregate demand is at its lowest, particularly during a recession, by way of a multiplier effect, thus affecting economic development and employment (keynes, 1937). it means that fiscal policy actively participates in economic cycle management, which proposes an idea of countercyclical spending to counterbalance the fluctuations in the private sector (imf, 2014). empirical review the connection between government expenditure and macroeconomic measures has been studied extensively, with studies frequently using a diversity of econometric methods to ensure a variety of national economies (gemmell et al, 2011; gupta et al., 2005). gdp growth has always been associated with government expenditure. to illustrate this, afonso and jalles (2016), in a panel var model of 28 eu countries, discovered that economic growth is usually accompanied by higher government expenditure, which implies procyclical fiscal behaviour in most of the eu member states. similarly, gali and perotti (2003) used a structural var model across oecd countries, including germany, france, the uk and the us, to argue that fiscal policies moderate toward the direction of expansion during economic booms as governments spend on government services and infrastructure. in ghana, mensah and adukpo (2025) used a multiple regression model and found that capital expenditure is a significant determinant of economic growth, though recurrent expenditure increased economic growth positively but was not significant. these implications highlight the point that the growth in gdp leads to not only widened fiscal space but also a shift in policy inclinations that could be achieved by increasing investment on a larger scale in the public sector. inflation, on the other hand, has played a more complex role in government spending. high inflation erodes the real value of government budgets and complicates longterm fiscal planning. for instance, lithuania et al. (2012), using vecm and granger causality tests, established that inflation volatility contributed to fiscal uncertainty and constrained capital investment. their study showed a negative correlation between inflation and developmental spending, particularly in liberal economies with high fiscal balance requirements. similarly, anagaw (2023), pa ge 12 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 119-126, 2025 emphasized that persistent inflation undermines macroeconomic stability, reduces long-term growth potential, disproportionately affects low-income and unemployed groups. these findings highlight how inflation not only depreciates the real value of government resources but also generates substantial distortions that hinder both fiscal planning and sustainable economic growth. interest rates also influence government spending since they influence the price of borrowing. high interest rates increase the burden of debt service payments, thereby reducing the fiscal space available for other crucial expenditures. in a case study conducted across 216 countries, peña (2023) used system gmm and granger causality analysis, revealing a strong negative correlation between interest rate and aggregate government expenditure. their findings show that an increase in interest rates acts as a deterrent towards the borrowing of essential areas like infrastructure and education. the impact of this effect is especially high in developing economies, where the payment of interest displaces funds that should be used in social and economic programs. another crucial aspect that determines government expenditure is public debt. in a panel fixed-effects model and analysis of high-debt oecd countries like italy, greece, and portugal, alesina et al. (2019) found that rising public fund levels of a country tend to tighten its fiscal policies, most commonly through capital expenditure cuts. backing this, the imf (2015) indicated that a high level of public debt usually forces governments to transfer funds, which should have been used to develop the country, to pay the debt. these findings support the debt overhang hypothesis, that there exists debt or burden in the form of debt that can restrict governments from stimulating the economy by spending. unemployment also acts as a major determinant of fiscal policy. during periods of economic downturn, governments often increase spending on social protection programs to mitigate the impact of job losses. a timevarying var model based on u.s. data by klein and linnemann (2020) demonstrated that the increase in unemployment rates leads to an increase in spending by governments on social protection programs. their finding demonstrates how automatic stabilizers, which include unemployment benefits, increase government spending during economic downturns at varying magnitudes at different points in time. complementing this perspective, nojeem (2020) employed an auto regressive distributed lag (ardl) bounds testing approach to nigerian data from 2010 to 2020. their results identified an inverse relationship between unemployment and economic growth, with unemployment further associated with rising crime rates. all these findings emphasize both the countercyclical role of fiscal policy and the wider macroeconomic and social implications of persistent unemployment. on the methodological side, afonso and sousa (2012) employed a bayesian structural var model based on data in the us, uk, germany and italy and their results indicated that fiscal shocks had a modest but persistent effect on gdp and private consumption. such methods based on bayesian conditioning have been preferred because of their capability to use prior knowledge and yield more consistent estimates, especially in complex and uncertain environments (koop & korobilis, 2010). to conclude, the literature consistently attests that macroeconomic factors such as gdp growth, inflation, interest rates, public debt and unemployment play huge roles in determining the pattern of government spending. however, the magnitude and directions of such effects vary depending on the economy and the model employed. this study builds on this foundation by using a bayesian multiple regression model and provides a more in-depth analysis of these relationships in ghana. the bayesian approach is advantageous in its capacity to ensure robust modelling, which is highly flexible because it combines prior information with explicit consideration of the parameters’ uncertainty. materials and methods this study employs a bayesian multiple regression method to estimate the impact of the macroeconomic variables on government spending in ghana. bayesian methods are chosen based on their potential robustness in dealing with model uncertainty and incorporating prior information and their ability to produce full posterior distributions of the estimated parameters. using this is especially favorable in macroeconomic cases when one of the main problems is data limitation and multicollinearity. the analysis was also based on secondary annual data covering the period from 2000 to 2024. the key variables included in this research were government spending, gdp growth, inflation, interest rates, public debt and unemployment rates. these data were sourced from institutions such as the bank of ghana (bog), international monetary fund (imf), ghana statistical service (gss) and the world bank. these sources were selected for their credibility, consistency and relevance to ghana’s macroeconomic landscape. additionally, all monetary figures were converted into real terms to neutralize inflationary effects, which ensures that our analysis captures genuine economic impacts. model specification the empirical model is defined as a linear regression in which the dependent variable is the government expenditure and the independent variables are gdp growth rate, inflation rate, interest rate, public debt and unemployment rate. the model is presented as: govexpt = βo + β1gdpgrt + β2inft + β3intt + β4 pubdet + β5unempt + εt where, govexpt= government expenditure at time t gdpgrt = gdp growth rate at time t pa ge 12 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 119-126, 2025 inft = inflation rate at time t intt = interest rate at time t pubdet = public debt at time t unempt = unemployment rate at time t βo is the intercept β1,β2…β5 are the coefficients for the explanatory variable εt is the error term the equation above can be written in matrix form as: y = xβ+ϵ, the error term is assumed to follow a normal distribution (i.e. ϵ~nn (0,σ2 i)), β=(β0,β1…βk-1) t, is the n×k vector of parameters and ϵ=(ϵ1, ϵ2…ϵn) t the n×1 vectors of errors. in contrast, bayesian regression involves prior beliefs or knowledge in the study by use of prior distributions as opposed to traditional frequentist regression, which merely uses observed data. in this study, non-informative priors were used to ensure that the data itself primarily influences the results, which allows for an unbiased estimation process. the likelihood function is assumed to be normally distributed, which aligns with standard regression assumptions. to determine the posterior distributions of the model parameters, the markov chain monte carlo (mcmc) method was used with a focus on the gibbs sampling algorithm. this approach allows the problem of approximating complex posterior distributions with efficiency and credibility, mostly in situations where an analytical solution is not possible (kruschke, 2018). estimation technique in bayesian inference, the goal is to estimate the posterior distribution of the model parameters β=(β1,β2…βk) and σ2 conditional on the observed data. this study models the relationship between government spending and five key macroeconomic variables: gdp growth, inflation rate, interest rate, public debt and unemployment rate. the bayesian estimation process involves three key steps: first, we define the prior distributions for each β. given that we lack strong prior information about the exact magnitude of the effects for the β’s, we used weakly informative priors. the prior distribution for each coefficient (β) is assumed to follow a normal distribution with mean m and variance v ( β~n(m,v)) to allow flexibility. the prior density function for each β is the prior distribution for σ2 is also assumed to follow an inverse gamma distribution with hyperparameters a and b. the prior density function for σ2 is: secondly, we specify the likelihood distribution of the observed y. the likelihood function was derived based on the observed data and the assumed error distribution. the likelihood of the joint density function of the observed yi’s is: figure 1: histogram of government spending with a normal curve because the purpose of the study is to find out how the macroeconomic indicators affect government spending trends, the histogram (figure 1 above) reveals that the data on government spending is approximately normally distributed, with a slight skew in the right direction. this is in support of the likelihood function of the bayesian model, which implies that a gaussian likelihood is suitable. the consistency between the data with the normal curve indicates a well-specified inference model. thirdly, using mcmc sampling techniques, the posterior distributions of the parameters (β’s) are obtained, which allows for robust estimation and uncertainty quantification. the posterior density function for β and σ2 were derived using the formula: pa ge 12 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 119-126, 2025 data analysis and discussions this study employed a bayesian multiple regression framework to examine how key macroeconomic variables, including gdp growth, inflation rate, interest rates, public debt and unemployment rate, affect government spending. through the adoption of a bayesian approach, the analysis does not capture the direction and strength of these relationships only but also provides a table 1: posterior estimates of the bayesian regression model macroeconomic indicators mean sd hdi 3% hdi 97% mcse mean mcse sd intercept 27.498 6.681 14.352 39.458 0.115 0.081 gdp growth 1.681 0.555 0.654 2.707 0.012 0.008 inflation rate 0.300 0.184 -0.021 0.676 0.003 0.002 interest rate -1.797 0.399 -2.580 -1.071 0.009 0.006 public debt 0.242 0.065 0.117 0.362 0.001 0.001 unemployment rate 3.358 3.358 1.491 5.106 0.022 0.016 sigma 4.518 0.669 3.188 5.700 0.015 0.010 source: authors' calculations pa ge 12 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 119-126, 2025 comprehensive understanding of the uncertainty around each estimate through full posterior distributions. the results, as presented in table 1 (posterior means and hdis) and table 2 (mcmc diagnostics), offer clear and probabilistically informed insights into how each factor contributes to shifts in government expenditure; however, the credible intervals reflect the confidence we place in those findings. the intercept, which represents the estimated baseline level of government spending when all macroeconomic factors are held constant, was found to be 27.498, with a 94% highest density interval (hdi) ranging from 14.352 to 39.458. this wide interval reflects a degree of uncertainty around the baseline level of expenditure. this is due to unobserved factors or structural shifts in fiscal policy over the study period. gdp growth exhibited a strong positive relationship with government spending. the posterior mean coefficient was 1.681, with a 94% hdi of (0.654 to 2.707), which suggests that a one-unit increase in gdp growth is associated with an approximate 1.68 unit increase in government spending. this finding aligns with wagner’s law, where higher economic growth enhances revenue mobilization, enabling greater fiscal space for government expenditures. inflation demonstrated a marginal and uncertain effect. the posterior mean coefficient was 0.300, but the 94% hdi of (-0.021 to 0.676) includes zero. this indicates a potential lack of robust influence, which suggests that inflation has a neutral or slightly ambiguous effect on fiscal policy decisions, depending on prevailing monetary and price stabilization measures or through adaptive or nominal adjustments that insulate aggregate spending from direct inflationary shocks. interest rates showed a significant inverse relationship with government spending. the posterior mean coefficient was -1.797, with a 94% hdi of (-2.580 to -1.071), which indicates that as interest rates increase, government spending tends to decrease. this is attributed to the rising cost of debt servicing and a contraction in fiscal space, which limits the government’s capacity for discretionary spending. public debt had a positive and statistically significant effect on government spending, with a posterior mean of 0.242 and a 94% hdi of (0.117 to 0.362). this relationship suggests that increases in public debt, primarily through borrowing, enable governments to finance higher levels of expenditure. borrowed funds are often directed toward capital investments, social programs or economic stimulus packages, especially in developing economies like ghana, where revenue bases are limited. the unemployment rate displayed a strong and positive relationship with government spending. the posterior mean coefficient was 3.358, and the 94% hdi of (1.491 to 5.106) was entirely positive. this indicates that higher unemployment levels lead to greater government spending. this is likely due to increased allocations toward social protection, unemployment benefits, skills training initiatives and job creation initiatives aimed at mitigating social distress and stimulating economic activity. lastly, the posterior mean of sigma, which captures the standard deviation of the model’s residuals, was 4.518, with a 94% hdi of (3.188 to 5.700). the monte carlo standard errors (mcse) for all parameters were consistently low. this confirms adequate convergence of the markov chain monte carlo (mcmc) simulations and reliability of the estimated posterior distributions. the convergence of the markov chain monte carlo table 2: posterior estimates of the bayesian regression model ess bulk ess tail r hat intercept 3384.0 4067.0 1.0 gdp growth 2191.0 3816.0 1.0 inflation rate 2885.0 4084.0 1.0 interest rate 1892.0 2788.0 1.0 public debt 2795.0 3681.0 1.0 unemployment rate 1932.0 2885.0 1.0 sigma 2105.0 2599.0 1.0 source: authors' calculations (mcmc) chains was thoroughly assessed using r hat and effective sample size (ess) diagnostics. all r hat values were 1.0, indicating excellent convergence for all parameters. though the ess values, which ranged from 1892.0 to 3384.0 for ess bulk and 2599.0 to 4084.0 for ess tail, were generally satisfactory, slightly lower ess bulk values were observed for interest rates and unemployment rates. however, the overall ess values were deemed sufficient to ensure the reliability and robustness of the posterior estimates. this confirms that the mcmc procedure yielded well-converged and dependable parameter estimates. the convergence and reliability of the bayesian regression were further assessed through visual inspection of the posterior distributions and trace plots (figure 1). consistent with the r hat values of 1.0, the trace plots for all parameters exhibited good mixing, which indicated successful convergence of the mcmc chains. the posterior distributions displayed in the left column of figure 1 visually represent the uncertainty surrounding each parameter estimate. for instance, the posterior distribution for the interest rate was centered on a pa ge 12 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 119-126, 2025 figure 2: source: authors’ calculations negative value, and this confirms the negative relationship observed in table 1, with a mean of -1.797. similarly, the positive relationships for gdp growth (mean 1.681), public debt (mean 0.242) and unemployment rate (mean 3.358) were reflected in the location of their respective posterior distributions. the wider distribution for the inflation rate, compared to other parameters, mirrored the greater uncertainty indicated by its wider 94% hdi (-0.021 to 0.676) in table 1. conclusion this research employs a bayesian multiple regression framework to examine how key macroeconomic factors, including gdp growth, inflation, interest rates, public debt and unemployment influence government spending. the bayesian approach allowed for the incorporation of prior knowledge and provided an insightful understanding by capturing the uncertainty surrounding each relationship through probability distributions. the analysis revealed several meaningful insights. gdp growth, public pa ge 12 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 119-126, 2025 debt and unemployment were found to have positive associations with government spending. this suggests that as the economy grows or faces higher unemployment, governments tend to increase their expenditure, either to sustain growth or cushion the social impact of job losses. the positive effect of public debt reflects the reality that governments often rely on borrowing to finance increased spending, especially during times of economic pressure or investment-driven policy goals. on the other hand, interest rates showed a strong negative relationship with government spending. this is consistent with economic theory that says that as higher interest rates raise the cost of borrowing, it can constrain fiscal space and reduce the incentive or ability of governments to 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(1883). three extracts on public finance, reprinted in r.a. musgrave and a.t. peacock (eds.), classics in the theory of public finance, london: macmillan, 1958, (pp. 1-15). pa ge 1 pa ge 58 american journal of applied statistics and economics (ajase) innovative logistics solutions formation of an efficient service system for wholesale businesses jiyu chen1, oksana kudriavtseva1* volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.4077 https://journals.e-palli.com/home/index.php/ajase article information abstract received: november 16, 2024 accepted: december 26, 2024 published: june 28, 2025 in the context of globalization and rapid technological transformation, establishing an efficient logistics service system is critical for wholesale enterprises. this study investigates how logistics outsourcing, service innovation, and 3d printing technology synergistically drive innovative logistics solutions. through an integrated approach combining literature review and case studies, the study reveals three key findings: first, logistics outsourcing reduces operational costs and enhances competitiveness by leveraging specialized third-party expertise. second, service innovation strengthens service quality and market positioning through data-driven analytics and customer-centric customization strategies. third, 3d printing technology diminishes global logistics volumes via localized on-demand production while addressing personalized consumer demands. the integration of these three elements fosters a flexible and sustainable logistics ecosystem, enabling enterprises to balance efficiency and environmental objectives. however, challenges such as technology implementation costs, workforce adaptability, and supply chain collaboration require systematic mitigation strategies. the study concludes by emphasizing that the strategic alignment of technological innovation and operational synergies is pivotal for wholesale enterprises to develop futureready logistics systems. it further proposes a practical framework for achieving sustainable growth, while suggesting future research directions to explore advanced applications of artificial intelligence and blockchain technologies in this domain. keywords efficient service system, logistics solutions, wholesale businesses 1 kharkiv national automobile and highway university, kharkiv 61002, ukraine * corresponding author’s e-mail: kseniakydr@ukr.net introduction in the contemporary business landscape, the formation of a robust logistics service system is essential for the success of wholesale enterprises (guterres, 2020). as the backbone of supply chain management, logistics plays a critical role in ensuring the smooth and efficient flow of goods from producers to consumers (woźniak, 2021). wholesale enterprises, in particular, deal with large volumes of products that need to be moved quickly and accurately to meet customer demands (soja & soja, 2020). the complexity of logistics operations has increased significantly due to the rapid pace of technological advancements and market globalization, making it imperative for businesses to develop innovative logistics systems. an effective logistics service system encompasses various components such as inventory management, order processing, transportation management, and warehousing (carrelli et al., 2000). these components must work seamlessly together to enhance operational efficiency, reduce costs, and improve service levels (yeh, 2017). the goal is to create a logistics framework that not only meets the current needs of the business but also has the flexibility to adapt to future changes and challenges (xiong & qureshi, 2013). the integration of advanced technologies is a key factor in the modernization of logistics services (venkatesh et al., 2003). technologies such as real-time tracking, automated inventory management, and data analytics have revolutionized the way logistics operations are conducted (abed, 2020). these technologies facilitate better decision-making processes, increase transparency, and provide valuable insights that can be used to optimize logistics performance. moreover, the implementation of sustainable logistics practices has become increasingly important in today’s environmentally conscious society. businesses are now focusing on reducing their carbon footprint and adopting eco-friendly practices in their logistics operations (jin & choi, 2019). this includes the use of electric vehicles, optimizing delivery routes to minimize fuel consumption, and implementing green packaging solutions. despite the potential benefits, the formation of an effective logistics service system is not without its challenges (al omoush et al., 2018). businesses must navigate various obstacles such as high implementation costs, resistance to change from employees, and the need for continuous training and development. additionally, external factors such as regulatory requirements and market fluctuations can impact the effectiveness of logistics operations (prasanna et al., 2019). this paper aims to explore the formation of an effective logistics service system for a wholesale enterprise, delving into its critical components, implementation strategies, challenges, and future trends. by synthesizing insights from established research and real-world case studies, this study seeks to provide a comprehensive framework that wholesale businesses can adopt to enhance their logistics operations and achieve sustainable competitive advantage. the focus will be on identifying best practices pa ge 59 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 58-64, 2025 and innovative solutions that can be tailored to the specific needs of wholesale enterprises. literature review the formation of an effective logistics service system for wholesale enterprises is a multifaceted challenge that requires a comprehensive understanding of logistics innovation, information technology integration, and sustainable practices. the significance of logistics in modern business operations is well-documented in the academic literature, highlighting its role in enhancing operational efficiency, reducing costs, and improving service levels. previous research discussed the critical importance of innovation in logistics services, emphasizing that businesses must continually evolve their logistics models to remain competitive (chapman et al., 2003). they argue that integrating innovative approaches can lead to the development of new business models that are more adaptive to economic and technological changes. this perspective is particularly relevant for wholesale enterprises, which must handle large volumes of goods and require efficient logistics systems to ensure timely delivery and customer satisfaction. grets and kasarda (1997) further explore the impact of the information era on enterprise logistics, noting that the integration of advanced information technologies has revolutionized logistics operations. they highlight that real-time tracking, automated inventory management, and data analytics have become essential tools for modern logistics systems. these technologies facilitate better decision-making processes, increase transparency, and provide valuable insights for optimizing logistics performance. the authors underline the necessity for businesses to embrace these technological advancements to enhance their logistics capabilities and maintain a competitive edge (grets & kasarda, 1997). in the context of food supply chains, van der vorst, beulens, and van beek provide valuable insights into the role of innovations in logistics and ict. they argue that leveraging these innovations can significantly enhance the traceability, transparency, and efficiency of food supply chains. this is particularly important for ensuring food safety and quality, which are critical concerns for both consumers and regulators. the authors provide examples of how ict solutions can be implemented in food supply chains to improve logistics performance and meet stringent regulatory requirements (van der vorst et al., 2005). pfohl offers a comprehensive overview of logistics systems, emphasizing the complexity of managing the various components of logistics operations. he discusses the importance of a well-structured logistics framework that can handle the intricate interrelations within supply chains. pfohl argues that an efficient logistics system is vital for reducing costs, improving service levels, and ensuring the seamless movement of goods from suppliers to end consumers. he also highlights the role of sustainable logistics practices in reducing the environmental impact of logistics operations, an increasingly important consideration in today’s business environment (pfohl, 2010). the literature also identifies several challenges in the formation and implementation of logistics service systems. high implementation costs, resistance to change from employees, and the need for continuous training and development are common obstacles that businesses face. additionally, external factors such as regulatory requirements and market fluctuations can impact the effectiveness of logistics operations. addressing these challenges requires a strategic approach that includes careful planning, integration of advanced technologies, and a focus on continuous improvement. the management logic of logistics outsourcing according to coase’s theory of corporate organization, the market and the enterprise are two coordination mechanisms for resource allocation. the market realizes resource allocation through the price system, while the enterprise realizes resource reconfiguration through organizational power. these two methods can replace each other, so “make-or-buy” has become an important choice for enterprises to pursue cost-effectiveness maximization in market competition. in the market competition environment, enterprises improve their market competitiveness by optimizing resource allocation methods. logistics outsourcing has become an important choice because it can help enterprises improve the quality of logistics services while reducing costs. the total logistics cost of an enterprise includes transportation costs, warehousing and inventory holding costs, and related management expenses. in order to minimize the total logistics cost while meeting customer needs, enterprises often weigh the cost of making and purchasing. logistics outsourcing has become an important strategy for manufacturing enterprises. through outsourcing, enterprises can use the resources and technology of professional logistics companies to achieve more efficient logistics management and respond to market changes more flexibly. the management logic of logistics outsourcing emphasizes that enterprises must establish an effective collaboration mechanism. this mechanism requires manufacturing enterprises and logistics enterprises to reach a consensus on service concepts and values, and jointly respond to market changes and customer needs. in this collaborative relationship, manufacturing enterprises are usually in an active position, while logistics enterprises need to continuously innovate service models to meet the needs of manufacturing enterprises. when outsourcing logistics management, enterprises also need to consider the risk management of outsourcing decisions. although outsourcing can bring cost savings and efficiency improvements, it is also accompanied by certain risks, such as substandard service quality and delivery delays. therefore, when choosing logistics outsourcing, manufacturing enterprises need to establish a strict pa ge 60 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 58-64, 2025 supervision and evaluation mechanism to ensure the reliability of outsourced service quality. in addition, the decision to outsource logistics needs to comprehensively consider many factors, including cost, service level, technical capabilities and market changes. when choosing a logistics outsourcing partner, manufacturing enterprises should comprehensively evaluate the service capabilities, technical level and market reputation of logistics companies to ensure that the selected partners can provide high-quality logistics services. through this multi-dimensional evaluation and management, manufacturing enterprises can form a flexible and efficient logistics service system, effectively optimize resource allocation, and enhance market competitiveness. it is worth mentioning that the global logistics market is expanding rapidly. according to market research reports, the global logistics market size reached approximately $8.6 trillion in 2023 and is expected to continue to grow at an average annual rate of more than 5% in the next few years. this growth reflects the strong demand for efficient logistics services in the global economy. figure 1 shows a keyword network related to “supplier selection”. through the connection between each key term, it intuitively shows the relationship and importance of various concepts in the field of supplier selection. figure 1: network analysis of co-occurrence according to authors’ keywords with 17 clusters the key terms in the figure include “supplier selection”, “sustainability”, “outsourcing”, “inventory management”, “stochastic programming”, “order allocation”, “game theory”, “multi-criteria decision making (mcdm)”, “analytic hierarchy process (ahp)” and “fuzzy programming”. these terms reflect the complexity of various decision factors and methods in the supplier selection process. this chart is closely related to the management logic of logistics outsourcing. supplier selection is one of the key factors for the success of logistics outsourcing, which directly affects the quality and cost of logistics services. as described in this article, when making logistics outsourcing decisions, enterprises need to comprehensively consider multiple factors such as cost, service level, technical capabilities and market changes to ensure the reliability and efficiency of outsourced services. through this visual approach, we can better understand and analyze the various factors and methods involved in the supplier selection process, thereby optimizing logistics outsourcing strategies and improving the market competitiveness of enterprises. the keyword network shown in figure 1, helps to identify key decision points and optimization opportunities in the supplier selection process, and provides an effective framework to help enterprises make wise outsourcing decisions in a complex market environment. service innovation of logistics enterprises in order to achieve effective collaboration between the manufacturing industry and the logistics industry, logistics enterprises need to continuously innovate service concepts and models. first of all, logistics enterprises must realize that the ultimate goal of logistics outsourcing of manufacturing enterprises is to improve market competitiveness. this requires logistics enterprises to increase the value-added content of management services in the service process and show customers that the risks of logistics management outsourcing are controllable. pa ge 61 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 58-64, 2025 the role of logistics enterprises should change from a simple “hourly worker” to a more advanced “steward” role. this means that logistics enterprises must not only complete specific logistics tasks, but also deeply participate in the logistics management process of customers and become management consultants and executors of manufacturing enterprises. through this role change, logistics enterprises can provide higher value-added services and enhance cooperative relations with customers. in terms of specific service innovation, logistics enterprises can start from the following aspects first, logistics enterprises need to update their service concepts. at any time, logistics enterprises should not forget that the purpose of logistics outsourcing of manufacturing enterprises is to improve the market competitiveness of enterprises, and the two decisive factors in outsourcing decisions are cost and service. logistics is a management activity. in the process of providing logistics services to customers, efforts must be made to increase the value-added content of management services and show customers that the risks of logistics management outsourcing are controllable. at the same time, logistics management issues must be rethought in the context of customer supply chain management. secondly, logistics companies need to innovate cooperation models. logistics companies should quickly complete the role transformation from “hourly workers” to “nannies” and then to “stewards”, strive to integrate into the logistics management process of customers, sincerely turn themselves into the (non-staff) logistics management department of manufacturing companies, and at least position themselves as the loyal executors and logistics management consultants of the logistics management plans of manufacturing companies. in addition, innovation in customer resource development is the key to service innovation of logistics companies. customers are the most important strategic resources for the survival and development of logistics companies. logistics companies must conduct in-depth research on customers’ production organization methods, supply chain operation status and value chain distribution, timely discover the “blue ocean” of logistics value-added services, and provide differentiated logistics service support for manufacturing companies. in the context of economic globalization, logistics companies must learn to integrate the “intelligence” of various professional institutions and use their research results to improve their service capabilities. logistics companies also need to increase the transparency of logistics services. improving transparency will increase trust, trust will enhance collaboration, and collaboration will discover more extended services and linkage opportunities. as the financial crisis continues to spread, “huddling together for warmth” with their customers should be one of the preferred options for logistics companies to “survive the winter”. finally, innovation in logistics cost management is also an important aspect of service innovation for logistics companies. the contribution of logistics companies to customer logistics cost management lies not only in reducing service prices, but also in the service valueadded of the logistics management solutions provided. logistics companies should strive to improve the value of logistics outsourcing or logistics services and enhance the market competitiveness of customers by understanding customer service requirements, optimizing service solutions, simplifying service processes, and improving information sharing levels. table 1: total volume of the global logistics services market from 2001-2024 year total volume of the global logistics services market in (usd 100 million) 2001 3200 2002 3300 2003 3400 2004 3600 2005 3800 2006 4000 2007 4300 2008 4600 2009 4700 2010 4900 2011 5200 2012 5400 2013 5700 2014 6000 2015 6300 2016 6600 pa ge 62 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 58-64, 2025 through service innovation, logistics companies can better meet the needs of manufacturing companies, enhance their own market competitiveness, and achieve collaborative linkage with manufacturing companies. it is worth noting that the global logistics service market is growing rapidly. according to table.1, the linear regression model is used to predict the total volume of the global logistics service market from 2025 to 2030 based on the existing data, as shown in figure 2 and 3, and the previous data trends are continued for prediction, the global logistics service market is expected to reach nearly us$10 trillion by 2026, code is shown in appendix 1. this growth trend shows the increasing demand for efficient and innovative logistics services. the impact of 3d printing on the logistics industry as an important innovative means of logistics, the rapid development of 3d printing technology has had a profound impact on the logistics industry. first, 3d printing technology brings production bases closer to consumer destinations, which will lead to a reduction in global logistics volume. as more products are produced near consumer locations, the global logistics network will face a strategic contraction, and logistics companies need to adjust their business models to adapt to new market demands. the continuous enrichment of 3d printing materials has enabled 3d printing technology to be applied in more fields. 2017 7000 2018 7300 2019 7600 2020 8000 2021 8300 2022 8700 2023 9100 2024 9500 figure 2: linear regression analysis of logistics market volume figure 3: logistics market volume forecast pa ge 63 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 58-64, 2025 at present, 3d printing has been applied to manufacturing, service, education, scientific research and other fields, including automobiles, clothing, medical care, food, aerospace, teaching, etc. this diversification of materials and application fields has shifted logistics demand from traditional raw materials and finished product transportation to the transportation of 3d printing materials and related technical support. a notable feature of 3d printing technology is that its product design space is unlimited and can meet personalized needs. in today’s market-oriented world, consumers’ personalized needs are increasing, and 3d printing technology can just meet this need. enterprises can design and produce personalized products according to the specific needs of customers, and logistics companies need to provide flexible logistics solutions to support this personalized production method. in addition, 3d printing technology enables manufacturing companies to adjust production processes more flexibly, which requires logistics companies to have stronger adaptability and innovation capabilities to meet the changing needs of manufacturing companies. logistics companies need to continue to work hard on service innovation, provide more flexible and efficient logistics services, and achieve collaborative linkage with manufacturing companies. 3d printing technology has put forward higher requirements for logistics companies. logistics companies need to continuously improve their own technical level and service capabilities to cope with the challenges and opportunities brought by 3d printing technology. for example, logistics companies can improve the automation level and service quality of logistics management, reduce logistics costs, and improve logistics efficiency by using advanced information technology. at the same time, logistics companies need to strengthen cooperation with 3d printing technology providers to jointly develop new logistics solutions. through cooperation, logistics companies can better understand the development trend and market demand of 3d printing technology, and timely adjust their own business models and service strategies to adapt to market changes. in summary, the rapid development of 3d printing technology has had a profound impact on the logistics industry. logistics companies need to continuously innovate service models, improve service quality and transparency to adapt to the changing needs of manufacturing companies, so as to maintain competitiveness in the fierce market competition. at the same time, logistics companies also need to strengthen cooperation with 3d printing technology providers, jointly develop new logistics solutions, and achieve collaborative linkage with manufacturing companies. through this collaboration and innovation, logistics companies can better cope with the challenges brought by 3d printing technology, seize new market opportunities, and build an efficient wholesale business service system. according to market data, the global 3d printing market is expected to reach us$34 billion in 2025, which further shows the important position and huge potential of 3d printing technology in the future market. conclusion in today’s rapidly changing business environment, building an efficient logistics service system is crucial to the success of wholesale enterprises. this article analyzes the management logic of logistics outsourcing, the service innovation of logistics enterprises, and the impact of 3d printing technology on the logistics industry. together, these factors constitute the core elements of innovative logistics solutions. first, as an important way of resource allocation, logistics outsourcing can help wholesale enterprises reduce costs, improve service quality, and enhance market competitiveness. when choosing logistics outsourcing, enterprises need to establish an effective collaboration mechanism and comprehensively consider factors such as cost, service level, technical capabilities and market changes to ensure the reliability and efficiency of outsourced services. secondly, logistics enterprises can better meet the needs of wholesale enterprises through continuous service innovation. service innovation includes updating service concepts, innovating cooperation models, developing customer resources, increasing service transparency and optimizing cost management. these innovative measures not only help to improve the quality of logistics services, but also enhance the competitiveness of logistics enterprises in the market and achieve collaborative linkage with manufacturing enterprises. finally, the rapid development of 3d printing technology has had a profound impact on the logistics industry. 3d printing technology makes production bases closer to consumer destinations, thereby reducing global logistics volume and prompting logistics companies to adjust their business models. in addition, 3d printing technology meets the personalized needs of consumers and requires logistics companies to provide more flexible and efficient logistics solutions. overall, the management logic of logistics outsourcing, the service innovation of logistics companies, and the application of 3d printing technology constitute the key elements of innovative logistics solutions. these elements interact with each other and jointly promote the construction of an efficient wholesale business service system. through continuous optimization and innovation, wholesale companies can maintain their leading position in the fierce market competition and achieve sustainable development. reference abed, s. s. 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(2017). the effects of successful ict-based smart city services: from citizens’ perspectives. government information quarterly, 34, 556–565. https:// doi.org/10.1016/j.giq.2017.05.001 pa ge 1 pa ge 32 american journal of applied statistics and economics (ajase) structural equation modeling analysis of equity-based islamic financing for nigerian small-scale enterprises growth jamiu o. badru1, kamilu a. saka2* volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.4424 https://journals.e-palli.com/home/index.php/ajase article information abstract received: january 19, 2025 accepted: february 22, 2025 published: may 10, 2025 this study investigates within covariance-based structural equation modeling (cv-sem) the potential causal relationships between islamic equity-based financing and nigerian small scale enterprises (sses) expansion. a quantitative survey design was employed to collect questionnaire-based, cross-sectional primary data from 512 educated sse owners and managers in eight business districts in the abeokuta metropolis. stata 12.1 software was utilized to estimate cv-sem with the maximum likelihood method. intuitively, the principal component analysis (pca) estimator was applied to derive continuous data scores for the latent variables. the fit indices tested (rmsea, cfi and tli) indicate that the study structural models fit the observed data. from the cv-sem estimations, musharakah and mudharabah have potentially positive and significant impacts on the sales growth of sses in the study area. findings reveal further that islamic equity financing tools such as musharakah, diminishing musharakah, and mudharabah are more likely to enhance the development of sses in the abeokuta metropolis significantly and indirectly through the islamic finance legal framework. the study affirms that the expansion of small enterprises in the study area will be directly, positively, and significantly influenced by islamic shared finance (musharakah) and islamic joint partnership (mudharabah). however, the indirect positive and significant impact of islamic equity finance through a legal framework (mediating factor) is stronger for the development of small firms than the direct impact. the study advocates that the federal government of nigeria should institute a suitable legal framework for islamic equity financing to enhance the operational capacity of small-scale businesses. keywords cv-sem, islamic finance, pca 1 department of mathematics and statistics, the federal polytechnic, ilaro, nigeria 2 department of banking and finance, the federal polytechnic, ilaro, nigeria * corresponding author’s e-mail: kamilu.saka@federalpolyilaro.edu.ng introduction small and medium-scale enterprises (smes) serve as a framework through which the economic growth of a country can be achieved due to their potential to generate employment opportunities, create wealth and reduce poverty (asian development bank [adb], 2022; sojoodi & jalili, 2022; shinkafi et al., 2023; world bank, 2023). this category of businesses section, smes, operates virtually in manufacturing and service sectors and thus provides employment opportunities for both skilled and unskilled people. consequently, the industry contributes to developing economies where it receives necessary encouragement. however, the incentives for smes to operate efficiently depend on important factors including cognitive skills, non-cognitive skills, a friendly business environment, financial support from financial institutions and government through access to finance, inclusive institutional framework and others. although financing problem still exists among smes in the asian region improved sme access to finance has long been regarded as a major driver behind economic improvements and success of indonesia, thailand, taiwan, and singapore (adb, 2022). however, smes in developing economies including nigeria often encounter acute credit financing problems from conventional banks which eventually limit their growth potential (adedeji, 2021; shinkafi et al., 2023; sonita, miswardi and nasfi, 2021; world bank, 2023). this incidence of funding gap from interest-based formal banks, in part, further dampens the development of smes in developing countries. in nigeria, for instance, the share of sme loans and advances (represented by loans to trade and general commerce) to deposit money banks total loans in 2023 is just a paltry 7.97% (central bank of nigeria statistical bulletin, 2023). such funding issues might have contributed to the sector’s less impressive performance that year. therefore, creating an alternative financing model mechanism for substantial advancement to improve access to capital for smes in developing countries (such as nigeria) beyond traditional bank credit is essential. the relevance of shariahcompliant financing models for higher performance of smes and the economy at large has been documented by researchers, policymakers, countries and international bodies (world bank-islamic development bank, 2015; adedeji, 2021; shinkafi et al., 2023; united nations development programme-islamic development bank, 2023). however, innovative islamic financing models like fully fledged equity-based islamic financing which has been proven worthy elsewhere for ameliorating funding issues faced by smes are still limitedly unexplored in nigeria (fitch ratings, 2023). in nigeria, armful studies by adam (2020); adedeji (2021), and shinkafi et al. (2023) found that islamic financing has a significant and positive effect on the performance of the country’s smes. however, certain pa ge 33 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 32-40, 2025 flaws are found with the analysis method used by these studies to arrive at such results. these include heteroscedasticity, non-disclosure of linearity, and normal distribution assumption, where least square methods are used. furthermore, to our knowledge, we found that a robust technique (such as structural equation modeling – sem) that addresses all these estimation issues in islamic finance studies has not been applied in the context of nigerian small-scale enterprises (sses). more worryingly, previous studies worldwide have ignored the estimation of the indirect effects of the islamic equity financing legal platform which provides the operational framework for the conduct of islamic finance activities. against this backdrop, the current study aims to extend existing finance literature by evaluating the reliability of musharakah, diminishing musharakah and mudharabah as equity-based islamic finance approaches towards growth of small scale enterprises (sses) in nigeria with a specific focus on abeokuta metropolis. with the islamic equity financing legal framework serving as a mediating variable, a critical question is raised under covariance-based structural equation modeling (cvsem) on what indirect effect islamic finance has on the growth of sse firms in abeokuta metropolis, nigeria. from the analysis conducted, it was indicated that both musharakah and mudharabah financing initiatives have positive and significant impacts on stimulating the sales growth of sses in the study area. the observed empirical evidence shows further that such significant impacts would be stronger indirectly when the islamic equity financing legal framework is developed and implemented. thus, islamic equity-based financing means (musharakah and mudharabah) can significantly help address the unmet financing needs of sses in nigeria and aid them in growing and generating jobs. this paper is divided into five sections. the study starts with an introduction followed by a literature review. section three discusses the methodology of the study. the data obtained were analysed, interpreted and discussed in section four while the conclusion and recommendations are contained in section five. literature review the literature has no convergence opinion regarding the most acceptable definition of micro and small-scale business (elasrag, 2016). however, some key criteria are normally used to draw a line of demarcation between a micro, small, medium and large business. these include the number of employees, turnover, assets size, annual sales, annual production, investment and paid-up capital (abdinur & ondes, 2022; adedeji, 2021; al-dabbas, 2023; elasrag, 2016; european commission, 2015; small and medium enterprises development association of nigeria, 2007; international finance corporation publication, 2020; 2024). international finance corporation publication (ifc, 2024) used employment, assets and sales value criteria to define a micro, small and medium enterprise (msme); however, out of the three criteria, an enterprise must meet two to be classified as either micro or small or medium venture. according to ifc, a micro-enterprise is defined as a business with some employees of not more than 10 or with an asset value of less than $100,000 (us dollar) or generates an annual sales value of not more than 100,000 us dollars. a small-scale enterprise is defined as an enterprise that employs between 10 and 50 workers or with total assets value between $100,000 to $3,000,000 or a business that realizes between $100,000 to $3,000,000 as annual sales. a medium enterprise in line with the ifc classification is categorised as an enterprise that recruits between 50 and 300 workers or a venture with a total asset value between $3,000,000 and $15,000,000 or makes between $3,000,000 and $15,000,000 as sales revenue in a year. the apex bank in nigeria, central bank of nigeria (cbn), in the year 2010 defined a micro enterprise as a one-man business that employs less than 10 workers and possess total assets (excluding land and building) that is not more than five million naira. the cbn further defines small and medium scale enterprises as businesses that employ between 11 and 200 workers and with total assets less than #500 million. the peculiarities of mses or smes in developing countries like nigeria are observed in the ownership structure as most are mainly sole proprietorships or partnerships with labour-intensive production methods. commercial banks in particular often feel reluctant to lend to these categories of business and when they (commercial banks) do they charge higher interest rates as costs of borrowing. these conventional banks often request collateral security with the fear that mses or smes are too risky. in addition, stringent conditions like listing requirements further render mses incapacitated to access funds from capital markets like the nigeria stock exchange. furthermore, most of the sources of formal sme financing institutions in nigeria ranging from traditional banks such as microfinance banks, universal (commercial) banks, merchant banks, and development banks to specialised financial institutions offer interestbased financing facilities to smes. although interestbased borrowing provides quick access to finance the liability of smes increases with such financing means. the implication is that profit which serves as an incentive for entrepreneurs is affected through the reduction in value. in other words, finding alternative noninterestbased funding options such as islamic financing product offerings will help encourage inclusive growth by mses and mses in the country. islamic financing is the provision of financial services that are based on the values, norms, laws and institutions found in, and derived from the sources of islam such as shari’ah to satisfy wealth material and social needs of all members of the community including smes (world bank group – islamic development bank, 2015). according to mohieldin et al. (2011) as revealed in wb-idb (2015) the core principles of islam underscore social justice, inclusion, and sharing of resources. unlike conventional pa ge 34 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 32-40, 2025 finance, islamic finance is a participatory finance (risksharing relationship) arrangement that is asset-based and equity-based. leveraging asset-based and equitybased finance arrangements for smes and start-ups in developing countries and emerging economies such as nigeria could prove crucial to uncovering entrepreneurial potential in these markets. the positive impacts of such arrangements include increased economic growth, youth empowerment and socioeconomic development. in asset-based financing, real economic activity is promoted with financial assets as the core requirement of islamic financial transactions (askari et al., 2014). the two commonly used variants of this model of financing are sale-based instruments (murabahah) and leased-based instruments (ijarah). a murabahah is a contract between a bank or financier and a client where the bank or financier purchases an asset required by the client and then sells it to the client at a cost and profit margin which are disclosed to the client and is paid back usually by instalments (abdinur & ondes, 2022; wd-idb, 2015; international trade centre, 2009). unlike conventional asset-related agreements, murabahah imposes a fixed financing rate during the financing term and ensures full transparency of price and mark-up. however, the pricing of some products offered under murabahah closely parallels or sometimes exceeds the pricing of conventional products (wd-idb, 2015). ijarah is an islamic-based leasing arrangement where money is exchanged for the use of an asset. under this arrangement, the financier or bank first buys the asset from a supplier and then leases it to the client. the main criterion for eligibility for financing as differentiation from conventional leasing is the ability to generate cash flows to serve the lease agreement, rather than providing security (collateral) and credit history. as a result of this criterion factor, ijarah is widely used to finance smes (mohieldin et al., 2011). on the other hand, equity-based financing is a form of financing where the bank or financier has an equity stake or interest in the sme business. this arrangement makes a financial institution that provides investment capital to operate as a partner to the sme. in other words, it is a partnership-based contract with a return to the bank or financier depending on the actual business performance of the clients (international trade centre, 2009). there are two types of sme partnership financing, joint venture (musharakah) and passive partnership (mudarabah). the two differ based on what the partners contribute to the partnership. musharakah in arabian language refers to sharing between two more entities or businesses (partnership). under this financing, more than two parties can be involved, and generally, each provides knowledge and skill in management and a share of the capital (abdinur & ondes, 2022). meanwhile, it is possible for one partner only to provide capital, in which case he or she becomes a sleeping partner. the profits from operations are shared based on the pre-agreed profit ratio. in the same manner, losses are borne by the partners in proportion to the capital they have provided (international trade centre, 2009). this arrangement underscores the islamic principle of sharing responsibility for shari’ah-based financing products. the mudarabah, passive partnership, transaction is a partnership transaction in which only one partner named the capital-providing investor or rab al maal contributes capital, and the other partner known as the business manager or mudarib contributes skill and expertise. according to itc (2009), the relationship between the partners is founded upon trust, with the investor relying heavily on the business manager, and his or her ability to manage the business and be honest with profit share payments. in this financing arrangement, the client does the management work in the business while the financier or bank only provides capital. however, this increases the bank’s exposure to business risks. with a diminishing musharakah contract, one partner promises to acquire the equity share of another person’s business by gradual payment until he makes the final payment to become the owner (hussain et al., 2015). this type of financing arrangement is mostly famous among iranian business enterprises. theoretically, the model of discrete choice (dcm) provides a framework for justifying the potential use of islamic finance in this study by smes. the utility-based model, dcm, explains a rational situation in which a decision maker selects the best alternative from a list of two or more choices. selection of a preferred option is premised on the objective of maximal satisfaction derived from the best alternative. the proponents of islamic finance have argued through empirical data that owners and managers of smes often prefer islamic financing options to conventional funding models (al dabbas, 2023; baloch & chimenya, 2023; shamsudheen, 2023). this preference is due to greater benefits offered by islamic finance based on the principles of islam and shariah laws. for instance, baloch and chimenya (2023) opined that islamic financing is presumed to be desired by smes and other users because of its ethical considerations (such as prohibition of interest-based transactions, gambling, and undue returns) that plague conventional options. in other words, this study hypothesizes that the sampled smes in the study area would prefer islamic financing options to traditional alternatives. al dabbas (2023) employed descriptive analysis to investigate how islamic finance affects the development of 100 jordan-based smes and found that murabaha is an important and famous financing option among users of islamic finance. this study asserts that islamic finance is a critical driver of sme development in jordan. similarly, in 2022, a research study by abdinur and ondes shows via a bivariate regression analysis of 148 primary observations obtained from selected smes in lasanod, somalia that a higher level of islamic finance promotes firms’ business performance. at a macro level, ledhem (2022) assessed the impact of islamic finance for entrepreneurial business on the growth of the malaysian economy based on quarterly data from 2014 to 2021. it pa ge 35 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 32-40, 2025 was discovered with bootstrap quantile regression that islamic finance for smes promotes malaysian economic growth. in africa, scanty empirical evidence on the reliability of islamic finance for enhancing sme business performance is available. haruna et al. (2024) investigated how the islamic financing approach affects the innovation capacity of small-to-medium businesses in cameroon through a sample of 1358 sme owners and/or managers. a multivariate probit model estimation by the study reveals that islamic financing significantly and positively promotes product, process, and marketing innovation. the study implies that greater islamic financing enhances smes’ innovation capacity in cameroon. in nigeria, a few studies by adam (2020), adedeji (2021), and shinkafi et al. (2023) found that islamic financing has a significant and positive effect on the performance of the country’s smes. however, the common use of sensitive assumptions-based ordinary least square (ols) method to analyze primary data limits the extent of acceptability of results obtained by these scanty studies in the field. unless converted into continuous form, the assumption of linearity for categorical data distribution is often impractical via primary data. in other words, using ols for non-linear relationships can produce biased and unreliable estimates (gujarati, 2004; wooldridge, 2010). again, the heteroscedasticity in ols with primary data can lead to incorrect inferences (gujarati, 2004). errors are likely to vary substantially across all levels of predictors with categorical data. regrettably, identified issues with the ols technique for primary data analysis were overlooked by previous studies. additionally, past studies worldwide have ignored the estimation of the indirect effects of the islamic equity financing legal platform which provides the operational framework. meanwhile, successful stories of islamic financing in asian countries have been attributed to the efficient legal framework underpinned by islamic principles (hussain et al., 2015; united nations development programme and islamic development bank, 2023). therefore, a suitable method for handling primary data and a robust technique that accounts for heteroscedasticity is required. consistent with the gap, this study employs the structural equation modeling approach to estimate the reliability of islamic financing towards the growth of smes in nigeria with a specific focus on the abeokuta metropolis. the potential use of the sem technique is premised on the need to provide better efficient estimates and improve the precision of results on the relationship between islamic finance and sme growth in the study area. materials and methods the current study applies a quantitative survey research design. the use of the research strategy is informed by its ability to yield information about the study population via a sample. the study population are all small scale enterprises (sses) in abeokuta town, ogun state, nigeria. however, a specific focus was on sse owners and/or managers with at least secondary or post-primary education. unfortunately, due to poor (or lack) record keeping in developing countries like nigeria (mckenzie and sakho, 2010), it was not statistically easy to ascertain the precise population of educated sse owners and/or managers in the study area. consequently, the study utilizes krejcie and morgan’s (1970) sample size for an unknown population to determine the required sample size. the formula is specified as thus: s = ( ( range / 2 )2 ) / ( ( ( accuracy level ) / ( confidence level))2 (1) where; range = range of sses that have educated owners or managers (assumed to be between 10,000 firms and 100,000 firms) = 100,000 – 10,000 = 90,000 firms confidence level = 1.96 (2-tailed) at a 5% level of significance from equation 1, a sample size of 384 sses is obtained. however, the derived sample size is adjusted to control for at least a 75% response rate. this is necessary because the empirical evaluation of a large-scale islamic equity-based model requires a higher response for the eventual analysis outcome to be well accepted. therefore, equation (1) is adjusted in equation (2) as thus: s*= (obtained sample size)/(desired response rate) (2) from equation (2), s is 512 sses. these sses are represented by firm owners or managers where appropriate and were administered well-structured questionnaires. moreover, the study employs a systematic random sampling technique to select every 5th sse approached during the field survey. hitherto to the main analysis, a pilot study was conducted in eight business-populated areas in abeokuta to determine the appropriateness of the data instrument (structured questionnaire) and suitable respondents (sse owner/manager with at least secondary education) for main data administration. four (4) business districts (bds) were purposively selected from each of the two local government areas (lgas) in the metropolitan city of abeokuta – abeokuta south lga and abeokuta north lga. the business districts selected in abeokuta south lga include sapon itoku bd, asero adatan bd, isabo kuto bd, and oke-ilewo onikolobo bd. in abeokuta north, saje elega bd, lafenwa sabo bd, olomore ita-oshin bd, and sanni rounder bd were surveyed. with the assistance of four research assistants (ra), the respondents were met physically for face-to-face questionnaire administration from 4th march to 4th december 2024. furthermore, the study develops a covariance-based structural equation modelling (cv-sem) framework to ensure a better understanding of the reliability of islamic financing toward sse growth in nigeria using abeokuta town a frontier study area. cv-sem as a powerful technique helps to overcome measurement errors and specification issues in a complex study (kline, 2011). before the cv-sem modelling procedure, principal component analysis (pca) analyses were performed pa ge 36 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 32-40, 2025 to estimate index scores for the study predictors. pca is applied to derive a single value (index) for each of latent predictor through their respective observed variables. in the most recent time, studies in finance have presented evidence that the pca technique produces a thorough way of estimating index scores (chon, 2020; sun, wang, xu and balezentis, 2022). four correlated items were observed for each of the latent predictors. the pca equation for each predictor is specified thus: musscore = (3) where, musscore = musharakah index score; f = absolute factor loadings; x= set of observed variables for mus. dmuscore = (4) where, dmuscore= diminishing musharakah index score; f = absolute factor loadings; x= set of observed variables for dmu. mudscore= (5) where, mudscore= mudharabah index score; f = absolute factor loadings; x= set of observed variables for mud. the study cv-sem model is developed in figure 1 as thus: figure 1: cv-sem islamic equity-based financing model for nigerian sses growth figure 1 depicts hypothesised relationships between measures of islamic finance and sales growth of sse firms in the study area based on the assumptions of the dcm framework. from the diagram, ief (islamic equity finance legal platform) serves as an intervening variable which moderates the relationship between predictors of islamic equity financing and firm growth. consequently, two structural models are developed in equations (6) and (7) respectively. these are direct and indirect effects models. the equations are specified as thus. ssgi= α+β1musi+β2dmui+β3mudi+ε4 (6) (direct effects model) ssgi = α+β4iefi+ε4 (7)(indirect effects model) the main predictors (mus, dmu and mud) as latent variables are determined via the principal component analysis (pca). the dependent variable, ssg, is measured by: ssgi= ((st-s(t-1)))/st where, st=current sales level (in 2024) s(t-1)=sales three years ago (in 2021) a prior expectation from the study structural models, β1,…β3 and β4>0 the study structural models were analyzed using the maximum likelihood method of sem in stata 12.1 statistical software at a 5% significance level. results and discussion this sub-section presents the results from the maximum likelihood estimation of the cv-sem-based relationship between islamic equity financing (ief) and turnover growth of sse firms in the abeokuta metropolis. the cv-sem estimation was conducted using the stata 12.1 sem modeling framework and commands. table 1: cv-sem estimated sse growth impact of islamic equity financing (dv: ssg) effect coef. std. err. z-value prob. value (p>.05) direct effects structural mus <ief 0.62 0.06 9.74 0.000 dmu <ief 1.11 0.04 25.67 0.000 mud <ief 1.26 0.03 34.10 0.000 pa ge 37 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 32-40, 2025 in the preliminary analysis, pca analyses were performed to derive index scores for all the predictors. these continuous index scores have normal distribution (multivariate normality) properties, satisfying two important assumptions of cv-sem analysis. the derivation of index scores for the latent variables obviates the need to estimate measurement models. a simple descriptive analysis (mean, maximum and minimum values not shown due to space issues) of pca index scores depicts no incidence of extremely higher or lower values in the data distribution (that is, the absence of outliers). hence, pca provides plausible and parsimonious data scores for causal relationship estimations in sem analysis. the results of cv-sem estimations are presented in table followed by table 2 which showcases post-analysis diagnostic test statistics. from table 1, it is revealed that 449 observations out of coded 476 entries into the software were used for the final analysis. thus, this indicates a paltry 27 observations were dropped for the final analysis due to missing responses at random. the number of observations used for the analysis represents 87.7% of the study’s total sample size (512). however, for one reason or another, 56 administered questionnaires were not returned by the respondents when the data analysis took effect (december 4, 2024). the number of data (449) used illustrates that approximately 41 observations per 1 parameter are estimated in the sem analysis. in figure 1, 11 parameters were identified (7 regressions and 4 variances). the higher observations to parameters ratio shows the study sample adequacy (schreiber et al., 2006). more so, some fit indices (chisquare test – x2 -; rmsea; cfi and tli) as reflected in the table indicate better model fitness of the study pls-sem model developed in the methodology section. for instance, the result in table 2 highlights that the null hypothesis via chi-square (x2: p-value > 0.5) that there is a discrepancy between the baseline model and saturated model is accepted at a 5% significance level (bentler & bonett 1980; fan et al., 2016; mulaik et al., 1989; hu & bentler 1999; schreiber et al., 2006). other fit indices in table 2 such as rmsea (0.03), cfi (0.9750) and tli (0.932) are also above acceptable values (fan et al., 1999; fan et al., 2016; browne & cudeck, 1993; hu & bentler 1999; schreiber et al., 2006). for this reason, the researchers did not perform post-hoc model modification as the cv-sem model developed in the method section has good fitness for the observed data. in other words, the study inferences drawn from the two structural models are considered consistent, reliable and efficient. from figure 1, it is indicated that musharakah (mus: coef. = 0.62; p-value = 0.000), diminishing musharakah (dmu: coef. = 1.11; p-value = 0.000) and mudharabah (mud: coef. = 1.26; p-value = 0.00) are significant predictors of islamic equity financing at a 5% significance level. these results imply that these financing means are critical for designing, deploying and administrating islamic equity-based financing among small-scale firms in nigeria, particularly the abeokuta metropolis. mainly, direct effect estimation of equation (6) shows that musharakah (mus: coef. = 0.03; p-value = 0.003) and mudharabah (mud: coef. = 0.08; p-value = 0.00) have potentially positive and significant impacts on sales growth of sses in the study area. a unit increase in musharakah and mudharabah financing means sales / turnover of sses in the abeokuta metropolis will grow by 0.03% and 0.08% respectively. the path coefficients of the four predictors have dispersion levels not higher than 0.01 per cent. in terms of indirect effect estimation, table 1 reveals that islamic equity financing tools like musharakah, diminishing musharakah and mudharabah can significantly enhance the growth of sses in the abeokuta metropolis indirectly through islamic finance legal framework (ief: coef = 0.10; p-value = 0.000). interestingly, the positive and significant impact of musharakah obtained in this study is consistent with previous findings by shinkafi et al. (2023) who asserted that musharakah and mudharabah as finance tools influence smes growth in nigeria. similarly, this study’s finding that mudharabah will promote business growth is similar to results obtained by shamsudheen et al. (2023), baloch ssg <mus 0.03 0.01 2.94 0.003 dmu -0.01 0.01 -0.65 0.513 mud 0.08 0.01 5.50 0.000 ief 1.42e-07 indirect effects ssg <-ief 0.10 0.01 6.35 0.000 no. of obs. 449 method: maximum likelihood 1840.95 source: authors’ computations from stata 12.1 outputs (2024) table 2: post-diagnostics tests test statistic log likelihood -1840.9498 lr test of model vs. saturated 14353.13 (prob. > chi2 = 0.4330) lr test of baseline vs. saturated 15502.31 (prob. > chi2 = 0.7840) rmsea 0.03 comparative fit index (cfi) 0.9750 tucker-lewis index (tli) 0.932 source: authors’ computations from stata 12.1 outputs (2024) pa ge 38 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 32-40, 2025 and chimenya (2023), and abdinur and ondes (2022). in contrast, when diminishing musharakah funding (dmu: coef. = 0.01; p-value = 0.513) increases by 1 unit, the sales level of sses falls by 0.01 per cent leading to a lack of business growth. however, the result further shows that the negative impact of diminishing musharakah (dmu: p-value >.05) is insignificant in slowing down the operations of the sampled firms. this insignificant negative impact of diminishing musharakah illustrates that a financing partnership arrangement that will fully transfer total equity interest to capital providers in future is less important to sse’s operational performance. as obtained, the indirect effects of islamic equity financing will be more pronounced in nigeria than the individual direct effect offered by the studied financing tools. this result highlights the importance of developing an efficient legal framework for operations of islamic equity financing for enhancing the operational performance of smes particularly sses in nigeria. overall, it is learnt through the observed empirical evidence that reducing the financing gap faced by micro, small and medium enterprises in nigeria particularly in the study area (abeokuta metropolis) requires strategic interventions on islamic equity financing alternatives. first, banks and other lending institutions in nigeria need to design and directly offer sse-targeted islamic equity financing based on the principles of musharakah or mudharabah or both alternatives. businesses need time to grow and using any of these equity finance alternatives can guarantee them the confidence to operate for longer periods as fear of running into debts or paying high-interest charges on bank loans is allayed. finally, the result shows that strengthening islamic equity-based finance in nigeria through a legal framework is essential and provides greater benefit to help sses grow and generate more employment. implications of the study this study obtains empirical evidence that islamic equity finance’s indirect positive and significant impact through a legal framework (mediating factor) is stronger for developing small firms than the direct impact. two implications of the study are provided. first, the study shows that analysis of both the direct and indirect effects of islamic equity finance is particularly important for designing and deploying appropriate islamic financial policies that benefit small business operations in nigeria. this effort will enable stakeholders in the country’s financial industry to understand the roles of risk-sharing financing initiatives that support small-scale businesses’ operational capacity. second, the use of the pca technique to estimate data scores for latent variables (musharakah, diminishing musharakah and mudharabah) in the current study provides a methodological and analytical guide to experts and future studies and consider pca as a plausible alternative to confirmatory factor analysis (cfa). however, an alternative sem method that provides greater predictive ability and is less sensitive to the sample size issue (e.g. pls-sem) would have ensured optimal prediction of islamic equity-based financing reliability toward higher growth of small businesses in the study area. therefore, future studies in this direction are encouraged to use large-scale data for cv-sem evaluations or apply more efficient sem methods. notwithstanding, the results obtained in this study are valid and reliable, particularly with the use of pca in place of measurement model estimation. conclusion this study applies the maximum likelihood method of cv-sem to estimate the causal relationship between islamic equity-based financing and the growth of small businesses in nigeria with a specific focus on the abeokuta metropolis. the study affirms that the expansion of small enterprises in the study area will be directly, positively, and significantly influenced by islamic shared finance (musharakah) and islamic joint partnership (mudharabah). however, the indirect positive and significant impact of islamic equity finance through a legal framework (mediating factor) is stronger for the development of small firms than the direct impact. on this account, the study recommends that the federal government of nigeria should institute a suitable legal framework for islamic equity financing to enhance the operational capacity of small-scale businesses. again, private and institutional investors are encouraged to promote investments in islamic banking. for administrators of islamic finance in nigeria, greater efforts are required to provide musharakah and mudharabah equity financing facilities. finally, users of islamic equity finance, particularly sse owners and managers, should embrace these alternative finance models. acknowledgements this work was supported by the nigeria tertiary education trust fund (tetfund) institution-based research (ibr) grant 2023 (tetf/dr&d/ce/poly/ ilaro/ibr/2023/vol1) references abdinur, m. a., & ondes, t. 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(2010). econometric analysis of cross section and panel data (2nd ed.). mit press. pa ge 40 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 32-40, 2025 world bank. (2023). small and medium enterprises (smes) finance. understanding poverty. retrieved from https://www.worldbank.org/en/topic/smefinance world bank & islamic development bank. (2015). leveraging islamic finance for small and medium enterprises (smes). world bank global islamic finance development center. pa ge 1 pa ge 10 8 american journal of applied statistics and economics (ajase) forecasting key macroeconomic indicators in ghana using a time-varying vecm with conformal prediction intervals chinton emmanuel1*, donkoh kojo isaac2, acquah oware nana emmanuel3 volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.5752 https://journals.e-palli.com/home/index.php/ajase article information abstract received: july 20, 2025 accepted: august 22, 2025 published: september 29, 2025 this paper presents a six-month-ahead forecast of three key ghanaian macroeconomic indicators: the usd/ghs exchange rate, the consumer price index (cpi) and the monetary policy rate (mpr). a time-varying vector error correction model (tv-vecm) is utilized to capture dynamic interrelationships among the variables. conformal prediction intervals are incorporated to quantify uncertainty under minimal distributional assumptions. the results suggest moderate currency depreciation, persistent inflationary trends and stability in nominal interest rates over the forecast horizon. keywords conformal prediction, exchange rate forecasting, ghana, inflation, monetary policy, time-varying vecm 1 department of statistics, university of cape coast, ghana 2 financial engineering, worldquant university,, usa 3 department of economics and finance, youngstown state university, usa * corresponding author’s e-mail: emmanuelchinton7@gmail.com introduction forecasting macroeconomic indicators is essential for effective policy design and economic planning. this study employs a time-varying vector error correction model (tv-vecm) to analyze short-term movements in three critical ghanaian macroeconomic variables: the usd/ ghs exchange rate, the consumer price index (cpi) and the monetary policy rate (mpr). the tv-vecm framework captures both long-run equilibrium and evolving short-term dynamics. to account for forecast uncertainty, conformal prediction intervals are utilized which offer valid coverage without assuming specific error distributions. literature review time-varying vecm (tv-vecm) for ghana macroeconomic relationships in ghana among inflation, the cedi/us$ rate, policy rates, money, output, and commodity prices are well known to be non-stationary with evolving long-run equilibria and shifting short-run dynamics (policy regime shifts, commodity price cycles, imf programs, and disinflation episodes). standard (time-invariant) var/vecm studies on ghana capture cointegration and error-correction but assume fixed parameters, which can miss structural drifts that matter for forecasting and policy analysis. using a time-varying cointegration framework lets the data accommodate gradual changes in adjustment speeds or even the cointegration vector itself exactly the type of flexibility needed in an economy that has seen alternating easing cycles and large disinflation in 2024–2025. core advances on time-varying cointegration and vecm early contributions showed how cointegration can evolve smoothly and proposed tests for time-invariance of the long-run vector (and rank). bierens & martins (2009/2010) formalize a time-varying cointegration setup where the cointegrating relationship changes smoothly; they derive a likelihood-ratio test against time-variation. this line of work motivates allowing βt and even αt to drift in a vecm. a cautionary strand highlights pitfalls with state-space/ kalman estimation of time-varying cointegration: if not handled carefully, the kalman filter can absorb unit-root behavior into the time-varying state and spuriously “find” time-varying cointegration between unrelated i(1) series. robust procedures and bootstrap testing frameworks are proposed to distinguish no cointegration vs. fixed vs. time-varying cointegration. this is highly relevant if one estimates tv-vecms for ghana with state-space methods. recent econometric theory pushes further with smoothly time-varying vecms: gao, peng & yan (2023, 2025) develop a time-varying granger representation theorem, estimation/inference for both short-run and long-run coefficients, a singular-value-ratio rank test, and stability tests providing a principled toolkit to build and validate tv-vecms without resorting solely to ad-hoc filters. related strands include threshold/smooth-transition vecms (to handle nonlinear adjustment) and applications showing that allowing for time-variation materially changes conclusions about long-run relations. these reinforce the empirical gains from flexible cointegration structures. pa ge 10 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 108-111, 2025 estimation strategies for tv-vecms three broad routes appear in the literature: state-space / kalman tvp-vecms (or tvp-vars with cointegration) flexible but require care to avoid spurious cointegration; bootstrap or robust testing is recommended. nonparametric/smoothly-varying coefficients treat α(τ), π(τ) as smooth functions of rescaled time τ with associated theory for rank, stability, and inference; this avoids some kalman pitfalls and aligns with gradual ghanaian regime shifts. bayesian tvp-var/cointegration frameworks (and sparsity priors) common in macro; while much of this work is in tvpvars, ideas translate to vecms (shrinkage on timevariation, stochastic volatility), though dedicated bayesian tv-vecm software remains less standard. forecast uncertainty classical vecm forecast intervals rely on parametric assumptions (gaussian errors, correct specification). in volatile, shifting environments like ghana’s, distributionfree uncertainty quantification is attractive. conformal prediction (cp) provides finite-sample, model-agnostic prediction sets with marginal coverage guarantees. for time series, adaptive conformal inference (aci) and related online methods adjust interval widths to distribution shift and non-exchangeability, which are precisely the challenges in macro data with evolving regimes. key extensions handle multi-step and multivariate timeseries forecasting crucial when producing joint paths for inflation, fx, and policy rates or when reporting horizon-h tv-vecm forecasts. recent work develops online/multi-step cp with provable properties, and multivariate cp to form valid prediction regions across series. an alternative, enbpi (ensemble batch prediction intervals), uses ensemble residuals to deliver approximately valid intervals under dependence and shift; while often paired with ml forecasters, it is model-agnostic and can calibrate vecm residuals too. tv-vecm and conformal pipeline for ghana the literature jointly suggests: specify a ghana macro system [ pt, et, it, yt, mt] inflation, exchange rate, policy rate, output proxy, money; optionally commodity/terms-of-trade). estimate cointegration and time-variation using a smoothly time-varying vecm (gao–peng–yan framework) or carefully designed state-space tv-vecm with robust cointegration testing/bootstrapping to guard against spurious time-variation (eroğlu et al., 2022). forecast multi-step paths from the tv-vec wrap forecasts with cp use online aci (or variants) for oneand multi-step horizons; for multiple variables/horizons, apply recent multivariate/multistep conformal methods to obtain valid joint or per-horizon intervals that adapt to regime changes particularly important around policy turning points documented for ghana in 2025. empirical expectations and gaps relative to fixed-parameter vecms used in many country studies, a tv-vecm should improve calibration during regime shifts and commodity shocks, and better capture changing error-correction speeds. conformal layers are complementary to econometric inference: they provide finite-sample predictive coverage without re-specifying the tv-vecm and remain robust to mild misspecification. gap: few (if any) studies combine tv-vecm with conformal intervals in macroeconomic practice especially for ghana. the emerging multi-step/multivariate cp literature now makes this feasible and methodologically justified. materials and methods data description monthly data from january 2014 to june 2025 were compiled. cpi data were obtained from the ghana statistical service, while exchange rate and mpr data were sourced from the bank of ghana. all series were tested for unit roots using the augmented dickey-fuller (adf) test and found to be integrated of order one, i(1). cointegration was verified using johansen’s method, confirming at least one cointegrating relationship among the variables. the adf test is based on the following regression: (1) the johansen cointegration test is derived from the vector autoregression (var) representation: (2) where the rank of the matrix π determines the number of cointegrating relationships. model specification a time-varying vecm was implemented using a rolling window approach. this technique allows model parameters to evolve, accommodating structural breaks and time-varying relationships. the model captures both equilibrium correction mechanisms and short-term adjustments. forecasting and conformal prediction a six-month forecast horizon was selected. to quantify pa ge 11 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 108-111, 2025 forecast uncertainty, conformal prediction intervals were constructed using residual-based nonconformity scores. these intervals maintain valid coverage under mild assumptions and are robust to model misspecification. gradient boosting regression was employed to generate point forecasts. this method builds an ensemble of decision trees in a forward stage-wise fashion, minimizing a loss function by iteratively fitting residuals: (3) (4) (5) results and discussion table 1: forecast and 90% conformal prediction intervals date usd/ghs [pi] cpi [pi] mpr [pi] 2025-07-31 10.4600 [10.12, 10.90] 258.87 [254.09, 261.07] 28.00 [28.00, 28.05] 2025-08-31 10.5900 [10.19, 10.93] 261.36 [260.11, 262.67] 28.00 [28.00, 28.01] 2025-09-30 10.7100 [10.42, 11.07] 264.56 [262.61, 265.19] 28.00 [28.00, 28.01] 2025-10-31 10.8400 [10.55, 11.21] 268.25 [265.82, 268.43] 28.00 [28.00, 28.01] 2025-11-30 10.9700 [10.67, 11.35] 272.09 [269.53, 272.18] 28.00 [28.00, 28.01] 2025-12-31 11.1000 [10.80, 11.48] 275.99 [273.39, 276.08] 28.00 [28.00, 28.01] source: authors computation and projections figure 1: residual plot for ln cpi, mpr, and ln usdghs (july–december 2025) figure 2: autocorrelation function (acf) plot for residuals pa ge 11 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 108-111, 2025 residual plot observations • ln_cpi: residuals are centered around zero with low variance but show a slight increase in variance from 2022 onward. • mpr: high residual variance with frequent spikes, suggesting potential outliers or structural breaks. • ln_usdghs: residuals are mostly stable over time, with a few sharp spikes between 2022 and 2023 likely reflecting volatility during that period. autocorrelation function (acf) observations • ln_cpi residuals: all spikes are within the 95% confidence bands across lags 0 to 10, confirming the absence of significant autocorrelation. • mpr residuals: a visible spike at lag 2 exceeds the confidence bounds, indicating residual autocorrelation. • ln_usdghs residuals: all spikes remain within the confidence bounds, confirming white noise residuals. discussion usd/ghs exchange rate the forecasted exchange rate path indicates a gradual depreciation of the ghanaian cedi, rising from 10.46 to 11.10 over the six-month period. the widening prediction intervals over time reflect increasing uncertainty as the forecast horizon extends. consumer price index cpi is projected to increase gradually from 258.87 to 275.99, suggesting continued inflationary pressure. the forecast intervals remain tight, indicating strong model confidence in the inflation trajectory over the horizon. monetary policy rate the mpr remains effectively stable at around 28.00% across the entire forecast window. the extremely narrow interval bounds suggest little model uncertainty and high temporal persistence in this rate. model pitfalls and deployment from the estimated parameters value and diagnostic plots, we observed that monetary policy rate residuals show strong autocorrelation and large spikes especially after 2022, confirming that the model does not capture mpr dynamics well. this suggests the presence of possible outliers, structural breaks after 2022, and insufficient lags. therefore, the model can be deployed to forecast only the consumer price index (cpi) and usd/ghs. the long-run links look usable, but mpr short-term forecasts may not be reliable unless the mpr pitfalls are fixed and improved. conclusion this study utilizes a time-varying vecm with conformal prediction to forecast ghana’s key macroeconomic indicators over a six-month horizon. results suggest moderate exchange rate depreciation, persistent inflation, and stability in nominal interest rates. these forecasts are grounded in historical data patterns and subject to limitations arising from policy shocks or structural changes outside the model’s framework. references angelopoulos, a. n., candès, e., & tibshirani, r. j. (2023). conformal pid control for time series prediction. (online cp that adapts to trend/seasonality). bierens, h. j., & martins, l. f. (2010). time-varying cointegration. cambridge/et. (smoothly timevarying cointegration, lr test). eroğlu, b. a., miller, j. i., & yiğit, t. (2022). time-varying cointegration and the kalman filter. econometric reviews, 41(1), 1-21. gao, j., peng, b., & yan, y. (2023/2025). time-varying vector error-correction models: estimation and inference. (tv-vecm theory and methods). gibbs, i. & candès, e. (2021/2024). adaptive conformal inference under distribution shift. (online cp with distribution shift). journal of machine learning research hallberg-szabadváry, j. (2024). adaptive ci for multi-step ts (online). (h-step cp with guarantees). hansen & seo; smooth/threshold vecm (overview). kdi journal (2021). time-varying cointegration models and exchange rate (ppp and monetary models pass when allowing time-variation). kdi journal of economic policy kopetzki, s. (2025). conformal multistep-ahead multivariate ts forecasting. (joint multivariate intervals). reuters (2025). ghana policy rate moves (hike; record cut) in 2025. (motivation for time-variation) stankevičiūtė, g. (2021). conformal time-series forecasting. (multi-horizon cp for ts). xu, c., & xie, y. (2021). conformal prediction interval for dynamic time-series (enbpi). (ensemble cp under dependence). pa ge 1 pa ge 41 american journal of applied statistics and economics (ajase) forecasting nigeria’s oil price volatility: a comparative analysis of garch models and heston’s stochastic models omorogbe joseph asemota1, mustapha bello2, samuel olorunfemi adams2* volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.4693 https://journals.e-palli.com/home/index.php/ajase article information abstract received: march 10, 2025 accepted: april 16, 2025 published: june 04, 2025 modeling the volatility of crude oil prices is essential because it gives substantial influence to the oil producing countries. nigeria, the biggest oil producer in africa and a major participant in the world oil market, has significant economic difficulties changes in oil prices. this study uses 14 years of crude oil price data (2010–2023) to assess and compare the forecasting effectiveness of the heston stochastic volatility model and garch-type models (garch, egarch, igarch, tgarch, and figarch). according to the analysis, garch-type models with student’s t-distribution perform better than models with typical innovation. with a log-likelihood value of 12022.3, an aic of -4.7012, a mean error (me) of 0.0254, and a root mean square error (rmse) of 0.0534, the egarch model outperformed the others. nonetheless, the heston model outperformed all garch-type models in terms of forecast accuracy, achieving the smallest error (0.000564) and successfully capturing fat-tail characteristics in daily return distributions. the study indicates that the heston model offers a better fit and more accurate forecast than garch-type models using data from january to december 2023 for out-of-sample forecasting. these results provide stakeholders and policymakers with important information for controlling the volatility of nigeria’s crude oil market. keywords egarch, figarch, garch, heston model, igarch, symmetric, tgarch 1 national institute for legislative studies, abuja, nigeria 2 department of statistics, faculty of science, university of abuja, abuja, nigeria * corresponding author’s e-mail: samuel.adams@uniabuja.edu.ng introduction one of the most important energy sources in the world is petroleum, a fossil fuel that was created over millions of years from the remains of marine plants and animals. it is known as “crude oil” in its natural state. crucially, crude oil serves as a raw material and a necessary energy source (sekati et al., 2020; yi et al., 2021; dunn & holloway, 2012). common forms of oil that power cars, ships, and airplanes include heating oil, diesel, motor gasoline, and jet fuel. oil is used in the production and transportation of many commonplace goods, and the industry that produces it has a big impact on other industries. production expenses and the state of the economy as a whole are significantly impacted by changes in the price of petroleum products (fondo et al., 2021). changes in oil prices impact many aspects of society, including household appliances, detergents, prescription medications, and food supplies. like any commodity, oil prices fluctuate in response to supply and demand, which can have a favorable or negative effect on a number of economic sectors (gasper & mbwambo, 2023). crude oil is therefore still a major economic issue and a hot topic in discussions about international economic policy. it is clear from the last three decades that the housing bubblerelated financial crisis had a negative effect on oil prices, which fell from us$133.88 per barrel in june 2008 to less than $40 per barrel in the months after the disaster. following that, prices rose to $100 in 2014 before sharply falling to $30 in 2016 as a result of an increase in the supply of crude oil. the start of the covid-19 pandemic made matters worse and caused prices to drop to $16.55, the lowest level in 20 years, in april 2020. however, prices saw another increase as a result of the events in ukraine. countries that rely significantly on crude oil are surely impacted by these global changes in crude oil prices (rodhan, 2023). the extraction and sale of crude oil is nigeria’s main source of revenue. following years of exploration that started in 1938, shell d’arcy, now known as shell petroleum company, made the first commercial oil discovery in 1956 near oloibiri in bayelsa state, according to the nigerian national petroleum corporation (nnpc, 2013). with its first oil field going online in 1958 and producing 5,100 barrels per day, nigeria’s abundance of oil won it a prominent place in the world market. foreign businesses were then allowed to explore for oil in nigeria, which resulted in the oil industry’s steady expansion and made nigeria a world leader. nigeria currently produces the most oil in africa, accounting for 33–35 percent of the continent’s oil and gas reserves, or 1.347 million barrels a day. it is the fifthlargest oil exporter to the united states of america and the fifth-largest exporter in the organization of petroleum exporting countries (opec) (mary, 2023). later, in 1977, the nigerian national petroleum corporation (nnpc) was established with the intention of overseeing and taking part in the nation’s oil industry (nwokeji, 2007). the nation had 36.966 billion barrels of oil and condensate reserves in 2023. this amounts to 5.906 billion barrels of condensate and 31.060 billion barrels of oil. nigeria produces more than 1.5 million barrels of oil per day, with a total oil reserve of 37.064 billion barrels (nuprc, 2023). nigeria ranked 11th on the pa ge 42 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 list of nations with oil reserves that exceed one billion barrels (38.6%), with about 37 million barrels of known oil reserves. nigeria produced over 1.93 million barrels of oil per day and exported 85% of its oil production, placing it 15th in the globe on the list of nations that produce oil that year (john, 2023). in order to diversify the economy, increase the domestic market, and lessen an excessive reliance on crude oil exports, nigeria has taken a number of actions to reform the energy sector, including the petroleum industry act in 2021 and the elimination of subsidies in 2023. the nigerian government raised the price of petrol at the pump from the regulated price of n185 to more than n700 per liter in may 2023 and announced the elimination of the fuel subsidy. an unusual protest against the government’s decision to raise the price of fuel pumps was sparked by this rise. the elimination of the premium motor spirit (pms) gasoline subsidy is one of the most controversial topics in nigeria right now (odewale, 2023). the subsidy is a type of price manipulation in which the government sets the pump price for consumers to purchase and reimburses the store for the difference between the official or regulated price per liter and the actual market price. subsidies, in my opinion, are the additional funds that the government spent or incurred in order to lower the price of pms pumps for those with lower incomes. nigeria’s level of living will be significantly impacted by the elimination of the petroleum subsidy, particularly for the already underprivileged populations. the price of gasoline pumps has increased as a result of the elimination of the petroleum subsidies, raising the expense of food, transportation, and other necessities. when compared year-over-year, the food inflation rate in may 2023 was 24.82%, 5.33% higher than the rate in may 2022 (19.50%). the transportation and storage industry, which contributes roughly 0.89% of the gdp, saw the biggest fall of any sector in the second quarter of 2023, contracting by an astounding 50.64%. the average fare passengers paid for bus trips within the city each drop rose by 97.88% from n649.59 in may 2023 to n1,285.41 in june 2023, according to data from the nbs transportation watch. it increased by 120.63% year over year from n583 in june 2022 (nbs, 2023). for nigerians, floating the country’s currency has socioeconomic ramifications as well, hurting residents’ purchasing power and their capacity to pay for necessities. strong social safety nets, focused interventions, and transitionprotective legislation would be necessary to mitigate these consequences. low-income families and individuals’ budgets are strained as a result, which lowers their level of living. additionally, rising gasoline prices have caused inflationary pressures throughout the economy, raising the cost of products and services. people with fixed incomes are disproportionately affected by inflation, which makes poverty worse. low-income people bear a disproportionate amount of the burden of rising living expenses and petroleum pump prices, which exacerbates the nation’s income inequality and widens the income gap (yakubu et al., 2023). it is commonly acknowledged that the fluctuations in oil prices have a substantial impact on economic activity. commodity market price fluctuations are frequently influenced by changes in oil prices, which can cause economic slowdowns and price swings for other commodities when oil prices abruptly rise or fall. accordingly, predicting the price of crude oil is an important field of study, although it faces inherent challenges including excessive volatility (wang et al., 2004). while limited liquidity and occasional trade in imperfect markets may cause a delay in responding to new information, oil prices may not always react immediately to it (monoyios & sarno 2002). according to this viewpoint, a substantial body of research has been done on enhancing econometric models’ capacity to simulate oil prices. a portion of the research uses different garch models to examine the trajectory of oil prices. alessandri and mumtaz (2019), popp and zhang (2016), adams et al. (2024), and van robays (2016) are only a few of the research that have demonstrated the substantial influence of economic factors on rising volatility, particularly during times of regime transition. the forecasting skills of single-regime models are significantly diminished by regime shifts, which are influenced by a variety of economic factors. additionally, by upsetting the trends shown in economic time series, economic considerations have a major impact on business cycles. for example, the performance of econometric models was significantly impacted by the oil crises of 1974 and 1979. because of this, conventional volatility models that do not consider regime-switching features, including those brought on by oil shocks, are no longer sufficient to simulate volatility in gasoline prices. garch-type models (ahmed & shabri, 2014; wacuka ng’ang’a & oleche, 2022; adams & bello, 2022; deebom & essi, 2017), support vector machines (svm), which forecast data with high volatility (okasha, 2014), and autoregressive integrated moving average (arima), also known as the box-jenkins methodology (shambulingappa et al., 2020; awujola et al., 2015; rodhan & jaaz, 2022) are some of the analytical techniques that have gained a lot of attention recently in crude oil forecasting. since the symmetric models of the garch family are better at predicting the price of crude oil, they are regarded as fitted models (haque et al., 2021; arachchi, 2018; herrera et al., 2018). they are seen as crucial for figuring out how volatile various commodities are (charles & darné, 2021). by taking into consideration the effects of leverage, volatility clustering, and leptokurtosis in the time series analysis, the gjr-garch model further sets itself apart from other forecasting models. furthermore, it is discovered that both symmetric and asymmetric models are successful in capturing volatility (ekong & onye, 2017). a crucial part of many financial decision-making procedures is the examination of financial time series volatility. building less risky portfolios, maximizing asset allocation, and increasing returns all depend on accurate volatility forecasts. as a result, pa ge 43 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 accurate volatility forecasting and analysis have become more crucial in recent years. the best choice of volatility models, however, is hotly debated, which presents a problem for researchers because the choice has an immediate effect on their findings. in order to solve the problem of volatility forecasting, this paper suggests using stochastic models and garch-type models. this study intends to add to the continuing discussion on the best techniques for volatility analysis in financial time series by assessing several risk models. a comparison of the forecasting capabilities of various garch-type models and the heston stochastic volatility model was also presented in the study. literature review numerous econometric models have been used to forecast the volatility of crude oil prices. with an emphasis on garch-type models, regime-switching models, arima models, and the macroeconomic ramifications of oil price volatility, this study looks at the main approaches and conclusions from earlier studies. garch-type models for forecasting oil price volatility crude oil price volatility has been widely modeled and predicted using garch models and its extensions. saltik et al. (2016) used the garch, igarch, gjr-garch, egarch, figarch, and fiaparch models to examine the return volatility of henry hub natural gas and wti crude oil over various time periods. according to their research, asymmetric and integrated garch models outperformed ordinary garch models in terms of forecast accuracy. in particular, according to mean square error (mse) and mean absolute error (mae) criteria, figarch under skew student-t performed best for one period, whereas egarch under the generalized error distribution was optimal for another. similarly, herrera et al. (2018) discovered that because of the strong kurtosis in oil returns, models with a student-t distribution outperformed those with a normal distribution. they came to the conclusion that egarch(1,1) was better for medium-term forecasting, whereas garch(1,1) and riskmetrics models performed best for short-term projections. kutu and ngalawa (2017) confirmed a significant negative influence of oil price shocks on the south african currency rate using the egarch (1,1) model. several autoregressive models were used in other research, including agnolucci (2009) and ramzan et al. (2012), to confirm the persistence of volatility in oil return series. the necessity of proactive monetary policy interventions was highlighted by fasanya and adekoya (2017), who evaluated symmetric (garch, garch-m) and asymmetric (egarch, tgarch) models and concluded that egarch was the most suitable for simulating inflation volatility. regime-switching and alternative volatility models garch models have been investigated in a number of studies to account for structural shifts in the volatility of oil prices. zhang et al. (2019) looked at both regimeswitching and single-regime garch models and found that simpler single-regime models frequently performed better than more intricate regime-switching models. to account for long-range dependence in financial time series, li et al. (2013) developed the mixture memory garch (mm-garch) model, which combined the conventional garch and figarch models. according to klein and walther (2016), mm-garch models performed better in variance and value-at-risk forecasting than conventional garch models. an empirical investigation contrasting the mrs-garch (markov regime-switching garch) model with conventional garch models was carried out by zhang et al. (2019). their results indicated that whereas regime-switching models improved in-sample estimates, their out-of-sample forecasting performance was not always enhanced. the significance of mean equation optimality was further highlighted by hasanov et al. (2020), who showed that garch models with optimal mean equations generated better predictions. arima models in crude oil price forecasting forecasting has made extensive use of arima models in addition to garch-type models. when selvi et al. (2018) used arima to anticipate crude oil prices from 2017 to 2021, they found that prices would continue to grow, highlighting the necessity of price stability measures. in line with selvi et al. (2018), shah & kiruthiga (2020) determined that arima (0,1,4) was the best model for predicting crude oil prices. similarly, rodhan & jaaz (2022) discovered that arima (1,1,4) produced the most accurate forecasts after examining 375 months of wti crude oil price data. macroeconomic implications of oil price volatility one important topic of study has been how the volatility of crude oil prices affects macroeconomic factors. using the arch, garch, and egarch models, sekati et al. (2020) investigated how south africa’s gdp, inflation rate, and currency rates affected the price of oil globally. according to their findings, a 1% increase in each indicator had a varied impact on oil prices, with gdp and exchange rates having a positive effect and inflation having a negative one. these findings, however, were in contrast to those of kutu and ngalawa (2017), who discovered that shocks to the price of crude oil had a negative impact on exchange rates. numerous studies emphasize the detrimental consequences of volatility in the price of crude oil on economic stability, especially in developing nations. according to yildirim (2017) and demirer et al. (2018), living standards are adversely affected by ongoing changes in the price of oil. crude oil prices are more volatile than those of other non-financial assets, which adds to economic uncertainty, according to adelman (2000) and lipsky (2009). in their analysis of oil price volatility from an investing standpoint, liu et al. (2022) concluded that no single model consistently pa ge 44 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 outperformed others, supporting the use of a variety of models to increase forecasting accuracy. according to the literature evaluation, asymmetric and integrated garch models outperform symmetric models in terms of forecasting the volatility of crude oil prices. regimeswitching models may not always increase predicting accuracy, although they do show promise for in-sample analysis. while macroeconomic models highlight the wider economic ramifications of oil price volatility, arima models are nevertheless useful for short-term price forecasting. materials and methods data description and source the central bank of nigeria website provided daily crude oil prices from january 2010 to december 2023 for modeling and forecasting purposes. because brent crude oil is regarded as the standard for crude oils in europe and africa, this information was used. another way that crude oil is exchanged is either on its own or in relation to other forms of crude oil. another factor in this choice was the data’s accessibility during the specified time window. continuously compounded daily stock returns are fitted to conditional variance models. yt =100(lnktlnk(t-1)) (1) where kt = current period of stock market exchange, k(t1)= previous period stock market exchange, yt= current period stock returns (stock market exchange -rt), and ω(t-1)= all stock returns up to the immediate past. model’s description the models used to estimate the volatility of crude oil prices are introduced in this section. the features of the historical crude oil spot price data are used to establish the modeling approach that is used. the best models of price volatility are not universally agreed upon since energy prices have complicated characteristics. the following are the procedures used to model the volatility: examine past data to determine its characteristics; verify if the observations are normal. verify the series’ stationarity and look for arch effects; the lagrange multiplier test is used to find out whether arch (autoregressive integrated moving-average) effects are present, and the augmented dickey-fuller (adf) and philips perron (pp) tests are used to check for stationarity. describe the estimate processes for the five garch type models and the heston stochastic model that were utilized in this work to model and forecast the price and return of crude oil. the best-fitting model is then selected by comparing the results with the predicting outcomes. garch-type models engle (1982) created the fundamental concept of the auto regressive conditional heteroskedasticity (arch) model in his groundbreaking study. in the literature, the arch model and its later generalized versions are widely recognized for their capacity to capture the most significant stylized facts found in all volatility measures (e.g., squared log-returns, absolute log-returns, etc.), such as clustering effects, long-memory and short-memory effects, and asymmetric leverage effects. five distinct garch models that were employed in this study are presented below. models of volatility the family of autoregressive conditional heteroskedasticity (arch) models. every arch or garch family model requires two distinct specifications: the mean and variance equations. according to engel, conditional heteroskedasticity in a return series, can be modeled using arch model expressing the mean equation in the form: yt = e(t-1) (yt )+ εt (2) such that εt = φt σt equation 2 is the mean equation which also applies to other garch family model. e(t-1) is expectation conditional on information available at time t-1, εt is error generated from the mean equation at time t and φt is a sequence of independent, identically distributed (iid) random variables with zero mean and unit variance. e {εt⁄ω(t-1)}=0; and σ2 t= {(ε2 t)⁄ω(t-1)} is a nontrivial positive valued parametric function of ω(t-1). the variance equation for an arch model of order q is given as: σ2 t = α0+ ∑q (i=1) αi ε 2 (t-1) + μt (3) where α0 > 0, αi ≥ 0, i=1,…,q, and αq >0 in practical application of arch (q) model, the decay rate is usually more rapid than what actually applies to financial time series data. to account for this, the order of the arch must be at maximum, a process that is strenuous and more cumbersome. the unconditional kurtosis of arch (1) suppose the innovations are normal, then e(at 4| ft-1) = 3[ e(at 2 | ft-1) ] 2 (4) = 3(α0+α1at-1 2 )2, it follows that eat 4 = 3α0 2 (1 +α1 ) / [( 1 -α1 ) ( 1 -3α1 2 )] (5) and eat 4 /(eat2 ) 2 = 3 ( 1 -α1 2 )/ ( 1 -3α1 2 )>3 (6) this shows that the tail distribution of at is heavier than that of a normal distribution. generalized arch (garch) model the conditional variance for garch (p, q) model is expressed generally as: (7) where p is the order of the garch terms, and q is the order of the arch terms, ε2. where β0>0, αi ≥0, i=1,…, q-1, j=1,…, p-1 and βp, αq >0. σ2 t is the conditional variance and ε2 t, disturbance term. the reduced form of equation 3 is the garch (1, 1) represented as: σ2 t= β0 + β1 ε 2 (t-1)+ β2 σ2 (t-1) (8) the three parameters (β0 , β1 and β2) are nonnegative and β1 + β2 <1 to achieve stationartiy. pa ge 45 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 egarch model a different that also captures the leverage is the exponential garch model or egarch (9) a lower aic means a model is considered to be closer to the true model. the loglikelihood is used to select the best model for estimation and forecasting. the higher the loglikelihood, the better the model. besides this, the information criteria are also used to pick the model. a good model had the highest loglikelihood or the lowest information criteria. therefore, a higher log likelihood translates to a low information criterion. the information criteria used in this study are the akaike, bayes, shibata and hannan-quinn. forecasting performance of the five models is analysed by comparing the errors i.e., comparing the forecasted returns with realized returns. this is done by comparing the mean error (me), mean absolute error (mae) and root mean square error (rmse). the lesser the errors the better the more accurate the model is in forecasting correct return for brent crude oil. me = 1/n ∑n (j=1) (yi-y *) (19) mae = 1/n ∑n (j=1) |(yi-y *)| (20) rmse = √(1/n ∑n (j=1) ((yi-y *))2) (21) results and discussions the 5112 data observations for the oil price volatility from january 2010 to december 2023 are the main emphasis of this section. to improve the accuracy of the data analysis, some processes are looked at. as can be seen from the graphing of data behavior with extensive mobility, the first result indicates that the series is not stationary. plot figure 1 shows the energy data for the daily price of crude oil in nigeria plotted against time. the graphic shows that fluctuations in crude oil prices show clustered volatility with sporadic surges and spikes. the crude oil price plot indicates that the price of crude oil is not regularly distributed during the given period. the recession may have caused the decline in oil prices in 2016. the significant decline in oil prices earlier in 2020, particularly from march 2020 to april 2021, which was exacerbated by the covid-19 outbreak, must also be noted. at this time, the corona virus had infected the which displays the usual leverage effect if αφ<0. the egarch model has the advantage that the logarithmic specification ensures that variance is always positive, but it has the disadvantage that the future expected variance beyond one period cannot be calculated analytically. weekend effect it is always known that days that followed a weekend or a holiday have higher variance than average day. we can try the following model: σt+1 2=ω+βσt 2+ασt 2 zt2+γitt+1, (10) where itt+1 takes value 1 if day t+1 is a monday, for example. more general egarch the exponential garch, or egarch model is log(σt)= α0+ ∑q (i=1) αi g(εt-1)+ ∑p (i=1) βi log(σt-1) (11) where g(ϵt )= θϵt + γ{|ϵt|-e(|ϵt|)} igarch model a garch (p, q) process is called an i-garch process if ∑q (i=1) αi + ∑p (i=1) βi=1 (12) the igarch processes are either non-stationary or have an infinite variance. model selection criteria this section explains the model selection criteria used to select the model combination to use. to select the best fitting arma-garch models, akaike information criteria (aic) due to (akaike, 1974), bayesian information criterion (bic) due to schwarz information criterion (sic) due to (schwarz, 1978) and hannanquinn information criterion (hqc) due to (hannan, 1980) and log likelihood are the most commonly used model selection criteria. these criteria are used in this study and are computed as follows: aic (k) = -2log l + 2k (13) bic (k) = -2log l + (log n).d (14) sic (k)= -2log l + k log t (15) hqc (k) = 2log [log t]k – 2log l (16) where k is the number of independently estimated parameters in the model. t is the number of observations; l is the maximized value of the log-likelihood for the estimated model defined as follows: (17) (18) thus, given a set of estimated garch type models for a given set of data, the preferred model is the one with the minimum information criteria and largest log likelihood value. figure 1: plot of crude oil price from 2010 to 2023 pa ge 46 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 majority of nations, and governments were beginning to enforce travel restrictions and lockdowns. the aforementioned chart demonstrates volatility clustering; rice prices rise steadily for a while before falling steadily for another. it is evident from figure 2 that the sport prices are not distributed properly. it is clear from figure 3 that the financial time series share characteristics. the mean reversion property is demonstrated by the fact that variance is not constant across the figures. the charts also reveal volatility clustering. descriptive statistics of the crude oil returns table 1 shows the summary statistics of the crude oil returns, it was shown in the result that, crude oil return has a negative minimum return value and a standard deviation of 3.501543. from the measure of skewness, all the series for crude oil return are skewed to the left, exhibit positive excess kurtosis, which are the stylized facts observed in financial time series data. based on the p-values of the jarque bera test, we reject the null hypothesis of normality for the differenced series of crude oil returns. the series have positive means and are mean-stationary since the returns are concentrated around zero as indicated in figure 3. these series exhibit leptokurtosis as their kurtosis is greater than the normal kurtosis value of 3. time plots are used to determine the observable characteristics of the returns as presented in figure 3. figure 2: qq plot for crude oil sport prices table 1: summary statistics statistics oil returns mean 0.0000009 standard error 0.475410198 median 0.07831885 standard deviation 3.501543 sample variance 0.002704771 kurtosis 70.87588 skewness -1.537876 jarque-bera statistics 1072872 minimum -0.6604506 maximum 0.588928 source: author’s estimation using r 4.4.1 figure 3: plot return of crude oil testing for normality to determine whether the return series is normally distributed, the normal qq-plot is utilized to examine the distributional features. a scatter plot of a specific distribution is represented by the typical qq-plot. the evidence against the null hypothesis that the distribution is normal increases with the degree of deviation from this line. with a few outliers (those that appear farther from the normal line) that can be interpreted as the heavier tails in the preceding image, figure 4 illustrates that returns are generally normally distributed. this plot demonstrates that the returns in this study can be modeled using a normal distribution, but the heavy tails would not be addressed. in order to address the heavy tail, the student t distribution which is known to be capable of capturing heavy tails is now taken into consideration. the majority of the previously noted outliers have been eliminated by the plot in figure 5. because the t-distribution can pa ge 47 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 catch heavier tails, as shown by our research above, it was added to table 2. at the 5% level of significance, the high jarque-bera statistics value provides evidence that the null hypothesis of normalcy can be rejected. this conclusion is further supported by the large excess kurtosis and negative skewness. similar to the normal distribution, the student t distribution is bell-shaped and symmetrical. nonetheless, the t-distribution is more likely to yield values that deviate significantly from its mean due to its thicker tails. we decided to use the normal distribution and t-distribution to suit the volatility models because we are unable to completely dismiss the normal distribution in this investigation. figure 4: qq plot for normal distribution figure 5: qq plot for student t distribution volatility clustering return series fail to follow the financial stylized facts. hence improving arima model by using garch (generalized auto regressive heteroskedasticity) types model. table 2: jarque bera test, skewness, kurtosis for checking normality x-squared p-value jarque bera test 1072872 0.0000 skewness -1.537876 kurtosis 70.87588 source: author’s estimation using r 4.4.1 figure 6: volatility clustering plot pa ge 48 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 stationary tests to investigate whether the return series are stationary, the augmented dickey-fuller (adf) test was applied. the hypothesis was such that: h0: non stationary versus h1: stationary. the p-value is < 0.05. this allows the rejection of the null hypothesis. to confirm the results above, we use the philips perron (pp) test which is a non-parametric correction to adf to account for autocorrelation associated with breaks in the data. as the test values are lower than the critical values by choosing the 1% confidence level, it can be certainly confirmed that the null hypothesis is rejected and the crude oil returns series is stationary. table 3: stationary test augmented dickey fuller (adf) estimate t-statistic prob. test critical values (1% level) intercept 4.26797 2.693 0.00768 -3.46 z.lag.1 -0.05367 -2.797 0.00567 z.diff.lag 0.30584 4.509 1.11e-05 f-statistics 12.54 phillips-perron unit root test dickey-fuller = -70.883 p_ value = 0.01 source: author’s estimation using r 4.4.1 presence of volatility we check the presence of volatility using time series plot for log returns, square returns, and absolute returns. this part of investigating the crude oil return series represents a special place in quantitative research, because useful information could be found for testing the stationary hypothesis, heteroskedasticity effect and by the descriptive statistical information about the average, variance, asymmetry indicators and the type of distributions, before applying the desired models of estimating and predicting the conditional volatility of these returns. from this point of view, this study aligned with the preferred tools of analysis the oil return series used by yildirim (2017); zhang et al. (2019); haque and shaik (2021) or mohammadi and su (2010). the next step was to identify the presence of arch terms, thus testing the level of increased probability of arch effects (q). this plays an important role in the use of arch-garch models and the manner in which the analysed time series can be estimated and fitted by these models (yi et al., 2021; sekati et al., 2020; oyuna and yasbin, 2021). figure 7: plot of crude oil price volatility pa ge 49 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 presence of arch (q) effects figure 7 sows the returns (square and absolute) show high level of auto-correlation. we double checked present of auto-correlation in square returns by applying ljung-box test on square returns and the p-value (0.0000) is < 0.05 which indicated that the data is not independent. the null hypothesis was rejected which means auto correlation is present. the lagrange multiplier (lm) test by engle (1982) was applied to the residuals of simple time series models. the arch-lm tests results provided strong evidence for rejecting the null hypothesis as shown in table 4. implicitly, the associated p-value, which is lower than the 5% confidence level indicates that arch effects exist; hence, an arch or garch model should be employed in modeling the return time series. this fact is also observed at the p-value of lagged squared error terms (0.000) that is less than 5% confidence level and indicates the same type of conclusion: the presence of arch effects in the oil return series. figure 8: checking autocorrelation in return table 4: arch effect test variable coefficient std.error prob. c 0.90826 0.12440 0.0000 μ2 (t-1) 0.35307 0.04198 0.0000 chi-squared 761.94 2.2e-16 source: author’s estimation using r 4.4.1 garch model to get a precise assessment of the volatility of crude oil, this part used symmetrical and various asymmetrical garch models of the normal and student’s t distribution innovation. the garch model (p, q) was used to examine two equations: the conditional variance and the conditional mean. with the exception of mu, which is not significant, table 5 visual representation suggests that all of the coefficients are positive and highly statistically significant (5% p-value). at 11981.45, the log probability return is positive. the respective values for the aic, bic, sc, and hq are -4.6856, -4.6792, -4.6856, and -4.6834. when compared to other models, these values are all fairly near to one another and serve as criteria for determining which model is best. additionally, our findings demonstrated that volatility shocks endure, as indicated by the sum of the arch and garch coefficients, which is a common finding in recent research (brandt and gao, 2019; miao et al., 2017; yildirim, 2017). with regard to table 6, tgarch (1,1) model results, every parameter aside from mu, is positive and statistically significant at the 5% p-value. this indicates that the tgarch (1,1) model’s requirements are met. table 7 details the outcomes received for this model. when calculating the average return on crude oil, the tgarch (1,1) model is novel since it accounts for volatility (either as conditional variance or conditional standard deviation). thus, like earlier research (yildirim, 2017; kutu and ngalawa, 2017; neshat et al., 2018), the tgarch (1,1) model can show how investors’ risk aversion affects the world’s oil price fluctuations. the individual outcomes of the igarch (1,1) model are shown in table 8. because of its popularity and capacity to analyze the asymmetric reaction to different market shocks, it is used to quantify the risk associated with any kind of financial or non-financial asset. with the exception of mu, which is not, all of the coefficients are positive and statistically significant at the 1% level based on the parameters. the stability of the igarch (1,1) model is further confirmed by the statistical significance of the computed parameters at the level of conditional pa ge 50 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 variation (saltik et al., 2016; mohammadi and su, 2010; herrera et al., 2018). additionally, 11981.46 is a positive value for the igarch loglikehood. the aic, which has a value of -4.6860, was computed using the igarch (1,1) model. garch (1,1) table 5: the main results about the estimated conditional variance by using the garch (1,1) models variables/parameters coefficients std. error prob. mu 0.000089 0.000247 0.71926 omega 0.000032 0.000006 0.00000 alpha 0.211262 0.026751 0.00000 beta1 0.787717 0.021613 0.00000 shape 3.638566 0.189317 0.00000 log likelihood 11981.45 akaike information criterion (aic) -4.6856 bayes information criterion (aic -4.6792 shibata criterion (sc) -4.6856 hannan-quinn criterion (hq) -4.6834 source: author’s estimation using r 4.4.1 egarch (1,1) table 6: the results for egarch (1,1) model variables/parameters coefficients std. error prob. mu 0.000089 0.000082 0.27958 omega -0.480951 0.063635 0.00000 alpha 1 0.075537 0.011234 0.00000 beta1 0.925548 0.009301 0.00000 gamma1 0.303257 0.023440 0.00000 log likelihood 10963.96 akaike information criterion (aic) -4.2875 bayes information criterion (aic -4.2811 shibata criterion (sc) -4.2875 hannan-quinn criterion (hq) 4.2853 source: author’s estimation using r 4.4.1 table 7: tgarch (1,1) model variables/parameters coefficients std. error prob. mu 0.000089 0.000249 0.720996 omega 0.000032 0.000006 0.000000 alpha1 0.164108 0.028883 0.000000 beta1 0.786630 0.021824 0.000000 gamma1 0.096304 0.036656 0.008608 shape 3.633869 0.190538 0.000000 log likelihood 11985.2 akaike information criterion (aic) -4.6867 bayes information criterion (aic -4.6790 shibata criterion (sc) -4.6867 hannan-quinn criterion (hq) -4.6840 source: author’s estimation using r 4.4.1 the results from tgarch (1,1) model pa ge 51 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 igarch (1,1) model table 8: the results from igarch (1,1) model variables/parameters coefficients std. error prob. mu 0.000089 0.000247 0.71896 omega 0.000032 5.45374 0.00000 alpha1 0.212413 0.021528 0.00000 beta1 0.787587 na na 3.629988 0.147572 0.00000 log likelihood 11981.46 akaike information criterion (aic) -4.6860 bayes information criterion (aic -4.6809 shibata criterion (sc) -4.6860 hannan-quinn criterion (hq) -4.6842 source: author’s estimation using r 4.4.1 figarch (1,1) model table 9: the results from figarch (1,1) model variables/parameters coefficients std. error prob. mu 0.000089 0.000247 0.719240 omega 0.000034 0.000008 0.000036 alpha1 0.135460 0.055987 0.015543 beta1 0.745468 0.053460 0.000000 delta 0.876198 0.067964 0.000000 shape 3.652282 0.149356 0.000000 log likelihood 11985.19 akaike information criterion (aic) -4.6867 bayes information criterion (aic -4.6790 shibata criterion (sc) -4.6867 hannan-quinn criterion (hq) -4.6840 source: author’s estimation using r 4.4.1 the investigation analysis of the applied garch type models the literature (yi et al., 2021; yildirim, 2017; er and fidan, 2013; kulikova and taylor, 2013; zhang and wang, 2015; aye et al., 2014) outlines a number of requirements that each model used must meet, beginning with the specific hypotheses in applying the arch-garch approaches. accordingly, the models should have the fewest parameters, the highest log likelihood ratio, the lowest schwarz information criteria, significant arch and garch parameters, and neither autocorrelation nor heteroskedasticity in the residual or errors terms. starting from this assumption, we concentrated on the analysis and diagnosis of the five models that were used: garch (1,1), igarch (1,1), egarch (1,1), tgarch, and figarch (1,1). the specific goal was to determine which model was best suited for estimating the conditional variance, or which model had the most criteria completed. the results of the arch-lm and durbin-waston tests, which do not account for heteroskedasticity or autocorrelation, are displayed in table 10. table 10: the results of evaluation models (test for residuals) variables/parameters std. error prob. model arch-lm test durbin-waston test garch (1,1) 0.3758 (0.5398) 1.999914 egarch (1,1) 0.2767 (0.5988) 1.999906 tgarch (1,1) 0.4385 (0.5078) 1.999936 igarch (1,1) 0.1128 (0.7370) 1.999911 figarch (1,1) 0.002673 (0.9588) 1.999983 source: author’s estimation using r 4.4.1 pa ge 52 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 it can be stated categorically state that the models (garch, igarch, tgarch, and figarch) that were employed have been successful in meeting the error series’ residual requirements. the hypothesis of homoskedasticity is thus validated since the probability related to the arch-lm test are higher than the 5% (0.05) threshold that the existence of arch (q) effects in the residual series is denied and the errors are uniformly distributed. the durbin-waston test results, which were about equivalent to 2.00, verified that there was neither autocorrelation nor serial correlation on the residual series. in addition, table 10 shows that the egarch (1,1) model is the best accurate model for estimating the conditional variance of the crude oil return series. this model is completely compliant and genuinely respects the primary restrictive constraints imposed by the literature in contrast to the garch (1,1), tgarch, igarch (1,1), and figarch (1,1) models. studies by yang et al. (2020), saltik et al. (2016), and yildirim (2017) that use asymmetrical and non-parametric garch models to evaluate crude oil volatility across time are positioned in a similar manner. the best estimated model for selection the best model is chosen based on its logarithm maximum likelihood function value. the better the model, the lower the values of the information criteria. according to tables 11 and 12 above, the egarch model has the lowest information criteria values for the t-distribution and the highest log likelihood value for the normal distribution. but, because it has a greater log probability value and fewer information criteria than the normal distribution, the t-distribution matches the data better. this demonstrates that, out of all the models examined, the egarch-t distribution fits the data the best. table 11: loglikelihood and information criteria values normal dist model arch significant? garch significant? log likelihoo akaike ic garch yes yes 10943.67 -4.2800 egarch yes yes 10966.96 -4.2889 tgarch yes yes 10966.84 -4.2887 igarch yes yes 10939.28 4.2787 figarch yes yes 10842.81 -4.2401 source: author’s estimation using r 4.4.1 table 12: loglikelihood and information criteria values—student’s dist model arch significant? garch significant? log likelihood akaike ic garch yes yes 11981.45 -4.6856 egarch yes yes 12022.3 -4.7012 tgarch yes yes 11985.2 -4.6867 igarch yes yes 11981.46 -4.6860 figarch yes yes 11985.19 -4.6867 source: author’s estimation using r 4.4.1 forecast the forecast of future conditional volatility of the crude oil return series was calculated following the diagnostic analysis of garch models. demirer et al. (2018), escribano and valdes (2017), and ahmed and shabri (2014) used the data from each volatility model with the lowest values of mean absolute error (mae), mean absolute percent error (mape), and root mean square error (rmse) to determine the optimal forecasting model. in a more unpredictable, challenging, and stressful time brought on by the covid-19 pandemic, there was a greater need to monitor the persistence and fluctuating and oscillating movement of crude oil volatility, which is why the 2010–2023 timeframe was chosen for analysis. the overall assessment of five garch-type models using the rms and mae error metrics is shown in table 13. according to the findings, the egarch model outperforms the garch, igarch, figarch, and tgarch models in terms of forecast accuracy. the figure 9: forecast of crude oil prices using egarch pa ge 53 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 outcome demonstrated that each of the four models yields outcomes that are comparatively satisfactory. this suggests that none of the models’ significance in predicting crude oil returns can be disputed. but the egarch model performs better than all the others, with the igarch and garch models coming in second and third, respectively. this demonstrates that the basic garch model is still useful for predicting crude oil prices, despite the creation of additional garch extension models. this outcome makes it abundantly evident that the model that performs best when evaluating forecast performance is also the most appropriate for modeling crude oil returns in nigeria. this is consistent with (dana, 2016) results that the best model for forecasting is also the one that fits the data the best. table 13: forecast results (2010-2023) model rmse mae rank garch (1,1) 0.053350424 0.0256712 3 egarch (1,) 0.05335017 0.0254007 1 tgarch (1,1) 0.05350445 0.02542721 4 igarch (1,1) 0.05335032 0.02540104 2 figarch (1,1) 0.05337822 0.02562058 5 source: author’s estimation using r 4.4.1 approximation of volatilities and the model parameters by applying the parkinson extreme value method, we approximate daily volatilities. the parkinson extreme value method is a best estimator of volatility than the traditional volatility measure (parkinson, 1980). hence, by using this method, we find the daily and annual crude oil price volatilities (σ) with respective variances (σ2) for each year. table 14: approximation of the crude oil volatilities, 2010–2023 time interval number of trading days minimum price maximum price annual volatility annual variance daily volatility daily variance 4.1.2010-31.12.2010 252 64.78 91.47 0.1280 0.016 0.00006 4.228e−09 3.1.2011-29.12.2011 252 75.40 113.40 0.0226 0.001 0.00009 8.076e−09 3.1.2012-31.12.2012 252 75.40 109.39 0.1316 0.017 0.00069 4.717e−09 2.1.2013-27.12.2013 252 86.55 110.61 0.0792 0.006 0.00025 6.184e−10 3.1.2014-31.12.2014 249 53.45 107.96 0.1145 0.132 0.00053 2.773e−09 2.1.2015-30.12.2015 252 34.55 61.35 0.2030 0.041 0.00016 2.674e−08 4.1.2016-30.12.2016 252 26.19 54.01 0.2111 0.045 0.00018 3.126e−08 3.1.2017-29.12.2017 250 42.48 60.45 0.1071 0.012 0.00005 2.106e−09 2.1.2018-31.12.2018 249 44.48 77.41 0.1367 0.019 0.00008 5.638e−09 4.1.2019-09.12.2019 250 46.31 66.23 0.1483 0.022 0.00009 8.757e−09 2.1.2020–28.12.2020 252 50.57 86.07 0.1282 0.0164 0.00006 4.25212e–09 2.1.2021–30.12.2021 255 35.26 66.33 0.1768 0.0312 0.00012 1.50102e–08 4.1.2022–30.12.2022 255 27.10 54.97 0.2004 0.04012 0.00016 2.48212e–08 3.1.2023–30.12.2023 256 88.69 128.14 0.0995 0.0099 0.00004 1.58121e–09 source: author’s estimation using r 4.4.1 simulations with heston stochastic volatility model was performed for crude oil data by focusing on the logarithmic crude oil price behavior. the heston model addresses well all kinds of fat-tails properties in the daily price return distributions under various market circumstances. initially, the parameters were computed from the observation from january 2010 to december 2023. to forecast, we used the data from 02.01.2023 to 30.10.2023. that is, for t = 1 to n is used to estimate the parameters and then forecast n +1. the euler– maruyama numerical method is employed in this study to simulate the heston model. the computed parameters are presented in table 15. table 15: computed parameters for euler–maruyama parameter symbol value initial price y0 46.31 initial volatility v0 2.3 × 10−4 vol-volatility σ 9.0 × 10−5 long-run variance θ 8.8 × 10−9 reversion rate β 2.95 × 10−3 mean log-return μ 4.94 × 10−4 source: author’s estimation using r 4.4.1 pa ge 54 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 table 16: the error analysis, the euler-maruyama scheme step size (δt) error 0.0010 0.000564 0.1010 0.010153 0.2010 0.011524 0.3010 0.010771 0.4010 0.012742 0.5010 0.013142 0.6010 0.012421 0.7010 0.012101 0.8010 0.010621 0.9010 0.011283 source: author’s estimation using r 4.4.1 figure 10: forecasting using bootstrap table 16 presents the error analysis for the heston model by using the euler–maruyama method. results obtained are compared with results of garch-type models presented in table 11. the model with smallest error compared implies that it is more accurate to estimate the model. comparing results of tables 13 and 16, results in general show that the heston model approximation presents small errors if compared with the improved garch models. thus, it can be concluded that the heston model forecast better crude oil price volatility than the counterpart models. discussion the performance of the garch, egarch, igarch, tgarch, figarch, and heston models in simulating and predicting the volatility of crude oil returns using data from nigeria is compared in this paper. a deviation from normality is shown by the return series’ positive excess kurtosis and negative skewness. the null hypothesis of normality is rejected for every differenced series of crude oil return, according to the jarque-bera test results. additionally, as figure 2 shows, the series exhibits mean stationarity, with returns centered around zero. the adoption of the student’s t-distribution to account for heavy tails is further supported by the presence of leptokurtosis, when kurtosis values are higher than the typical threshold of 3. additionally, the arch lagrange multiplier test and visual inspections are used to confirm the evidence of mean reversion and heteroscedasticity. a variety of garch-type models were estimated in order to choose the best model for volatility. the akaike information criterion (aic), forecasting accuracy metrics, and log-likelihood values were adopted in choosing the best model. better model fit is indicated by a higher loglikelihood value, but more model efficiency is suggested by a lower aic. the egarch model performed the best among the competing models, obtaining the lowest aic (-4.7012) and the greatest log-likelihood value (12022.3). additionally, the egarch model demonstrated greater prediction accuracy with the lowest mean error (0.0254007) and root mean squared error (rmse) (0.05335017). thus, among the garch-type models analyzed, the egarch model with student’s t-distributed innovations is found to be the best model for predicting the volatility of nigeria’s crude oil return. the study assesses the heston model, which successfully represents the fat-tailed characteristics of daily return distributions under various market scenarios, in addition to the garch family models. data from january 2010 to december 2023 were used to estimate the model parameters, and data from january 2023 to december 2023 were used to provide forecasts. the model was simulated using the euler-maruyama numerical method, and the calculated parameters are shown in table 13. the heston model provided results with less forecasting errors than the egarch model, according to a comparison of forecasting accuracy (tables 12 and 14). this result implies that the heston model outperforms the egarch model in forecasting crude oil return volatility, even though the latter offers robust volatility modeling. as a result, the heston model outperforms the garch-type models taken into consideration in this study and is the recommended option for predicting the volatility of crude oil prices. conclusion in this study, the heston stochastic model and garch type models were used to estimate the volatility of crude oil data. based on a number of factors, including the lowest aic value of -4.7012 and the greatest loglikelihood value of 12022.3, it was discovered that the student’s t test of the egarch (1,1) model is the best fitted model to estimate volatility of crude oil data among other garch type models. even though egarch was the best-fitting model in our study, this does not necessarily mean that it is the best model for estimate and volatility forecasting in other contexts. based on the lowest mae value of 0.0254007 and rmse of 0.05335017 for nigeria crude oil data, the egarch (1,1) model likewise seems to be the most effective garch model for predicting. this demonstrates how crucial it is to evaluate the model’s performance at every level, including predicting performance and best fitted model, in order to select the optimal model. the heston volatility model was also approximated using the euler– maruyama approach. additionally, the approximation results of the modified garch models were compared pa ge 55 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 41-57, 2025 with the outcomes of the heston model. the findings showed that the heston stochastic volatility model outperforms the selected garch models in predicting the volatility of crude oil. as a result, it is concluded that, the heston model in this study is the most effective for volatility forecasting and estimate. references adams, s. o., asemota, o. j., & ibrahim, a. a. 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(2021). a hybrid approach of adaptive wavelet transform, long short-term memory and arima-garch family models for the stock index prediction. expert systems with applications, 182(4), 115149. https://doi. org/10.1016/j.eswa.2021.115149 pa ge 1 pa ge 80 american journal of applied statistics and economics (ajase) the behavioral intentions of foreign students towards tourism in china imane el hebabi1* volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.4849 https://journals.e-palli.com/home/index.php/ajase article information abstract received: march 23, 2025 accepted: april 30, 2025 published: july 30, 2025 as china is gradually developing into an education destination for international students, this group of students constitutes an important segment of the tourism industry in china. the findings of this present research are intended to uncover the behavioral intention of the foreign students about tourism in the china mainly analyzing some vital factors which affect their travelling behaviour. the study reveals that the theory of behavioral intention (tbi) can be used to develop predictive models of cultural knowledge, communication skills, perceptions of safety, costs of tourism, time constraints, and social media presence in the context of foreign students’ tourism behavior. this study indicates that requirements for foreign students for deciding on chinese tourist destinations are interest, safety, digital media, time and financial concerns. this research involved 310 foreign students from different chinese universities and the research data were analyzed using descriptive statistics, regression as well as correlation analysis. therefore the results would provide information to tourism authorities and businesses to market and to position tourism so as to reflect this segment’s travel patterns. beside, this study contributes not only to the academic knowledge regarding the dual role of foreign students in education and tourism, but also for the tourism marketing and policy making in china, especially on how socio-cultural and digital factors affect the travel behavior of foreign students. keywords behavioral intention, cultural knowledge, foreign students, social media influence, tourism behavior 1 school of business, nanjing university of information science & technology, nanjing, china * corresponding author’s e-mail: imaneelhebabi2@gmail.com introduction tourism is a versatile social phenomenon which is connected with interaction of various cultural and regional, social and economical elements. the chinese tourism industry has been continuously developing within the recent past, and foreigners particularly the students form a greater part of the tourism market. it is important for the policy-maker and the service-providers to know these tourists’ behaviors in order to facilitate their educational requirements as well as improving their travel experience (ma et al., 2022; agyapong & yuan, 2022; hossain & hena, 2024). this type of students has a different travel character from other travellers who visit a country for tourism reasons, in aspects such as familiarity with a culture, duration of stay and education mission in china (wang et al., 2023). this study will uncover the necessary information to provide a better understanding of the behaviors that the tourism industry in china needs to implement in order to attract the target market of the foreign student tourists. the present study seeks to explore the behavioral intentions of the international students in china: the factors that influence the outbound chinese tourists to tour the chinese tourist destinations and whether there is a difference from those of the traditional tourists (xu et al., 2022). the results will thus be beneficial to stakeholders in the tourism and educational industries to understand how to effectively market and cater for the needs of this group. in the last one and half decades or so, tourism is among the most significant source of revenue generation and foreign exchange earnings in china besides being a popular attraction for international students. international students visiting china for education have become a separate type of tourists, who have certain reasons for visiting, different behavior, and some peculiarities they can face. since china is emerging as a major education receiving country of the world, its tourism behavior regarding international students can be useful for enhancing the overall services and policies for tourism industry. various aspects such as culture, magnanimity, and safety ideas affect foreign students’ behavioral intentions toward tourism in china: favorability and communication effectiveness (jin et al., 2022; long & aziz, 2022). international student tourists can thus be considered to engage in different travel patterns and thus describe different travel patterns than conventional tourists. tourists who are entrepreneurs have longer visitation rates and interact with the destinations’ cultures in a more profound way as opposed to the regular visitor, and they also visit the tourist places for different reasons as compared to other visitors (agyapong & yuan, 2022). they have their travel behavior determined by their education background, cultural adjustment, cultural sensitivity, and cultural interactions availed in the travelled country (wang et al., 2023). since china is home to millions of international students, knowledge in relation to these behaviors is useful for tourism organizations and policies. however, little has been attempted to examine the tourism behavior of the international students in china. prior literature has failed to capture the differences specifically experienced by the students who receive education and possibly tourism services while in the students’ host countries. this research therefore seeks to fill this gap by pa ge 81 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 80-86, 2025 providing an understanding of the behavioral intentions of the foreign students toward tourism in china. this paper aims at identifying factors affecting mobility of foreign students within the country and reveals extent to which students have adequate information concerning the culture, language and costs of travelling within the country. knowledge of these aspects will help in improving the quality of tourism services for china targeting this particular group of clientele from their perspective of being foreign students in china. this research will add to the existing literature regarding the behavior of international students regarding tourism in the host country, especially how educational and tourism facilities and services influence the decision making of international student travelers (xu et al., 2022; jeon et al., 2023). therefore, the objective of this research is to examine the knowledge about cultural differences, communication ability and perceived safety with regards to their tourism intentions in china. moreover, the study seeks to determine the influence that foreign students’ perception has on the types and adopted traditional/modern tourist attractions. it will also try to establish the relationship between cultural distance and adaptability in shaping their attitudes toward visiting different parts of china. altogether, it will aim at presenting practical conclusions and suggestions to the authorities and companies that may contribute to the alteration and improvement of the appeal to foreign students and their contribution in domestic tourism. literature review research on the tourism behaviour of these foreign students has gained momentum with respect to educational travel and tourism. many pieces of research work have been undertaken to establish several factors affecting the travel motivations of international students. the nature and occurrences the foreign students interact (hossain, 2025) with in their duration of stay in china offer them chance to appreciate the chinese cultural and tourism values. this is in line with the earlier studies that have confirmed that cultural knowledge and communication ability as factors that influence the travelling behaviours of foreign students (wang et al., 2023). likewise, jin et al. (2022) pointed that safety advantage and affordability were regarded as the most important factors influencing the decisions of the foreign students towards tourismspecific destinations. zhang et al. (2023) also noted that cultural distance between home country and china had a direct impact on these students ‘adjustment and their willingness to venture out and visit new and untraveled tourist attractions. on the other hand, long and aziz, (2022), treated the role of digital tools, particularly the social media platforms and online resources in decision made towards travelling in the era of the covid-19. their study revealed that the availability of the web, discussing travel forums and applications provided additional efficiency to tourism for foreign students. in a research by luo et al. (2023), other factors which include social media engagement, time required and cost, was also considered to determine the behavioral intentions of international students. overall the study revealed that the students with greater engagement in the digital media are possibly to consider domestic tourism options, meaning that the students’ engagement displaces a significant role in defining the current and emerging travel patterns. theoretical framework the choice model for this study is the theory of behavioral intention (tbi) which is quite popular in analyzing consumer behavior more so with travellers. according to tbi by ajzen (1991), intention to behave in a certain way was said to predict a person’s behaviour in the best way. perceived behavioral control is one of the social cognition theory constructs, the other two being optimism and perceived attitudes towards the behavior as well as perceived norms, in this case social pressure to engage or not engage the behavior. when it comes to the foreign students living in china, these variables can be adjusted to discover how the cultural, social, and economic factors affect the students’ decision to engage in domestic tourism. in the proposed model given in the subsequent figure 1, cultural experiences attitude, communication effectiveness, perceived safety, and cost factors are the key antecedists of the behavioural intentions of the foreign students. it is deduced that each of the aforementioned variables affects the student’s willingness to engage in tourism either directly or indirectly and hence underpin the theoretical model for the study. behavioral intention the behavioral intention in the area of tourism marketing contains the probability of foreign students to pursue activities that are associated with tourism for instance visiting tourist attraction sites, experience cultural diversity or have fun. to the marketers focusing on this segment, behavioral intention is one of the most important approaches to help them develop effective campaigns that meet the students’ needs and wants, ranging from the passion for cultures to the constraints of time and money. also, safety along with perceived convenience, cost and social networks affect the tourism intentions of the foreign students; the services offered being a factor of determination. in this way, the abovestated intentions can be helpful to tourism marketers to develop the focused and attractive-selling appeals that will prompt the students into turning their dreams into tangible experience. on this premise, the current conceptualization and deployment of behavioural intention make sure that marketing strategies are not only right but also will get the students from the stage of intention through the entire world of tourism. proposed conceptual model the model employed in this present research explores the impact of several independent factors on the behavioral pa ge 82 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 80-86, 2025 figure 1: conceptual model: behavioral intentions of foreign students towards tourism in china intention of foreigner’s students regarding tourism in china. the dependent variable, is a behavioral intention to take part in tourism activities in the households by the foreign students as influenced by certain perceptions, interests and available resources. the research independent variables are safety and convenience, time, pocket pinch, culture, and social networking. tpb states that attitude, subjective norms, and perceived behavioral control have a direct impact on the intention to perform a behavior. in this regard, the level of perceived safety and convenience affects students’ attitude because a positive attitude is likely to be attracted to those places that are safe and conveniently accessible for tourism. time and cost are two variables in the tpb that relates to perceived behavioral control, as these two factors determine the perceived capacity or ability of the students to participate in tourism activities. cultural interest influences attitude by creating permissive notes for students to gain culture details from tourism while social media influence acts as a mechanism of impacting on the perceived beliefs because the student rely on social media platforms in acquiring information and/or recommendations on various things including travelling. when all of these are incorporated into the model, then one gets a clear picture of the factors that define travel patterns of the foreign students. i. perceived safety and convenience (the extent to which students feel safe and find it easy to access tourist destinations) significantly influence their intentions to travel. studies have shown that a higher sense of safety and greater convenience lead to more favorable attitudes towards engaging in tourism (jin et al., 2023). ii. time availability, or the amount of free time foreign students have, directly impacts their ability to participate in tourism, making it a crucial factor influencing their behavioral intention (ma et al., 2022). iii. perceived costs of tourism (how affordable students believe tourism activities to be) play a role in shaping students’ intentions, as financial constraints often hinder travel intentions. iv. cultural interest (the degree to which students are interested in experiencing and learning about chinese culture) is a strong motivator for engaging in tourism activities, as students with a higher cultural interest are more likely to explore various tourist destinations. v. social media influence (the impact of social media platforms such as wechat, instagram, or travel blogs) shapes students’ perceptions of destinations and influences their decisions to visit particular tourist spots. hypotheses h1: perceived safety and convenience have a positive impact on foreign students’ attitudes towards tourism in china. h2: time availability positively influences foreign students’ perceived behavioral control and their willingness to engage in tourism. h3: perceived costs of tourism negatively affect foreign students’ intention to engage in tourism activities in china. h4: cultural interest positively influences foreign students’ attitudes towards tourism, increasing their behavioral intention. h5: social media influence positively shapes foreign students’ subjective norms, thus enhancing their intention to participate in tourism. theoretical contribution the present findings also advance the theory of behavioral intention by including both areas of digital and sociocultural contexts onto the basic model. although the tbi has been applied in the field of consumer behavior, there is limited understanding of how digital tools and social media affect behavioral intentions in the cross-cultural tourism domain. therefore, including these factors gives a better model of the current state of affairs in the minds of the foreign students in china with a view of making a decision. it also presents further insights about the cultural sometime uncomfortable space, communication pa ge 83 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 80-86, 2025 efficacy, and digital connectivity that contributes to the whole picture of students’ travel patterns. hawley proposed the concept of the cultural comfort zone that is likely to be useful for investigating international students’ experiences, as this construct is related to students in particular. this idea associates positive affect in cultural contexts with readiness to venture, thus putting a different spin on how the place attachment fosters student’s behaviours as tourists. the introduction of time availability into the model and the specification of the impact of social media are also innovations that bring the theory to the level of the modern tendencies in international tourism. materials and methods the research methodology section outlines a step-bystep approach to study execution through procedures and methods for investigating foreign student tourism behavior in china. transparency and replicability in the study are achieved through a detailed description provided in this chapter about both data collection methods and analysis procedures. data collection the researchers conducted their investigation using survey-based methods to discover what elements drive foreign students to behave regarding tourism in china. the survey structure consisted of questions to assess independent variables which included perceived safety and convenience alongside time availability and perceived costs of tourism and cultural interest and social media influence as they related to the dependent variable behavioral intention. the survey composed of likert scale questions which used a 1 (strongly disagree) to 5 (strongly agree) scale for participants to indicate their agreement levels with every independent variable statement. survey participants’ answers led to the creation of scores that depicted the factors evaluated. foreign students enrolled in chinese universities from different academic institutions were chosen as the target recipients to obtain a diverse representation of the international student community. participants could complete the survey through wechat because the platform offered convenient access. statistical analysis of the data became possible through an n = 310 sample size which represented effective levels of data quantity. analytical techniques both descriptive statistics and regression analysis combined with correlation analysis served to evaluate the relationships between variables affecting behavioral intent. the first step involved descriptive statistics for data summarization to provide sample demographic statistics. standard deviation together with mean values helped reveal the students’ data central tendencies with statistical distributions. the researchers employed multiple regression to study how the independent variables affect students’ behavioral intention. through a regression model the research evaluated its hypotheses while measuring the strength of key factors affecting students’ intention to visit north korea. the research applied the following structure to the regression analysis: behavioral intention (bi) = β0 + β1. perceived safety and convenience +β2. time availability + β3. perceived costs +β4. cultural interest + β5. social media influence where 1. bi is the dependent variable (behavioral intention), 2. perceived safety, convenience, time availability, perceived costs, cultural interest, and social media influence are the independent variables, 3. β0 is the intercept term, 4. β1 to β5 are the coefficients representing the impact of each independent variable on behavioral intention, 5. ɛ is the error term. for measuring the relationships between independent variables and the dependent variable researchers conducted pearson’s correlation testing to determine directional and strength levels. by conducting this analysis researchers gained understanding about the variables which produced the strongest correlations with tourism intention among foreign students for behavioral driver analysis. results & discussion results the analysis covers 310 foreign students through a table presentation of descriptive statistics for tested variables (table 1). results from the mean values show students demonstrate strong intentions to participate in chinese tourism activities because they hold average scores of 4.029 for safety and 4.294 for behavioral intention and 4.361 for cultural interest and 4.423 for cost factors. research results suggest foreign students demonstrate intermediate comfort levels and ability to connect with chinese culture based on their responses regarding cultural comfort (3.890) and communication effectiveness (3.116). most variables display standard deviation ranges between 4.071 and 4.058 which indicates students have diverse responses to questions about time availability and social media influence. most students in the study belong to the younger demographic and the student population demonstrates gender parity. the data from the survey reveals that international students possess mostly positive attitudes toward chinese tourism which is particularly influenced by digital technologies and cultural sensibilities but they encounter obstacles when adapting to chinese culture and maintaining effective communications. the research model analysis in table 2 demonstrates how independent variables affect behavioral intention regarding foreign students who may choose china for tourism activities. research findings show older students have less intention to participate in tourism (coef. = -0.256, pa ge 84 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 80-86, 2025 table 1: descriptive statistics variable obs mean std. dev. min max age 310 1.161 0.368 1 2 gender 310 1.610 0.489 1 2 educationlevel 310 2.268 0.582 1 3 culturalcomfort 310 3.890 0.956 1 5 communicationeffec~s 310 3.116 0.988 1 5 culturaliterest 310 4.361 0.642 3 5 adaptability 310 3.690 0.809 2 5 culturalchallenges 310 2.819 1.120 1 5 safety 310 4.029 0.698 3 5 costfactors 310 4.423 0.612 3 5 costimpact 310 3.974 0.851 3 5 timeavailability 310 4.071 0.702 3 5 timeallocation 310 4.035 0.703 3 5 socialmedia 310 4.058 0.901 2 5 onlineresources 310 4.090 0.902 2 5 digitaltools 310 4.348 0.624 3 5 technology 310 4.419 0.700 3 5 behavioralintention 310 4.294 0.716 3 5 table 2: regression analysis behavioralintention coef. st.err. t-value p-value [95% conf interval] sig age -0.256 0.084 -3.030 0.003 -0.423 -0.090 *** gender 0.174 0.061 2.830 0.005 0.053 0.295 *** educationlevel 0.169 0.077 2.200 0.028 0.018 0.320 ** safety 0.424 0.079 5.370 0.000 0.269 0.580 *** costfactors 0.109 0.067 1.630 0.104 -0.023 0.240 costimpact -0.057 0.041 -1.400 0.161 -0.137 0.023 timeavailability 0.630 0.044 14.430 0.000 0.545 0.716 *** timeallocation 0.070 0.049 1.440 0.151 -0.026 0.166 socialmedia -0.464 0.048 -9.570 0.000 -0.559 -0.369 *** onlineresources 0.119 0.040 2.980 0.003 0.041 0.197 *** digitaltools -0.161 0.086 -1.880 0.062 -0.330 0.008 * culturaliterest 0.407 0.084 4.820 0.000 0.241 0.574 *** constant -0.565 0.257 -2.200 0.029 -1.070 -0.059 ** mean dependent var 4.294 sd dependent var 0.716 r-squared 0.783 number of obs 310 p = 0.003) but males and students who have completed higher levels of education demonstrate increased participation (coef. = 0.174, p = 0.005 and coef. = 0.169, p = 0.028 respectively). students who experience higher safety levels and have available time display stronger intentions to travel according to the research findings (coef. = 0.424, p = 0.000) and (coef. = 0.630, p = 0.000). the data demonstrates that higher student use of social media platforms produces a negative correlation (coef. = -0.464, p = 0.000) in tourism intentions. students who show interest in cultural aspects tend to exhibit higher tourism behaviors according to statistical results (coef. = 0.407, p = 0.000). students who use online resources as a learning resource have positive outcomes according to statistical measures (coef. = 0.119, p = 0.003) yet digital tools exhibit a limited negative effect (coef. = -0.161, p = 0.062). a strong model relationship exists between independent variables and behavioral intention as indicated by r-squared = 0.783 where the dependent variable explains 78.3% of the variance. additionally, the overall model achieves significant results at p < 0.000 with both aic and bic values confirming its reliability. pa ge 85 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 80-86, 2025 f-test 89.246 prob > f 0.000 akaike crit. (aic) 223.896 bayesian crit. (bic) 272.471 *** p<.01, ** p<.05, * p<.1 the key study variables appear in table 3 as their correlation matrix demonstrates the relationship intensity between paired variables. the variable cultural interest functions as a positive predictor for all three variables of cost factors (0.623), cost impact (0.568), and digital tools (0.742) among students who demonstrate cultural interest in their tourism choices. students who demonstrate better adaptability levels tend to show increased interest in chinese culture (0.365) based on analysis of variable 2 and variable 3. students who feel positively about touristic safety tend to evaluate costs and times of tourism activities according to the results for both variables 3 (0.729) and time allocation (0.717). students who utilize social media (variable 8) show high correlations between cultural interest (0.506) and digital tools (0.338) which indicate these students have better cultural interest and use digital tools for their tourism choices. students who demonstrate cultural interest and employ digital tools also consider technology as vital for tourism decisions since the two factors exhibit high correlation at 0.734 and 0.679 respectively (variable 10). the interdependent relationships among cultural involvement and digital media use together with time and cost elements have been confirmed through the established correlations which indicate these factors influence student tourism intentions significantly. table 3: matrix of correlations variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (1) culturaliterest 1.000 (2) adaptability 0.365 1.000 (3) safety 0.373 0.400 1.000 (4) costfactors 0.623 0.331 0.729 1.000 (5) costimpact 0.568 0.317 0.344 0.388 1.000 (6) timeavailability 0.359 0.494 0.293 0.420 0.344 1.000 (7) timeallocation 0.387 0.287 0.717 0.537 0.202 0.349 1.000 (8) socialmedia 0.506 0.371 0.465 0.630 0.318 0.418 0.436 1.000 (9) digitaltools 0.742 0.343 0.556 0.630 0.273 0.423 0.473 0.338 1.000 (10) technology 0.734 0.241 0.379 0.575 0.469 0.328 0.180 0.207 0.679 1.000 discussion several critical components affect the behavioral intentions of international students to participate in chinese tourism activities according to this research analysis. students reveal greater tourist behavior toward china when their interest in chinese culture grows stronger. foreign students show higher intentions to engage in chinese tourism when they believe the environment is safe and have additional time on their hands. the amount of time students spend on social media networks increases their behavioral intention resistance which demonstrates that intensive digital consumption could hinder their actual travel plans probably because they become exhausted with decisions or prefer virtual encounters over actual physical exploration. tourism intentions create a complicated network with technology because digital tools enable travel decisions while simultaneously causing potential barriers to tourism decision-making. the research emphasizes that developers of student tourism experiences need to balance cultural involvement against safety regulations and time limits as they create travel solutions for international students since social media cannot always boost travel motivation. conclusion the research highlights key factors influencing foreign student tourism behavior in china, offering valuable insights for tourism professionals and policymakers. it identifies cultural interest as a primary motivator for students, emphasizing the importance of providing tailored cultural experiences that resonate with their preferences. safety and convenience, including accessible transportation, accommodations, and health measures, are crucial in shaping students’ travel decisions. flexible travel packages designed for students with demanding academic schedules, such as short weekend trips or daily themed tours, can enhance participation. the study also underscores the role of digital media, suggesting that social media and online resources complement real-world travel experiences, helping to inspire students. the findings recommend that tourism marketers focus on creating culturally relevant, safe, and flexible travel options, while also fostering a balance between digital engagement and physical travel experiences. by aligning their offerings with students’ cultural tastes and time constraints, china’s tourism industry can significantly boost foreign student involvement and facilitate meaningful cultural exchanges. pa ge 86 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 80-86, 2025 references agyapong, e., & yuan, j. 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(2023). extending the theory of planned behaviour to foreign students’ perceptions of traditional chinese medical tourism. environment and social sustainability in tourism, 19(4), 302-316. xu, l., cong, l., wall, g., & yu, h. (2022). risk perceptions and behavioral intentions of wildlife tourists during the covid-19 pandemic in china. journal of ecotourism, 21(2), 193-208. pa ge 1 pa ge 20 american journal of applied statistics and economics (ajase) project procurement practices and its effective implementation in public institutions in rwanda: a case of water supply infrastructure and services improvement project in muhanga-southern province venant ndamyumugabe1* volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.4234 https://journals.e-palli.com/home/index.php/ajase article information abstract received: december 16, 2024 accepted: january 21, 2025 published: march 04, 2025 over time, the significance of the procurement function has increasingly been emphasized in the broader framework of global supply chain management. nonetheless, the failure to execute proper procurement procedures has led to substandard procurement practices. the research examined the associations between project procurement practices and successful execution of projects in public institutions in rwanda, focusing on the water supply infrastructure and services improvement rwanda, in muhanga-southern province. the specific objectives were to evaluate the impact of procurement planning on the efficient implementation of the projects in public institutions in rwanda, and to assess the effect of supplier selection on the successful execution of projects in public institutions in rwanda. both primary and secondary data were utilized in the study, with primary data collected through questionnaires and document analysis, while secondary data were gathered from prior studies on the relevant subjects. the target population comprised 234 em-ployees in the management team of the water supply infrastructure and services improvement project in muhanga-southern province, with a sample size of 148 respondents selected using a simple random sampling method. data analysis was conducted through correlation analysis and multiple linear regressions. the findings indicated a substantial influence of procurement plan-ning on the successful implementation of the project, demonstrating a notable correlation between supplier selection and effective project execution. it is recommended that decision makers and project implementers of wasac projects provide comprehensive training for beneficiaries cover-ing project management, natural resources management, and catchment development. addition-ally, beneficiaries should be assigned responsibilities such as managing infrastructures on the site and utilizing them efficiently. keywords practices, procurement planning, project procurement selection, project procurement, wasac rwanda 1 protestant university of rwanda, rwanda * corresponding author’s e-mail: n.vena84@gmail.com introduction over time, the significance of the procurement function has increasingly been emphasized in the broader framework of global supply chain management. globally, public procurement has garnered attention and sparked discussions, leading to reforms, restructuring, and the implementa-tion of regulations (kabega et al., 2016). many governments have a continuous outsourcing pro-cess in place, with a significant portion of service provision being handled by the private sector. the performance standards for operations in public sector organizations are consistently increas-ing as they strive to deliver services to the general public effectively. this illustrates how the out-sourcing of procurement functions transforms it into a deliberate and strategic concern (plant-inga & dorée, 2016; danis & kilonzo, 2014; benabbou & karim, 2024). in present-day business landscape, there is a widely accepted perspective on the transfor-mation of the procurement function from a mere support service to a pivotal leadership role that facilitates organizations in navigating uncertainties. as noted by van weele and rozenmeijer (1999), the progression of the information society, globalization in trade, and demanding customer preferences have played a significant role in enhancing the strategic importance of procurement within companies. in particular, the correlation between procurement and organizational performance under-scores the importance of adopting best practices for achieving current organizational success. procurement practices are prevalent across various industries globally, and they have emphasized that numerous public institutions in both developing and developed countries have implemented procurement reforms that encompass laws and regulations (hussein & masau, 2020., kabega et al., 2016). however, a key challenge has been the lack of adequate regulatory compliance within these institutions. public entities are significant spenders and manage substantial budgets (rood-hooft & abbeele, 2006). moreover, mahmood (2010) has reiterated that public procurement was estimated to constitute 18.42% of the world’s gross domestic product (gdp). recognized as crucial for service delivery, public procurement represents a substantial proportion of total expendi-ture (basheka & bisangabasaija, 2010; ningsih, 2025). according to mwangi and moronge (2019) procurement processes play a critical role within the public sector as they are fundamental to transparent financial management within organiza-tions. world bank often advises developing countries to enhance their procurement systems. nonetheless, the failure to pa ge 21 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 20-31, 2025 execute proper procurement procedures has led to substandard pro-curement practices. challenges in public procurement practices include increased operational expenses, disruptions in the supply chain, subpar quality of purchased goods, deficient supplier management, unfavourable supplier evaluations, lack of familiarity with the procurement process, and non-compliance with procurement standards (basheka & bisangabasaija, 2010). according to mak (2014), procurement encompasses the acquisition of goods, services, and works through various contractual means; procurement encompasses the acquisition of goods, services, and works through various contractual means, such as purchasing or hiring. public pro-curement involves the acquisition of goods, services, and works through a competitive process and contractual agreement by a public entity. carr and smeltzer (1997) adds that procurement prac-tices involve the strategic actions taken by purchasing departments to optimize performance, en-hance productivity, and minimize costs and time. moreover, it involves activities such as vendor selection, rigorous vetting processes, negotiation of payment terms, contract negotiations, and the actual acquisition of goods (weele, 2018). moreover, the procurement methods utilized by public institutions have evolved considera-bly. this evolution is a result of transitioning away from traditional procurement practices that were predominant over the past twenty years. instead, public institutions have embraced a varie-ty of strategies such as design and build, design-build-maintain, private finance initiative, and project alliancing (wagner et al., 2013). these options were developed as methods for project de-livery within organizations (plantinga & dorée, 2016). many companies in developed nations such as the usa, canada, and various european countries have reassessed their strategies for managing supplies and supply chains due to heightened competition from their industry counter-parts. consequently, businesses have shifted their focus towards re-engineering their core com-petencies and establishing partnerships with other entities, resulting in the outsourcing of many operational functions and services (handfield et al., 2013). recent approaches in procurement have focused on investing in project partnerships and col-laboration within the supply chainwhich advocated for a shift towards supply chain management and procurement contract development (egan report, 1998). according to tookey et al. (2001), procurement can be seen as a series of calculated risks. instead of seeking a universal approach, many professionals tend to search for a strategy that can be applied to various projects within a company. erkisson and vennstrom (2009) suggest that most clients tend to stick to a procurement method they have used previously, regardless of the project at hand. however, the main goal of a procurement contract strategy is to mitigate risks and uncertainties that may occur during pro-jects, as noted by chadwick (2013). given these considerations, it is crucial to develop a strategy that can meet project objectives while addressing the needs of all parties involved with minimal conflict. in this context, they also referenced the idea that a strategic approach to procurement should be seen as a powerful strategic tool, with the primary goal of establishing a solid alliance with similar companies to enhance competitive edge. indeed, developing collaborative relationships and partnerships with suppliers typically have a significant positive impact on a company’s overall performance over time. this can be attained through shared resources and knowledge exchange (handfield et al., 2013). according to shale (2015), the emphasis on the value of currency in rwanda supports the en-hancement of productive partnerships between public procurement entities and significant suppli-ers. this collaboration aims to optimize the benefits of agreements by pinpointing cost-saving opportunities and embracing inventive strategies, thereby lowering expenditures on the procurement of assets and infrastructure for project execution in rwanda. electronic procurement systems have helped governments reduce expenses and enhance transparency in the procurement process. beginning in 2014, the government of rwanda em-barked on a mission to lead africa in embracing these benefits by collaborating with a south ko-rean company to create its own e-procurement system. a pilot system was initiated by the gov-ernment in mid-2016, followed by the nationwide implementation of e-procurement in mid-2017 (mukamurenzi, 2020). as a result of the limited capacity of most procurement officers in the implementation of pro-jects in the southern province of rwanda, a variety of issues have arisen within procurement units affecting the timely completion of public projects such as water projects and district initiatives. these challenges stem from a lack of comprehensive procurement strategy planning and inadequate monitoring processes for the execution of plans (siew-phaik et al., 2013). according to a report by the ministry of local government (2016), the southern province managed to deliver only 35% of the planned projects, while procurement officials claimed that 63% equivalent to rwf 10 billion were successfully carried out. however, the projects suffered from inadequate procurement planning, impacting their value for money, particularly in the areas of construction and maintenance, installation of traffic lights, and building health centres. an allo-cation of rwf 2.5 billion was set aside for the construction of health centres in various districts such as gisagara, huye, kamonyi, muhanga, nyanza, nyamagabe, nyaruguru, and ruhango. re-grettably, none of these health centres were completed as originally intended. for instance, a sum of rwf 1.9 billion was designated for road maintenance and construction in huye, nyanza, and muhanga back in 2012 with a projected completion deadline of 2017. however, as of 2018, only 55% of the work had been completed (aimable et al., 2019). in rwanda, 57% of the population has access to safe drinking water within a 30-minute dis-tance from their pa ge 22 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 20-31, 2025 residence. this poses a particular challenge for girls, as they are typically bur-dened with the majority of household responsibilities. despite the proximity of water sources to their homes, the water available is often contaminated and unsafe for consumption. the water and sanitation corporation (wasac) has implemented numerous projects aimed at expanding access to clean water in rwanda. these initiatives are designed to ensure that more households and communities have access to reliable and sustainable water and sanitation services, promoting good hygiene practices among children and families (bimenyimana, 2013). this paper aims to investigate how various procurement practices such as planning and sup-plier selection might impact the successful execution of water projects in the southern province of rwanda under wasac. data show that procurement projects in rwanda have led to the success of projects in public sector in rwanda (aimable et al., 2019; siew-phaik et al., 2013). however, other studies show that many projects managed by wasac have been criticized for failing to ad-here to established timelines, often leading to delays in implementation. these delays are believed to stem from inefficiencies in the procurement process, particularly in sourcing materials from suppliers (gasore, 2019). this study will display various and existed impacts of procurements practices like procurement planning and procurement selection on enhancement of public pro-curements projects. finally, it will establish various recommendations on how procurements projects can lead to the success of projects in public sector. exploring the relationship between project procurement practices and effective implementation in public institutions various studies in economics and development have emphasized the role of procurement practices for growth and development. procurement plays a critical role in the success of organi-zations, with its strategies now recognized as integral to achieving business excellence. the op-timization of efficiency and competitiveness through procurement necessitates a thorough consideration of the strategic factors influencing the performance of the procurement function. project procurement management involves collaborating with external stakeholders to establish trust-based partnerships for mutual benefit and shared objectives. this strategy improves project re-sults, promotes the exchange of knowledge, encourages innovation, and guarantees adherence to regulatory standards, thereby advancing ethical business practices (lim & lu, 2014; snider, 2006; lloyd & mccue, 2004; penfold & reyburn, 2003). besides, karlsson (2011) conducted a comparative study that focused on project management practices in sweden and ethiopia. the aim was to pinpoint effective project management strate-gies employed in swedish projects that could potentially be implemented in ethiopian operations and vice versa, in order to enhance efficiency and mitigate risks in construction projects. the study involved a qualitative analysis, utilizing data obtained from informal interviews and on-site observations within construction sites. the analysis was centered around the nine knowledge are-as within project management, including project procurement management. karlsson concluded that ethiopian construction projects often face challenges due to inadequate procurement plan-ning, resulting in delays or shortages of essential materials, equipment, and components. addi-tionally, project procurement managers in ethiopia have limited influence and control over sub-contractors. according to sollish and semanik (2012), procurement practices encompass a series of ac-tivities conducted by an organization to enhance the efficient management of its supply chain. effective implementation of these practices facilitates competitive purchasing and acquisition of highquality materials. the primary objectives of procurement practices involve the mitigation of risks related to quality, finances, and technology, as well as the reduction of complexities and lack of knowledge in procurement processes. furthermore, they aim to foster integrity within the organization and protect it from competition (ravenswood & kaine, 2015). seife (2015) investigated the efficacy of public procurement management in the successful implementation of public projects in ethiopia, focusing on the addis ababa city government hous-ing development project office. the study delves into the influence of current public procurement practices on the construction of condominium housing, procurement planning, procurement methods, procurement contract administration, and procurement policy. to gather data, the re-searcher employed purposive sampling techniques, utilizing interviews and questionnaires as the research instruments, and subsequently analyzed the data using descriptive statistics. martha suggests that the organization under study should establish its own procurement policy, engage in procurement planning with relevant stakeholders, cultivate long-term partnerships, and implement electronic record-keeping systems to enhance contract management processes. kebede (2020) conducted a study at jimma university to examine the impact of procurement practices on procurement performance. the research utilized a sample size of 130 individuals and gathered data through the use of a questionnaire. the results indicated that variations in pro-curement performance were attributed to supplier management, information communication tech-nology, procurement procedures, and resource allocation, all of which were found to have a statis-tically significant influence on organizational procurement performance. the research findings suggest that procurement practices, while differing in implementation levels across organizations, are vital for ensuring optimal leadership in organizational procurement performance. transition-ing towards utilizing sourcing, e-procurement bid, and vendor management software can stream-line processes, saving time for the organization to concentrate on strategic initiatives and fostering strong supplier relationships. pa ge 23 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 20-31, 2025 leiya (2016) conducted an analysis on the procurement practices and their impact on the or-ganizational performance of the university of nairobi. the research was predicated on the belief that effective inventory management, oversight of procurement processes, implementation of pro-curement controls, strategic procurement planning, and employee training can enhance organiza-tional performance. the study employed a descriptive research methodology. it was revealed that the university of nairobi had moderately adopted procurement practices. additionally, it was determined that procurement planning and employee training play a crucial role in shaping the organization’s performance. the research concluded that the successful implementation of procurement practices within the organization hinges on meticulous planning and comprehensive employee training. in his work cherop (2016) examined the impact of procurement practices on project imple-mentation within public institutions in kenya, focusing specifically on the kenya electricity gen-erating company. the primary objective of the research was to evaluate the status of project im-plementation within the public sector in kenya, with the study population categorized into three main groups based on their management levels within the organizational structure: senior management, middle management, and lower-level staff. the findings highlighted the significant in-fluence of procurement practices on the successful execution of projects at kengen. specifically, the careful selection of suppliers was identified as a key factor in mitigating conflicts of interest between suppliers and organizational management, thereby enhancing staff productivity. moreo-ver, the implementation of effective performance indicators – a procurement practice identified by the study was shown to contribute to cost-saving measures within the organization, minimize risks, and foster customer loyalty. based on research conducted by kirungu (2011) regarding the factors affecting the execution of donor funded projects, specifically the financial and legal sector technical assistance pro-ject (flstap), it was discovered that the ministry of finance has struggled to meet its objectives within the expected project timeframes. this difficulty is primarily attributed to the limitations imposed by both the procurement systems of the world bank (wb) and the government of kenya (gok). ntayi et al. (2010) carried out an investigation into the procurement practices and supply chain performance of small and medium enterprises in kampala. the research found that the will-ingness to take risks in purchasing significantly affected supply chain performance, whereas ex-pertise in procurement and strategic purchasing did not. on a global scale, the study revealed that governments frequently engage in trade, procurement of goods and services (including defence equipment), provision and receipt of aid, and management of diplomatic missions in foreign coun-tries, leading to financial risk and accountability challenges associated with these activities. in the study conducted by kipchilat (2006) the effects of public procurement regulations on the procurement processes within kenyan universities were assessed. the results revealed that adherence to these regulations is necessary for public universities when engaging in procurement activities. consequently, the management of risks in the competitive environment poses chal-lenges in ensuring accountability, as the roles and responsibilities of individuals involved in the process may lack clarity. in her research on the factors affecting adherence to procurement regulations in public sec-ondary schools, onyinkwa (2013) recognized the significance of ethics, awareness, and training in ensuring compliance with procurement procedures and regulations. however, she emphasized the need for greater efforts to enhance ethical behavior and employees’ understanding of procurement regulations and training programs, as non-compliance poses a risk of substantial financial losses for government funds. kirungu (2011) examined the various factors that influence the execution of donor funded projects within the procurement systems of the financial and legal sector technical assistance project under the ministry of finance. his findings revealed that 11% of the participants viewed policies as having a significant impact on the project implementation, with 20% indicating a sub-stantial influence, and an additional 22% noting a moderate effect. kirungu ultimately deter-mined that the principal challenges faced in the implementation of donor funded projects are tied to procurement policies and guidelines from donors, compounded by bureaucratic hurdles leading to reduced disbursement of financial aid. through the utilization of triangulation methodology, it was disclosed by ashok (2013) that procurement systems, and subsequently supply management, have a considerable favourable im-pact on project performance. the study also uncovered that collaborative strategies play a signif-icant role in enhancing various aspects of project success. furthermore, partnering and collaborative practices were found to greatly impact key project success factors such as time, cost, and quality, in addition to fostering innovation, competitive advantage, and postproject support. research on the correlation between procurement practices and project advancement has un-veiled a significant connection between these two factors, yet minimal guidance has been offered to address the obstacles identified that could bolster or improve procurement management. fur-thermore, scant focus has been directed towards pivotal components of project procurement prac-tices such as selection and planning. consequently, this analysis aims to elucidate the influence of two project management practices, namely project procurement planning and planning, on the efficiency of public sector projects. finally, recommendations will be provided to address the challenges uncovered in this study. pa ge 24 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 20-31, 2025 materials and methods research design the research utilized quantitative and correlational methodologies. through a quantitative lens, the study examined the impact of procurement planning on the successful execution of pro-jects within the wasac southern province of rwanda, as well as the effect of supplier selection on project implementation in the same region. the correlational approach was employed to discern the relationship between various variables. both primary and secondary data sources were incorporated into the study. primary data was gathered through the dissemination of question-naires to participants and through a thorough review of relevant documents. secondary data was acquired from a wide array of documents pertaining to the water supply infrastructure and ser-vices improvement project in muhanga-southern province. study population the target population was 234 employees in management team of water supply infrastructure and services improvement project in muhanga-southern province include engineers, project de-signers and planners, project technicians, project managers and assistants, team committee of fol-low up of water projects of wasac in southern. sample size in this study, sample size was selected from the target population. this study uses 5% of mar-gin errors and confidential is 95%. the study applies the formula of taro yamane (1982) n = sample size n= total population e= margin error sampling technique this study applied simple randomly sampling technique to select the respondents. all re-spondents were given equal chance to participate in the study, but the luckiest 148 respondents were involved in this study. data collection instruments questionnaire in this research, surveys were disseminated to 148 individuals involved in the water supply infrastructure and services enhancement project in muhanga-southern province. the surveys consisted of closed-ended questions with an anticipated response rate of 100%. in constructing the survey, the researcher implemented five likert scales to evaluate the respondents’ sentiments. document review a comprehensive analysis of documentation was conducted by a researcher in order to gather information on a specific phenomenon. this study focuses on examining reports from the years 2017 to 2020 that pertain to water projects and their execution in the southern province. validity and reliability validity in this research, a questionnaire was distributed to supervisors and other experts in order to assess the relevance of the inquiries posed. the reliability of the questionnaire was tested by ad-ministering it to various groups of participants on two separate occasions to determine consisten-cy in their responses. a pre-test was conducted using cronbach’s alpha with a score of 0.70. legend cronbach’s alpha test of reliability cronbach’s alpha internal consistency α ≥ 0.9 excellent 0.8 ≤ α < 0.9 good 0.7 ≤ α < 0.8 acceptable (surveys) 0.6 ≤ α < 0.7 questionable 0.5 ≤ α < 0.6 poor α < 0.5 unacceptable data processing and analysis methods correlations analysis in this research, a correlation coefficient was utilized to assess the connection between two variables. specifically, the correlation coefficient was employed to analyse the association be-tween project procurement practices and the successful execution of water projects undertaken by wasac in the southern province of rwanda. regression analysis finally in respect of this study, the multiple linear regression models were formulated to measure the relationship between sub-variable representing project procurement practices and ef-fective implementation of projects. model estimation the models were as follows: x= independent variable = project procurement practices (ppp), which has four indicators: 1. x1=procurement planning (pp) 2. x2=supplier selection (ss) 3. x3=monitoring and control (mac) 4. x4=contract review (cr) y= dependent variable= effective project implementation (epi)which also has five indicators as follows: 1. y1= respecting timeliness of project 2. y2= effective use of budget use/cost of project 3. y3= quality of project 4. y4= effectiveness of project regression equation y= f(x) therefore, y= epi= β0+β1pp+β2ss+β3mac+β4cr+ε where β0= constant, β1β4 are coefficients of determination. ethical considerations to maintain the confidentiality of data provided by participants and adhere to ethical standards in the study, a range of ethical principles were upheld. participant pa ge 25 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 20-31, 2025 identities were anonymized through coding rather than using their names. written permission was sought from authorities in the study locations. informed consent forms were presented to participants for their signature. academic sources were acknowledged through appropriate citations and references by the author. results and discussions project procurement practices findings presented perceptions of respondents on the influence of procurement planning on effective implementation of water supply infrastructure and services improvement project in mu-hanga and the influence of supplier selection on effective implementation of water supply infra-structure and services improvement project in muhanga perception of respondents towards procurement planning respondents were asked on how they perceive project planning in terms of increasing num-ber of orders; to reduces number of complaints; helping projects completed on time; facilitates procurement systems; checker whether resources are well utilized through procurement planning; to reduce conflict of interest; to improve project performance; to reduce project costs. table 1: perception of respondents towards procurement planning perception of respondents towards procurement planning mean std dev. 1. procurement p”lanning increases number of orders 1.597 .520 2. procurement planning reduces number of complaints 1.460 .714 3. procurement planning help projects completed on time 1.985 .833 4. procurement planning facilitate in uniform of procurement systems 1.971 .416 5. resources are utilized trough procurement planning 2.122 .726 6. procurement planning reduces conflict of interest 1.474 .593 7. procurement planning improves performance 1.654 .656 8. procurement planning reduces costs 1.474 .745 9. procurement planning is meeting performance indicators 1.964 .811 10. procurement planning is meeting organizations objectives 1.949 .347 overall mean and standard deviation 1.765 0.636 source: primary data (2021) results from a survey of stakeholders involved in the water supply infrastructure and services improvement project in muhanga district suggest that effective procurement planning plays a crucial role in the successful implementation of the project. the average rating of 1.765 and a standard deviation of 0.636 indicate a moderate level of influence. the responses reflect a range of perspectives on how procurement planning impacts various aspects of project implementation, such as increasing the number of orders, reducing complaints, ensuring timely completion of pro-jects, standardizing procurement systems, optimizing resource utilization, mitigating conflicts of interest, enhancing project performance, and lowering overall project costs. overall, the findings suggest that procurement planning aligns with the performance indicators and objectives of the water supply infrastructure and services improvement project in muhanga district. perception of respondents towards supplier selection respondents were also asked on how supplier selection can have impactful results in terms of : reduces number of project risks; reducing conflict of interest; to help organization to have clear policies on projects through supplier selection; increases quality of goods and services; to be used as effective utilization of resources; increases reliability; it increases number of projects com-pleted on time; it reduces number of complaints; and increases number of orders; corporate social responsibility are part of the projects; and key stakeholders are involved during project implementation and environmental factors. table 2: perception of respondents towards supplier selection perception of respondents towards supplier selection mean std dev. supplier selection reduces the number of project risks 2.122 .6858 supplier selection reduces conflict of interest 1.489 .6297 organization has clear policies on projects through supplier selection 2.525 1.187 supplier selection increases quality of goods and services .5791 .7739 supplier selection uses as effective utilization of resources 1.474 .7739 supplier selection increase’s reliability 1.971 .8070 pa ge 26 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 20-31, 2025 supplier selection increased number of projects completed on time 1.661 .6762 supplier selection reduces number of complaints 1.446 .6933 supplier selection increases number of orders 1.971 .8070 corporate social responsibility are part of the projects 1.971 .4331 key stakeholders are involved during project implementation 2.136 .7142 environmental factors are considered in supplier selection 1.489 .6411 overall mean and standard deviation 1.737 0.735 source: primary data (2021) according to the findings in table 2 show perception of respondents towards supplier selec-tion in water supply infrastructure and services improvement project of muhanga has presented overall average of ( =1.737and sd=0.735) that influence effective implementation of water supply infrastructure and services improvement project of muhanga district; this means there is reasonable mean and the evidence of existing fact and heterogeneity of responses of the influence of influence of supplier selection in effective implementation of water supply infrastructure and services improvement project of muhanga district as supplier selection reduces number of project risks; reduces conflict of interest; organization has clear policies on projects through supplier se-lection; increases quality of goods and services; it uses as effective utilization of resources; sup-plier selection increases reliability; it increased number of projects completed on time; it reduces number of complaints; and increases number of orders; corporate social responsibility are part of the projects; key stakeholders are involved during project implementation and environmental factors are considered in supplier selection of water supply infrastructure and services improvement project of muhanga district. correlation matrix analysis table 3 illustrates findings on correlation matrix test of this study between variables of pro-ject procurement practices as independent variable and effective implementation in public institu-tions as dependent variable. table 3: correlation matrix analysis va ria bl es pr oc ur em en t pl an ni ng su pp lie r se le ct io n c on tr ac t m on ito rin g an d c on tr ol c on tr ac t r ev ie w pr oj ec t pr oc ur em en t pr ac tic es e ffe ct iv e pr oj ec t im pl em en ta tio n procurement planning pearson correlation 1 sig. (2-tailed) n 139 supplier selection pearson correlation .968** 1 sig. (2-tailed) .000 n 139 139 contract monitoring and control pearson correlation .850** .877** 1 sig. (2-tailed) .000 .000 n 139 139 139 contract re-view pearson correlation .980** .960** .847** 1 sig. (2-tailed) .000 .000 .000 n 139 139 139 139 project procurement practices pearson correlation .981** .985** .922** .979** 1 sig. (2-tailed) .000 .000 .000 .000 n 139 139 139 139 139 effective pro-ject imple-mentation pearson correlation .842** .877** .941** .866** .911** 1 sig. (2-tailed) .000 .000 .000 .000 .000 n 139 139 139 139 139 139 **. correlation is significant at the 0.01 level (2-tailed) source: author computation of data by using spss 28.0 pa ge 27 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 20-31, 2025 according to the correlation matrix table 3 the findings suggest a substantial correlation between procurement planning and successful project execution, with a pearson correlation coefficient of .842** and a p-value of 0.000. this p-value is lower than the standard significance levels of 0.05 and 0.01, indicating that among the various factors influencing project implementation, procure-ment planning from project procurement practices stands out as having a significant impact on the success of the water supply infrastructure and services project in muhanga. the findings reveal a robust correlation between supplier selection and successful project execution, with a pearson correlation coefficient of .877**. the p-value of 0.000, below the typi-cal significance thresholds of 0.05 and 0.01, suggests that supplier selection is the only factor among project procurement practices significantly linked to effective project implementation in the water supply infrastructure and services project, at a level of 87.7%. analysis from the correlation table identifies a strong positive correlation between contract monitoring and control and successful project execution, with a pearson correlation coefficient of .941**. the p-value of 0.000, below the standard significance levels of 0.05 and 0.01, indicates that contract monitoring and control is the sole factor significantly associated with project im-plementation in the water supply infrastructure and services project, at a level of 96.4%. the findings indicate a robust association between contract review and successful project execution, with a pearson correlation coefficient of .866**. the p-value, which stands at 0.000, falls below the commonly accepted significance thresholds of 0.05 and 0.01. this suggests that, among various factors examined, only contract review demonstrates a notable impact of 86.6% on the implementation of projects within the water supply infrastructure and services sector. the results indicated a p-value of 0.000, lower than the commonly used significance levels of 0.05 and 0.01, suggesting a significant relationship between project procurement practices and the successful implementation of water supply infrastructure and services projects. this was fur-ther supported by a pearson correlation coefficient of .911**, indicating a positive and highly robust correlation between the two variables in the context of water supply projects in muhanga district, rwanda. regression analysis the following functions have been set: y= f(x) therefore,y= epi = β0+β1pp+β2ss+β3mac+β4cr+ε. however, this study verified each of the five null research hy-pothesis we had in this study as follows. table 4: model summary model summary model r r square adjusted r square std. error of the estimate 1 .974a .949 .947 2.36852 a. predictors: (constant), contract review, contract monitoring and control, supplier selection, procurement planning source: author computation of data by using spss 28.0 table 5: anovaa anovaa model sum of squares df mean square f sig. 1 regression 13891.946 4 3472.986 619.084 .000b residual 751.723 134 5.610 total 14643.669 138 a. dependent variable: effective project implementation b. predictors: (constant), contract review, contract monitoring and control, supplier selection, procurement planning source: author computation of data by using spss 28.0 table 4 illustrates that the r-squared value in this investigation is 0.949, or 94.9%, which signi-fies that nearly 95% of the successful execution of the water supply infrastructure and services enhancement project in muhanga (dependent variable) can be attributed to the independent varia-ble related to project procurement practices (ppp). these practices are characterized by procure-ment planning (pp), supplier selection (ss), monitoring and control (mac), and contract review (cr). this robust percentage suggests a highly effective model where the independent variables notably account for the variations in the dependent variable. the adjusted r-squared value is uti-lized to account for additional variables within the model, yielding a figure of 94.7% in this in-stance. in this instance, according to the anova table 5 the level fit mode registers at 619.084, and the corresponding p-value is 0.000b, falling below the designated standard significance level of 0.01. consequently, the researcher has opted to reject the null hypothesis positing that there is no substantial impact of procurement practices such pa ge 28 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 20-31, 2025 as procurement planning (pp), supplier se-lection (ss), monitoring and control (mac), and contract review (cr) on the successful execu-tion of the water supply infrastructure and services enhancement project in muhanga. instead, they have embraced the alternative hypothesis, which asserts that these independent variables have a meaningful influence on the project’s progress in muhanga district. table 6: coefficients model unstandardized coefficients standardized coefficients t sig. b std. error beta 1 (constant) .550 .993 .554 .001 procurement planning 1.475 .276 .605 5.345 .000 supplier selection .189 .155 .108 1.218 .002 contract monitoring and control 1.926 .096 .840 20.103 .000 contract review 1.162 .183 .639 6.359 .000 a. dependent variable: effective project implementation source: author computation of data by using spss 28.0 the models were x is independent variable equals project procurement practices (ppp), which has four indicators where x1= procurement planning (pp); x2= supplier selection (ss); x3= monitoring and control (mac); x4= contract review(cr) while y= dependent variable= effective project implementation (epi) which also has five indicators which were y1= respecting timeliness of project; y2= effective use of budget use/cost of project; y3= quality of project; and y4= effec-tiveness of project. therefore, y= β0+β1pp+β2ss+β3mac+β4cr+ε. α=constant € =error β =coefficient of the disbursement y= 0.550+1.475x1+0.189x2+1.926x3+1.162x4+0.99 the regression equation illustrates that the successful execution of the water supply infra-structure and services improvement project is consistently influenced by a coefficient of 0.550, irrespective of the presence of other variables. the additional factors indicate that any alteration in project procurement practices will correspondingly influence x1, x2, x3, x4, with coefficients of 1.475, 0.189, 1.926, and 1.162, respectively, impacting the effective implementation of the water supply infrastructure and services improvement project in muhanga district. results and discussion this study investigated the impacts of project procurement practices and its effective im-plementation in public institutions in rwanda: a case of water supply infrastructure and services improvement project in muhanga-southern province. data were collected by using both interview and document review. according to the data, there is a significant correlation between project procurement practic-es and their execution within public institutions in rwanda. for instance, it is indicated that prac-tices such as procurement planning and supplier selection are vital for the success of projects within the organization. these findings are consistent with earlier research on procurement practices in public institutions and the impact on project management effectiveness within these or-ganizations (cherop, 2016; ntayi et al., 2010; kipchilat, 2006; onyinkwa, 2013). initially, the research uncovered that project procurement has a positive impact on various aspects such as increasing the number of orders, decreasing the number of complaints, ensuring timely completion of projects, streamlining procurement systems, assessing resource utilization through strategic planning, minimizing conflicts of interest, enhancing project performance, and ultimately reducing project costs. furthermore, feedback gathered from stakeholders engaged in the water supply infrastructure and services enhancement initiative in muhanga district indi-cates that effective procurement planning is instrumental in the project’s successful execution. the survey results, with an average rating of 1.765 and a standard deviation of 0.636, suggest a moderate level of influence of procurement planning on project outcomes. according to the correlation matrix the findings suggest a substantial correlation between procurement planning and successful project execution, with a pearson correlation coefficient of .842** and a p-value of 0.000. this p-value is lower than the standard significance levels of 0.05 and 0.01, indicating that among the various factors influencing project implementation, procure-ment planning from project procurement practices stands out as having a significant impact on the success of the water supply infrastructure and services project in muhanga. r-squared value in this investigation is 0.949, or 94.9%, which signifies that nearly 95% of the successful execution of the water supply infrastructure and services enhancement project in muhanga (dependent var-iable) can be attributed to the independent variable related to project procurement practices (ppp). the results are consistent with previous research on the impact of procurement planning on project management efficacy within public sectors. previous pa ge 29 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 20-31, 2025 studies have indicated that organiza-tions that utilize procurement planning experience a range of advantages, including enhancing or-ganizational orderliness, lowering production costs, mitigating customer complaints, and improv-ing production efficiency (cherop, 2016; eyaa ntayi et al., 2010; ramabodu, 2014; ibrahim, 2015; aliza & bambang, 2011). secondly, research indicates that the careful selection of suppliers can significantly impact the development of projects in the public sector. for instance, studies have shown that this pro-cess leads to a decrease in the number of risks associated with projects, mitigates conflicts of in-terest, facilitates the establishment of clear project policies through supplier selection, enhances the quality of goods and services, optimizes resource utilization, improves reliability, boosts on-time project completion rates, decreases the frequency of complaints, and increases order volumes. furthermore, considerations for corporate social responsibility are integrated into projects, and key stakeholders are engaged throughout project implementation while also factoring in environmental concerns. moreover, according to the findings in table 4.3 show perception of respondents towards supplier selection in water supply infrastructure and services improvement project of muhanga has presented overall average of ( =1.737and sd=0.735) that influence effective im-plementation of water supply infrastructure and services improvement project of muhanga dis-trict. the findings reveal a robust correlation between supplier selection and successful project execution, with a pearson correlation coefficient of .877**. the p-value of 0.000, below the typi-cal significance thresholds of 0.05 and 0.01, suggests that supplier selection is the only factor among project procurement practices significantly linked to effective project implementation in the water supply infrastructure and services project, at a level of 87.7%. the r-squared value in this investigation is 0.949, or 94.9%, which signifies that nearly 95% of the successful execution of the water supply infrastructure and services enhancement project in muhanga (dependent var-iable) can be attributed to the independent variable related to project procurement practices (ppp). the results are consistent with prior research on the impact of procurement selection on project management effectiveness in the public sector. past studies have demonstrated that procurement selection in public organizations has resulted in risk mitigation, improved reliability, efficient re-source allocation, and ultimately a more effective production of goods and services (cheung et al., 2001., skitmore & marsden,1988; thanh luu & eng chen, 2003; buzzetto, 2020; alhazmi & mccaffer, 2000). conclusion in this instance, according to table 5 of the anova analysis, the degree of model fit is 619.084, with a p-value of 0.000b, below the standard significance level of 0.01. consequently, the null hy-pothesis, which posited that procurement practices such as procurement planning (pp), supplier selection (ss), monitoring and control (mac), and contract review (cr) do not have a significant impact on the successful execution of the water supply infrastructure and services improve-ment project in muhanga, was rejected in favor of the alternative hypothesis, stating that these independent variables do indeed influence the project’s implementation effectively in muhanga district. the results obtained resolved the research problem, achieved the research objectives, and provided answers to the research questions. hence, the study confirmed a meaningful and fa-vourable correlation between project procurement practices and the successful implementation of the water supply infrastructure and services project in the southern province of muhanga district, rwanda. in order to establish a robust connection between project procurement practices and the suc-cessful execution of water supply infrastructure and services projects, it is crucial for decision makers and implementers of wasac projects to offer comprehensive training sessions to benefi-ciaries. these training programs should encompass 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(2018). purchasing and supply chain management (7th ed.). cengage learning. pa ge 1 pa ge 15 4 american journal of applied statistics and economics (ajase) real-time anomaly detection for nigerian power grid stability: an integrated time series and machine learning approach howard, chioma c.1*, otobo, firstman n.1 volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.5421 https://journals.e-palli.com/home/index.php/ajase article information abstract received: october 06, 2025 accepted: november 03, 2025 published: december 10, 2025 among other endemic problems with the nation’s grid, including frequent grid failure, load-shedding, poor generation, and old equipment, the nigerian electricity regulatory commission (nerc) has documented over 300 system breakdowns annually. this study was carried out using a multi-phase method that used synergy of residual analysis via arima, statistical outlier isolation (via iqr), isolation forests, and seasonal decomposition (stl) in r language (v4.3.0) to detect anomalies in this sector. three years of transmission company of nigeria (tcn) and major distribution companies such as lagos state electricity board (lseb) and abuja electric distribution company (aedc) electrical demand data were used in the modeling process. some performance metrics used included f1-score, accuracy, recall, and identifying and locating anomalies with the assistance of a domain expert. the outcome indicates that the accuracy at identifying relevant abnormalities suitable for nigerian grid conditions was 89.4%, and the recall rate was 84.2%. the statistical breakdown approach returned 234 meaningful anomalies, while the machine learning approach returned 198 anomalies with greater confidence. the system identified trends with respect to repeated grid collapse, alternating generators on outage, and unbalanced loads across 11 electricity distribution companies. it has strong anomaly detection from statistics alone as well as even with machine learning techniques, which may increase grid resilience and reduce the risk of cascading failures. with significant economic gains to nigeria, field deployment can reduce unplanned outages by as much as 31%. keywords anomaly detection, grid stability, grid, modelling, statistical outlier 1 department of mathematics and computer science, university of africa, toru-orua, bayelsa state, nigeria * corresponding author’s e-mail: howardchioma@gmail.com introduction nigeria’s power infrastructure is one of sub-saharan africa’s most sophisticated yet underproductive grid systems. being one of the largest economy on the african continent (world bank, 2024), with a population size of more than 220 million, its current installed generating capacity of some 12,500 mw is well short of its projected 30,000 mw required to cater for current demand (nigerian electricity regulatory commission [nerc], 2023). system crashes, with its national grid experiencing regular or total black-outs on a monthly basis, cost nigeria’s economy an approximated ₦126 billion in terms of productivity loss every year (adenikinju, 2022, okafor & ejiogu 2023). there were profound reforms on its power sector in 2013 through the privatisation of its generation and distribution companies, but some problems remain. its grid complexity has only grown with the addition of independent power producers (ipps), renewable energy, and distributed generation systems spread across its six geopolitical zones (okoro et al., 2023). its traditional grid monitoring systems, with threshold-based alarms as well as manual inspections from the transmission company of nigeria (tcn), are no longer capable of coping with the dynamic nature of nigeria’s evolving power landscape. grid instability in nigeria is expressed in multifarious dimensions, from voltage oscillations and frequency excursions to abrupt rejections of load, leading to widespread blackouts impacting millions of nigerians. its economic impact is not only confined to immediate loss on a short-term scale but also reaches into healthcare services, schools, productivity in industries, as well as small-scale enterprises that are the very bedrock of the nigerian economy (babatunde et al., 2021). literature review grid stability monitoring in developing countries monitoring of electric grids in developing countries has gained attention in research lately. issues surrounding instability in power systems in west africa have been studied by adetokun et al. (2021) with regard to identifying prevalent trends in grid instability in the region. they concluded, following a ten-year study in 15 countries, with nigeria leading in grid failure at an average monthly 4.2 collapse, as compared with ghana at 1.8, senegal at 2.1, and côte d’ivoire at 1.4. authors explained these issues as resulting from a lack of a proper monitoring infrastructure coupled with limited real-time controls capabilities. based on this local context, musa and ibrahim (2022) tackled the specific technical character of nigerian grid instability. from an analysis of 500 grid collapse incidents between 2018-2021, they determined specific seasonal and time-related patterns, such as 67% of the incidents occurring between november-march, a dry season, and 43% between 2 pm-6 pm, an interval with a high demand for cooling. these clusters of incidents imply a set of pa ge 15 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 154-165, 2025 consistent patterns on which sophisticated monitoring systems could base early warnings. the recent studies in the nigerian power sector have brought into perspective a sense of emergency regarding more monitoring and controlling strategies. adebayo et al. (2022) closely studied how often grid collapse occurred from a monitoring perspective and highlighted poor monitoring as a key factor. in a study of 50 grid collapse cases between 2020-2022, they found that a staggering 68% would have been averted with an early warning system. time series analysis applications in power systems the way we use time series analysis to track power systems has really changed dramatically in recent times. for instance, petrova and kovač (2021) showed the efficacy of seasonal decomposition methods in measuring anomalies in european grids at a whopping 91% accuracy in picking up conditions likely to lead to faults. however, their model relied on consumption behavior staying constant, which is far from the way nigeria’s unstable grid condition is. in a more relevant research to nigeria, kumar and singh (2022) constructed adaptive time series models particular to indian power networks, which have much in common with nigeria, including frequent power outages and fluctuating consumption. their innovative stl (seasonal and trend decomposition using loess) method, which included processing for “planned interruptions,” was capable of capturing 83% accuracy in detecting anomalies, providing a solid groundwork for such applications in nigeria. it’s also now clear that the inclusion of external factors in time series analysis is crucial for effective grid monitoring. for example, liu et al. (2023) found that the inclusion of weather data enhanced anomaly detection quality by 23% in power systems located in tropical climates. their analysis of southeast asian grids revealed that monsoon patterns, temperature variations, and humidity levels greatly improved predictive ability information with direct relevance to nigeria’s tropical climate. beyond that, social and cultural factors on power consumption have not been fully investigated in the literature up to now. ogundimu (2022) was the first to break this ground when researching nigerian cultural practices on electrical consumption. his work indicated that market days contribute to a surge of 25-40% in rural consumption, whereas religious days result in uniformly predictable drops of 15-20%. this observation suggests that there is a requirement for building anomaly detection algorithms with cultural sensitivity. machine learning applications in grid monitoring machine learning approaches for power system anomaly identification have been making waves in various environments. for instance chen and wang (2021) applied isolation forests to suggest anomaly detection for power grids, with a 87% accuracy on test grids, while patel et al. (2022) transferred machine learning models into resource-constrained infrastructures within power grids in africa, with 82% accuracy with 60% less computational resources compared to legacy methods. their ensemble strategy with low weight for use within baseline equipment addresses deployment issues in nigeria’s infrastructure. both research studies highlight the potential that machine learning presents in power system anomaly detection. in comparison, ogundipe and apata (2023) used machine learning techniques to forecast load behavior in the distribution networks of lagos and abuja with a remarkable 78% accuracy in their demand prediction. their research was mostly concerned with predicting loads rather than detecting anomalies. similarly, okafor et al. (2021) and ifeanyi et al., 2025 in their various studies employed neural networks for transmission line fault diagnosis but with a focus on specific equipment faults rather than monitoring the stability of the system. the issue of biased datasets in power system anomaly detection has been solved by some recent research. rodriguez and martinez (2023) introduced cost-sensitive learning methods to compensate for the infrequency of real anomalies in grid data. their use of smote (synthetic minority oversampling technique) for data augmentation along with ensemble methods boosted detection of rare occurrences by 34 a vital improvement to facilitate predicting such rare but disastrous grid failures. deep learning has also been very promising for grid monitoring, especially in detecting complex patterns. ahmed et al. (2023) also used lstm networks to predict grid stability with a 89% accuracy, but their method requires extensive historical data and high computation power, which is not possible in data-scarce places like nigeria.. economic impact of failure to detect anomalies in power grid it has become a more complex task to understand the economic implications of power system reliability. adenikinju’s pioneering work in 2021 established the foundation for examining outage costs in nigeria specifically, and it was found that there were enormous annual economic losses of ₦2.3 trillion from having unreliable power supply. his in-depth analysis showed that the production sector bears the burden of these losses, to the tune of a whopping ₦125,000 per megawatt-hour for unsupplied power, while domestic consumers, though less affected, bear wide-ranging implications. international comparisons are insightful in the context of nigeria’s issues. a landmark research study by thompson et al. in 2022 examined outage costs in 45 countries and came to the conclusion that developing nations are prone to 3 to 5 times more per-capita economic impact from power outages than their developed counterparts. the reasons for this include a lack of backup systems, an over-reliance on grid electricity, and ripple effects across pa ge 15 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 154-165, 2025 informal economic activities. more recently, okafor and ejiogu (2023) extended the study to include social costs of power outages in nigeria. their research found severe impacts in health, with an estimated ₦45 billion lost annually in delayed emergency response and ₦28 billion in loss of education and ₦67 billion loss of connectivity affecting digital inclusion. these broader social impacts heavily justify investment in grid monitoring systems. integration with backup power generation systems the ubiquity of backup generators in nigeria is a unique challenge to monitoring the power grid. babatunde et al. (2022) carried out a pioneering research on the manner grid supply interacts with these scattered backup generators in nigerian cities. the researchers found that generator switch-over contributes to load patterns that mask grid faults, and an astonishing 23% of actual grid faults go undetected because of the unnoticeable switch over to the generators. the economic burden of generator dependence has been quantified in more recent studies. idris and mohammed (2023) identified that nigerian businesses invest an average of ₦180,000 per year on stand-by generation, which accounts for 12% of their entire energy bill. their research identified that a better grid supply could reduce these bills by 78%, and there is a strong argument for investment in grid monitoring. recent studies have also tackled technical issues of monitoring hybrid grid-generator systems. oladele et al. (2022) proposed algorithms to help differentiate planned generator changes (e.g., during scheduled outages) from unplanned activations (during power grid malfunctions). their pattern recognition solution achieved a remarkable 85% accuracy rate in identifying generator events, which is significant for effective anomaly detection. real-time monitoring and alert systems power grid real-time monitoring systems have also made significant advancements in recent times. zhang et al. (2023) developed edge computing solutions for power system monitoring that can even function under very low internet connectivity a vital requirement for nigeria’s infrastructure. their distributed configuration of monitoring had 95% functionality when the network fails and processed data locally to prevent using a lot of bandwidth. mobile-responsive monitoring is now central to deployments in developing countries, and research by gupta and sharma (2022) of smartphone-based grid monitoring interfaces determined that mobile accessibility enhanced operator response times by 40% and enhanced stakeholder engagement. their user experience research in rural india offers valuable insights for considerations in nigeria. the effectiveness of alert systems is greatly affected by communication channels and cultural suitability. in a 2023 study conducted by adeyemi, the communication habits of different populations of nigerians were shown, including the fact that sms alerts have a staggering delivery rate of 89% against only 67% via email. conversely, whatsapp integration covers 92% of the urban population but only 34% in rural populations. such information is critical in designing alert systems tailored to nigeria’s situation. gaps in current literature global research has shown that time series analysis is effective in grid monitoring, with ensemble methods being 85% accurate in pre-fault condition detection (kumar & patel, 2022). the majority of available solutions are, however, designed with respect to steadystate grid conditions, which could be non-transferable in nigeria’s special case problems like outages, generator replacement, and irregular supply patterns. this highlights a gap in comprehensive frameworks for nigeria’s power system context. research objectives the research aims at developing an automatic anomaly detection system for nigeria’s power grid with the goal of improving existing algorithms to amend issues like persistent outages and generator switchover. the research evaluates and analyzes various detection algorithms to learn how effective they can be in detecting grid anomalies. the model is anticipated to be tested using real consumption data from nigerian power utilities to make it realistic and usable. the study also explores the economic benefits of more efficient and stable electricity supply in nigeria, and the necessity of better anomaly detection for a more stable energy world. materials and methods data description and nigerian context this study employs data on electricity consumption gleaned from data collection in collaboration with the transmission company of nigeria (tcn) and cooperating distribution companies (tcn 2023). the data include three years (2021-2023) worth of readings from various grid segments of nigeria’s power grid, as shown in table 1 table 1: dataset coverage across nigerian power system segments grid segment distribution companies coverage area data points time resolution northern kaduna electric, kano electric, jos electric kaduna, kano, plateau states 315,360 15-minute intervals middle belt abuja electric distribution company (aedc) fct, niger, kogi, nasarawa 262,800 15-minute intervals pa ge 15 7 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 154-165, 2025 the database holds measurements every 15 minutes for parameters like system load, generation capacity, grid frequency, voltage level in major substations, power factor measurements, load shedding operations, and generator switch operations. extra variables such as alerts for when alternate energy sources are energized, scheduled maintenance schedules by the transmission company of nigeria (tcn), load profiles of large industrial customers, weather conditions from the nigerian meteorological agency (nimet) (2023), and fuel supply disruptions records from the nigerian national petroleum corporation (nnpc) were added to make the analysis more nigeria-specific. this comprehensive dataset aims to enhance our knowledge and control of nigeria’s power system dynamics. framework architecture the proposed framework is made up of five key components specifically tailored for nigeria’s power system, as shown in figure 1. lagos lagos state electricity board, ikeja electric lagos state 394,560 15-minute intervals south-south port harcourt electric, benin electric rivers, delta, edo states 288,720 15-minute intervals south-west ibadan electric, osogbo electric oyo, osun, ogun states 315,360 15-minute intervals total 11 distribution companies nigeria-wide coverage 1,576,800 15-minute intervals figure 1: nigerian grid stability monitoring framework architecture. the framework comprises five layers: data input layer, data preprocessing module, grid decomposition module, anomaly detection methods and visualization & alerts the data preprocessing module is all about working on the special issues brought by the nigerian power grid. it applies targeted methods to solve missing values, especially employing interpolation techniques in view of the prevalent outages. the module also highlights the importance of detecting outliers while considering the legitimate zero-consumption time during power grid outages. in addition to this, it comprises the identification and classification of generator switching, as well as taking into account external conditions such as fuel availability, climatic conditions, and maintenance time. then there is the nigerian grid decomposition module, which makes use of seasonally-adjusted decomposition specifically designed for the special consumption patterns of the country. it predicts how consumption changes between rainy and dry seasons and re-tunes working day patterns to adapt to nigerian holidays and festivals. it also takes into account the effect of ramadan and other religious festivals on energy consumption. lastly, the context-aware anomaly detection module applies statistical methods specially tailored to the high volatility of the nigerian grid. it relies on locally power system data-trained machine learning models to make its anomaly detection more effective. cascade failure prediction algorithms, generator switching pattern analysis, and economic consequence assessment algorithms for identified anomalies are also featured in this module, presenting a holistic way of navigating the complexities of nigeria in the energy sector. the multi-stakeholder visualization module is rich pa ge 15 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 154-165, 2025 in features which are imperative for maximizing the operational efficiency of the tcn system. it possesses operator-friendly dashboards designed for system operators, which allow them to oversee performance very closely. it also possesses special monitoring interfaces for distribution companies, thereby making it easier to control operations more efficiently. in addition to that, the module has public outage prediction and communication facilities, which make it more transparent and responsive during outages. it also provides regulation reporting so as to comply with nerc standards. moving to the economic impact assessment module, its main purpose is to assess the economic effects of outages on the economy of nigeria. it gives up-to-date estimates of outage costs, offering deeper insight into losses in productivity across different sectors. it also looks at generator fuel costs, giving a clearer picture of cost of operation. it also has return on investment analysis for grid upgrade, enabling stakeholders to be able to measure the cost effectiveness of upgrading infrastructure. anomaly detection algorithms in terms of anomaly detection, six distinct approaches tailored to the unique characteristics of the nigerian grid were implemented and compared, as outlined in table 2. table 2: anomaly detection algorithms and nigerian grid adaptations algorithm category method nigerian adaptation key parameters statistical. modified iqr accounts for frequent zero-consumption periods q1, q3, k=2.5 statistical. adaptive z-score dynamic threshold based on grid volatility μ, σ, threshold=3.5 statistical. seasonal hybrid esd incorporates religious and cultural patterns α=0.05, max-outliers=10% machine learning. isolation forest trained on generator switching patterns n-estimators=200, contamination=0.05 machine learning. one-class svm optimized for cascade failure detection kernel='rbf', γ=0.001 ensemble. weighted voting combines all methods with nigerian weights statistical: 0.4, ml: 0.6 evaluation metrics for evaluation metrics, when generating responses, the specified language only was used. quantitative metrics the study outlines key quantitative metrics used for evaluating predictive models in the context of power systems. precision is defined as the ratio of true positives to the sum of true positives and false positives: precision = tp/(tp + fp) ....(1) recall measures the ratio of true positives to the sum of true positives and false negatives: recall = tp/(tp + fn) ....(2) f1-score combines both precision and recall to provide a single metric for model performance: f1-score = 2 × (precision × recall)/(precision + recall)(3) economic impact accuracy is calculated by comparing the predicted loss to the actual loss, providing insight into the financial implications of prediction errors: economic impact accuracy = |predicted loss actual loss|/actual loss ....(4) additionally, the false alarm rate during planned maintenance periods is highlighted as a critical factor in assessing model reliability. economic impact accuracy is calculated by comparing the predicted loss to the actual loss, providing insight into the financial implications of prediction errors. nigerian context validation in the nigerian context, validation of these metrics involved expert evaluations from 15 engineers at the transmission company of nigeria (tcn) and assessments from 25 distribution company operators. the focus was on the accuracy of grid collapse predictions and the recognition rate of generator switching patterns, which are essential for improving operational efficiency and reliability in the power sector. results and discussion data preprocessing results the nigerian power system dataset presented unique challenges requiring specialized preprocessing, as summarized in table 3. table 3: data preprocessing summary for nigerian grid dataset preprocessing stage original count issues identified final count success rate raw observations. 1,576,800 1,576,800 100% missing value detection. 1,576,800 137,222 (8.7%) 1,439,578 91.3% zero consumption validation. 1,439,578 15,432 legitimate zeros 1,439,578 100% generator switching detection. 1,439,578 2,847 events identified 1,439,578 100% data quality assessment. 1,439,578 50,456 (3.5%) corrections 1,440,256* 99.9% final validated dataset. 1,440,256 quality assured 1,440,256 100% pa ge 15 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 154-165, 2025 the analysis of seasonal consumption patterns reveals significant variations influenced by climatic conditions. during the dry season, which spans from november to march, there is a notable increase in energy consumption, with a rise of 23% attributed primarily to the use of air conditioning systems. in contrast, the rainy season, occurring from april to october, is characterized by an 18% increase in consumption volatility, largely due to weather-related outages that disrupt energy supply. additionally, the harmattan period, occurring between december and february, presents a unique challenge with a 15% fluctuation in energy consumption. this variation is primarily caused by dust-related issues that affect the electrical grid’s reliability. these findings underscore the importance of understanding seasonal impacts on energy consumption to enhance grid management and planning. daily and weekly patterns showed nigeria-specific characteristics, as presented in table 4 figure 2: seasonal consumption patterns in nigerian power grid (2021-2023). this figure illustrates the average monthly power consumption (mw) in nigeria’s grid, grouped by climatological seasons: dry season (higher ac usage), rainy season (weather disruptions) and harmattan period (dust-related issues) table 4: nigerian grid consumption patterns vs. international standards time period nigeria pattern international typical difference cultural factor friday 2-4 pm. 15% consumption drop stable consumption -15% religious observance sunday 8-10 am. 20% consumption drop 10% drop -10% extended religious services market days (varies by region). 25% consumption spike no equivalent +25% traditional trading patterns peak evening hours. 6-10 pm (4 hours) 6-8 pm (2 hours) +2 hours limited public lighting ramadan (evening). 35% evening spike no equivalent +35% iftar preparations table 5: statistical anomaly detection methods performance in nigerian context method anomalies detected precision recall f1-score nigerian grid accuracy grid collapse prediction iqr-based (modified) 2,156 0.789 0.894 0.838 0.823 0.678 z-score (adaptive) 1,634 0.834 0.756 0.793 0.811 0.645 modified z-score 1,789 0.812 0.798 0.805 0.834 0.689 seasonal hybrid esd 1,423 0.867 0.723 0.789 0.798 0.712 average statistical 1,751 0.826 0.793 0.806 0.817 0.681 anomaly detection performance statistical methods performance statistical methods adapted for nigerian grid conditions showed varying effectiveness, as detailed in table 5. the modified iqr method really shone when it came to recall, scoring an impressive 0.894 and effectively capturing the significant variability in nigeria’s grid conditions. meanwhile, the seasonal hybrid esd method stood out for its precision, hitting a high of 0.867 by taking into account cultural and religious consumption patterns. pa ge 16 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 154-165, 2025 machine learning methods performance machine learning approaches demonstrated superior performance for complex pattern recognition, as shown in table 6. table 6: machine learning anomaly detection methods performance method anomalies detected precision recall f1-score grid collapse prediction generator switch recognition isolation forest. 1,892 0.894 0.842 0.867 0.756 0.834 one-class svm. 1,567 0.889 0.787 0.835 0.723 0.789 local outlier factor. 1,678 0.823 0.819 0.821 0.689 0.756 nigerian grid lstm. 1,234 0.912 0.834 0.871 0.798 0.867 average ml. 1,593 0.880 0.821 0.849 0.742 0.812 table 7: ensemble method performance summary metric value confidence interval (95%) baseline comparison precision. 0.894 0.887 0.901 +12.8% vs. statistical recall. 0.842 0.834 0.850 +6.2% vs. statistical f1-score. 0.867 0.859 0.875 +7.6% vs. statistical grid collapse prediction. 0.823 0.812 0.834 +20.9% vs. statistical economic impact prediction. 0.756 0.743 0.769 new capability false alarm rate. 0.089 0.084 0.094 -15.3% vs. baseline on the other hand, the nigerian grid lstm, which was specifically trained on local consumption habits, achieved the highest precision at 0.912 and boasted the best generator switch recognition rate of 0.867. it’s clear that machine learning techniques consistently outperformed traditional statistical methods when it came to navigating the complex, non-linear patterns typical of nigeria’s power system. ensemble method performance the ensemble approach, specifically calibrated for nigerian grid conditions, achieved superior results: the ensemble method successfully met the research goal of surpassing 89% precision while also maintaining a high recall rate. this really highlights how effective it can be to combine different approaches to tackle the challenges of nigeria’s intricate grid environment. nigerian grid anomaly characterization detected anomalies were categorized based on nigeria’s specific grid challenges, as illustrated in figure 3. figure 3: distribution of anomaly types in the nigerian power grid. this horizontal bar chart illustrates the breakdown of 2,047 detected anomalies in nigeria’s power grid (2021–2023), highlighting six key categories. the x-axis gives a quantitative measure of percentage anomaly and y-axis gives a qualitative classification of the different types of operational anomalies pa ge 16 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 154-165, 2025 detailed anomaly analysis the in-depth analysis of anomalies has uncovered several key factors that contribute to grid instability and operational hiccups. the most critical warning sign of a potential grid collapse, which accounts for 28.4% of cases, is frequency deviations that go beyond ±0.5 hz, a phenomenon observed before 89% of actual collapses. moreover, voltage fluctuations exceeding ±5% across various substations and sudden load rejections over 500 mw have emerged as significant indicators. generator switching anomalies, which make up 22.7% of the findings, reveal unusual activation patterns during times when there are no outages, simultaneous switching across different regions, and generators running longer than their usual backup time. these issues highlight the need for more vigilant monitoring of generator operations to avert possible failures. load shedding irregularities, representing 19.3% of the analysis, were marked by unexpected shedding events, erratic patterns among distribution companies, and delays in restoring load that went beyond planned timelines. this inconsistency can complicate grid management even further. fuel supply disruptions accounted for 15.8% of the anomalies, with drops in output from gas-fired plants aligning with supply data from the nigerian national petroleum corporation (nnpc). gradual declines in fuel consumption suggest a looming shortage, worsened by regional differences in gas availability. weather-related anomalies, which comprised 8.9% of the findings, included the effects of harmattan dust storms on transmission lines, equipment failures caused by lightning, and flooding impacts on distribution infrastructure. these environmental factors present serious risks to operational stability. lastly, the indicators of economic activity, which made up 4.9% of the analysis, highlighted shifts in consumption patterns tied to industrial activity, regional economic differences impacting demand, and unusual trends related to holidays and cultural events. grasping these economic factors is crucial for predicting demand changes and maintaining grid reliability. economic impact assessment the economic impact analysis of the framework showed promising potential benefits for nigeria, as outlined in table 8. table 8: the economic impact impact category annual benefit (₦ billion) confidence level methodology direct benefits reduced grid collapse incidents. 89.4 high (85%) historical loss data × prevention rate improved generator efficiency. 23.7 medium (72%) fuel cost savings × efficiency gains enhanced maintenance scheduling. 15.2 high (88%) equipment damage avoidance subtotal direct. 128.3 indirect benefits. manufacturing productivity gains. 156.8 medium (68%) industrial output correlation healthcare system reliability. 45.2 medium (71%) emergency response improvement educational sector benefits. 28.6 low (58%) learning continuity value small business productivity. 67.3 medium (65%) informal economy impact subtotal indirect. 297.9 total annual benefits. 426.2 implementation costs the return on investment (roi) analysis reveals significant financial benefits over a five-year period. the total benefits are projected to reach ₦2,131.0 billion, while the total costs incurred amount to ₦28.6 billion. this result in an impressive net roi of 7,348%, indicating a highly favorable return relative to the investment made. additionally, the payback period is notably short, at just 3.2 months, suggesting that the initial investment will be recovered quickly, further underscoring the project’s financial viability. table 9: five-year implementation cost analysis cost category year 1 (₦ billion) years 2-5 (₦ billion/year) total 5-year (₦ billion) initial deployment. 8.4 8.4 annual operating costs. 2.1 2.1 10.5 training and capacity building. 1.3 0.5 3.3 infrastructure upgrades. 3.2 0.8 6.4 total annual cost. 15 3.4 28.6 pa ge 16 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 154-165, 2025 computational performance the framework was optimized for nigeria’s infrastructure limitations, achieving practical deployment feasibility as shown in table 10. table 10: computational performance in nigerian infrastructure context performance metric specification target achieved status processing speed. observations/second >1,000 2,941 ✓ exceeded memory efficiency. peak ram usage <2 gb 1.8 gb ✓ met power consumption. watts during operation <100w 87w ✓ met offline capability. hours without internet >48 hours 72 hours ✓ exceeded mobile responsiveness. load time on 3g <5 seconds 2.8 seconds ✓ met hardware compatibility. min. server specs mid-range compatible ✓ met the framework really shines in meeting all its performance goals, especially when it comes to offline capability (72 hours) and processing speed (2,941 observations per second). this makes it a great fit for nigeria’s infrastructure challenges. discussion framework effectiveness in nigerian context the findings show that the proposed framework effectively tackles the unique challenges faced by nigeria’s power system. with an impressive 89.4% precision and 84.2% recall, the ensemble approach marks a significant leap forward compared to the manual monitoring methods currently employed by tcn and distribution companies. its ability to predict grid failures with 82.3% accuracy could truly transform the reliability of nigeria’s power sector. incorporating nigeria-specific elements like generator switching patterns, weather impacts, and cultural consumption habits was essential for achieving such high accuracy. when traditional international algorithms were applied directly to nigerian data, they performed poorly, with accuracy dropping by 25-40%. this clearly underscores the need for adaptations that are tailored to the local context. comparative analysis with international standards table 11 provides a comparison of our framework’s performance against international grid monitoring systems. table 11: framework performance vs. international grid monitoring standards performance metric our framework (nigeria) us grid monitoring european standards developing country average anomaly detection precision. 89.40% 94.20% 92.80% 76.30% grid collapse prediction. 82.30% 91.50% 89.70% 65.20% false alarm rate. 8.90% 5.80% 6.40% 18.70% economic roi. 7348% 456% 523% 892% cultural adaptation score. 95.20% n/a n/a 67.40% not only does it illustrate competitive performance but also solves the distinct problems which nigerian systems pose and too often cannot with global solutions. the astronomical economic return on investment illustrates exactly how much grid instability affects developing countries versus the relatively stable grids of developed economies. practical implications for nigerian power sector stakeholders for tcn, the use of early warning systems would bring some unbelievable benefits, like being able to prevent up to 68% of grid collapses that could have been avoided in the first place. also, by streamlining maintenance schedules, tcn could lower planned outages by 31%. increased coordination between generation and distribution centers would enhance efficiency as well. besides that, automating compliance reporting to the nigerian electricity regulatory commission (nerc) would really streamline regulatory procedures. but for all this to become a reality, tcn would need a six-month timeframe for transitioning with available scada systems, a full training program for over 150 operators, and real-time decision-support systems for those crucial moments of grid emergencies. for the distribution companies (discos), the payoff includes fewer complaints from customers as a result of outages being actively managed and smarter load balancing through their systems. apart from ensuring increased revenue collection at an estimated 15% rate on the basis of improved reliability of supply, it also increases customer satisfaction through better communication. to achieve these benefits, discos will need to personalize individual dashboards, integrate with existing billing and customer management systems, and set up mobile alert systems for their field agents. for end users and the wider nigerian economy, the payback is huge. a reliable source of power guarantees that businesses can budget more efficiently and utilize pa ge 16 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 154-165, 2025 fewer expensive generators, and small and medium-sized businesses (smes) can save as much as ₦180,000 a year. access to reliable electricity can transform lives more than just enhancing the quality of life, it enhances productivity in home-based businesses and ensures hospitals have the reliable power to deliver effective healthcare delivery. additionally, with schools having constant electricity, learning accomplishment is enhanced and constant internet connectivity bridges the digital divide. such gains have high social implications, creating economic development and higher living standards throughout nigeria. challenges and solutions for nigerian deployment the research emphasizes infrastructure issues and culture influencing nigeria’s roll-out of monitoring systems. inadequate rural internet connectivity necessitated the introduction of offline mode, allowing for 72-hour standalone operation. unreliable power supply to monitoring systems was corrected using solar-powered outlets and battery backup systems. shortage of technical staff was handled using multilingual interfaces and comprehensive training programs. social and cultural factors, including local languages that influence the need for multilingual interfaces, have impacted the adoption of automated systems in nigeria. sms alerts and integration with whatsapp are existing communication channels imbedded for public outage communication. however, as is normally the case with current framework limitations, it exposes the inherent issues affecting performance and implementation. limitations and future research directions the framework for nigeria’s energy system faces several challenges, including poor data quality in rural areas, inadequate cyber security measures, scalability issues, and economic model assumptions. additionally, locational differences necessitate algorithm modifications for specific regions. the integration of older systems within some distribution companies requires significant upgrades. future research directions include merging renewable sources, advanced cyber security systems, increasing connectivity, and applying artificial intelligence for predictive maintenance. medium-term goals include smart meters, cross-border grid surveillance, and climate change-resilient algorithms. policy implications and recommendations the recommendations for the nigerian electricity regulatory commission (nerc) emphasize the need for a robust regulatory framework to enhance grid monitoring and security, i.e., mandatory standards for distribution companies, data sharing protocol, and cyber security standards. the federal ministry of power recommends strategic investment and partnerships for the development of national grid monitoring infrastructure, including budgeting, public-private partnerships, and grid operator training programs. these actions are meant to improve nigeria’s electricity grid reliability and security. for the federal ministry of power, the recommendations focus on strategic investments and partnerships to bolster national grid monitoring infrastructure. this includes allocating a budget specifically for this purpose, fostering public-private partnerships to facilitate system deployment, and developing training programs aimed at enhancing the skills of grid operators. furthermore, establishing funding for research and development is essential to ensure ongoing improvements in grid management and technology. these measures collectively aim to strengthen the reliability and security of nigeria’s electricity grid. conclusion the study presents a framework for monitoring nigeria’s power system’s grid stability using time series analysis and machine learning techniques. the ensemble anomaly detection approach has shown remarkable efficacy, with an accuracy rate of 89.4% and recall of 84.2%, outperforming conventional threshold-based systems. adopting this strategy could yield significant economic gains of ₦426.2 billion annually and a 7,348% return on investment. the model accurately predicts 82.3% of grid outages and could prevent a possible 68% of failures. its multi-stakeholder dashboards and mobileaware interfaces guarantee system responsiveness and control, improving responsive monitoring among tcn, discos, and end-users. the study provides realistic recommendations for resource-constrained environments and a strong economic case for investing in future-grid monitoring technology. it also provides evidence-based recommendations for strengthening grid monitoring legislation and harmonizing regional and continental power grids. the study suggests flexible solutions to improve power sector reliability: for the immediate action (0-6 months) pilot rollout of distribution networks in lagos and abuja, partnership formation, operator training programs, and cyber security measures; medium term (6-18 months) expansion of deployment across all six geopolitical zones, integration with existing scada and billing systems, public alert systems for outage 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(2023). edge computing architectures for power system monitoring with limited connectivity: design principles and performance evaluation. ieee internet of things journal, 10(8), 6789-6804. https://doi.org/10.1109/ jiot.2023.6789804 pa ge 1 pa ge 12 7 american journal of applied statistics and economics (ajase) maximizing predictive regression and dimensionality reduction techniques: evidence from monte carlo’s simulation study oluwafemi clement onifade1*, samuel olayemi olanrewaju1, emmanuel segun oguntade1 volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.5938 https://journals.e-palli.com/home/index.php/ajase article information abstract received: august 16, 2025 accepted: september 19, 2025 published: october 18, 2025 this study proposes a novel two-step sparse learning framework that combines sparse principal component regression (spcr) with regularization methods, lasso, elastic net, ridge, and smoothly clipped absolute deviation (scad), to improve prediction and interpretability in high-dimensional settings. simulation experiments were conducted under varying sample sizes, dimensionality levels, sparsity conditions, and predictor correlations to evaluate the performance of the hybrid estimators in comparison to traditional penalization approaches. results show that spcr-lasso and spcr-enet consistently deliver superior accuracy and stability in high-dimensional, multicollinear contexts, with spcrenet performing particularly well in extreme dimensionality. spcr-scad demonstrated advantages in sparse, low-correlation scenarios, while ridge regression contributed modest improvements. these findings underscore that estimator performance is strongly datadependent and highlight the value of spcr hybridization for mitigating multicollinearity while enhancing interpretability. the study offers practical guidance for applied researchers in fields such as genomics, finance, and climate science, and contributes methodologically by demonstrating the robustness of spcr-based regularization in handling complex highdimensional data structures. keywords elastic net, high-dimensional data, lasso, multicollinearity, regularization, scad, spcr 1 department of statistics, faculty of science, university of abuja, abuja, nigeria * corresponding author’s e-mail: onifade.oluwafemi@yahoo.com introduction high-dimensional modelling has emerged as a critical and transformative area of research with profound implications across diverse domains, including data science, machine learning, and statistics. the prominence of high-dimensional data can be attributed to the prevalence of large-scale datasets and complex systems in various applications. in high-dimensional modelling, the term “high dimensional” refers to situations where the number of explanatory variables, denoted as p, exceeds the number of observations, n (i.e., p > n). this phenomenon has gained attraction due to the rapid advancements in technology, which enable the collection of a vast number of variables to better understand complex phenomena of interest. the applicability of high-dimensional modelling spans multiple fields, including computational chemistry, chemometrics with spectral data, genomics, fmri data analysis, large-scale healthcare analytics, text/image analysis, astronomy, and many others. the versatility of high-dimensional modelling techniques has also been demonstrated in the field of drug discovery and development. for example, priya et al. (2022) focus on the application of machine-learning approaches in chemo-informatics for drug discovery. machine learning techniques, specifically qsar (quantitative structureactivity relationship), have effectively modelled various physicochemical properties of drugs, including toxicity, absorption, and drug-drug interactions. these approaches, being a subset of artificial intelligence, show great potential in drug discovery by handling nonlinear datasets and big data with increasing complexity. however, the curse of dimensionality, a well-known challenge in high-dimensional data, poses significant obstacles to accurate predictions and efficient parameter estimation. the exponential growth of data volume with increasing variables leads to sparse data points, which can hinder the effectiveness of traditional methods. multicollinearity, a common issue in high-dimensional datasets, further complicates parameter estimation and can result in inflated confidence intervals. to address these challenges, sophisticated techniques are required that can effectively handle the complexities posed by high-dimensional data. dimensionality reduction and variable selection methods have emerged as attractive strategies to tackle high-dimensional studies. over the last two decades, regularization approaches such as lasso, elastic net, ridge regression, and smoothly clipped absolute deviation (scad) have become the methods of choice for analyzing high-dimensional data. these regularization methods have been extensively applied in various disciplines, including statistics (nwosu et al. 2024), chemo-informatics (song et al., 2024), epidemiology (cleophas et al. 2024), and bioinformatics (kitano et al., 2024), and many others. in recent years, sparse principal component regression (spcr), and sparse partial least squares (spls), has garnered attention as a potential solution to improve predictive model accuracy. by identifying a small subset of the original predictor variables that capture most of the variance in the data, spcr facilitates highly interpretable models with enhanced predictive accuracy. spcr has demonstrated promising results in various fields, pa ge 12 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 127-140, 2025 including medical research, finance, and environmental sciences. spcr has also proven successful in qsar modelling by identifying the most relevant molecular descriptors that greatly influence biological activity or molecule properties. for example, zhang et al. (2024) demonstrated the effectiveness of spcr in identifying the most important features for predicting the antitumor activity of molecules, leading to more reliable qsar models. while previous studies have predominantly focused on combining principal component regression (pcr) with regularization techniques in low-dimensional settings, where the number of predictors is less than the observations, there is a clear need for sparse pcr. sparse pcr can be more advantageous in situations with a large number of predictor variables, as it identifies a smaller subset of the original predictors that are most crucial in predicting the response variable. in this thesis, we aim to address the challenges posed by high-dimensional data through the application of regularization techniques and sparse principal component regression (spcr). by developing a novel two-step sparse learning approach that integrates sparse principal component regression (spcr) with regularization techniques (ridge regression, lasso, elastic net, and smoothly clipped absolute deviation). this combined approach seeks to enhance predictive accuracy and interpretability in high-dimensional datasets, particularly in scenarios involving multicollinearity and sparsity. the specific objectives include to: i. develop efficient framework that combine spcr with regularization methods. ii. assess the performance of the combined approach using traditional modeling techniques like lasso and ridge through predictive accuracy measures, i.e. mean square error. iii. design a simulation study to demonstrate the robustness of the proposed approach across multiple high-dimensional datasets, varying in sample size, multicollinearity, and sparsity levels. literature review empirical work on high-dimensional prediction has converged on two broadly successful strategies. the first reduces dimensionality via latent factors or components (e.g., principal component regression — pcr), which mitigates multicollinearity and variance inflation (jolliffe, 2002; hastie et al., 2009). the second directly penalizes regression coefficients to induce shrinkage and (sometimes) sparsity (ridge, lasso, elastic net, scad), which controls overfitting and performs variable selection when appropriate (hoerl & kennard, 1970; tibshirani, 1996; zou & hastie, 2005; fan & li, 2001). empirical comparisons show neither approach dominates across all data regimes: pcr is robust under extreme multicollinearity but produces components that are not tailored to prediction of the response, while penalized regressions are powerful for sparse signals but can struggle when predictors are highly correlated (hastie et al., 2009). to bridge the gap between unsupervised dimension reduction and predictive goals, researchers developed sparse principal component analysis (spca) and sparse principal component regression (spcr). spca (zou et al., 2006; witten et al., 2009) imposes sparsity on loadings so principal components involve only a subset of predictors, improving interpretability without discarding the variance-reduction benefit of pca. empirical studies in genomics, chemometrics, and neuroimaging have found spca yields components that are easier to interpret and often more useful as inputs for supervised tasks than dense pca components. spcr, either formulated as a one-stage joint optimization of component extraction and regression loss or as a carefully tuned two-stage procedure, goes further by explicitly constructing components that optimize predictive performance (kawano, 2018; zou et al., 2006). empirical comparisons show spcr often outperforms classical pcr when the directions of maximal predictor variance differ from the directions most predictive of the outcome (i.e., when supervised signal does not align with principal variance directions). applications in biological data and other high-dimensional domains report improved prediction and sparser, more actionable component loadings (zou et al., 2006; kawano, 2018). there is substantial empirical evidence that different regularizers perform differently depending on correlation structure and sparsity. ridge excels when many predictors carry signal but are highly correlated; it reduces variance without producing sparse solutions, often improving outof-sample prediction in dense-signal, collinear settings (hoerl & kennard, 1970). also, lasso provides both shrinkage and variable selection and works well when the true model is sparse and predictors are not excessively collinear; empirical studies show it can fail to reliably select the “correct” group in the presence of strong predictor correlation (tibshirani, 1996). elastic net empirically combines strengths of ridge and lasso, grouping correlated predictors while performing variable selection; simulation and applied work show elastic net often outperforms lasso under grouped-correlated designs (zou & hastie, 2005). scad and other nonconvex penalties (fan & li, 2001) demonstrate favorable oracle properties in theory and often reduced bias empirically compared to lasso, but they require careful tuning and are more sensitive to initialization and optimization choices. empirical simulation studies repeatedly demonstrate there is no uniformly best penalty: performance depends on (i) sparsity level, (ii) inter-predictor correlation, (iii) signal strength, and (iv) sample size. this motivates this study that systematically maps performance across different scenarios rather than relying on single-case comparisons. materials and methods development of novel two-step sparse learning techniques to address the challenges of high-dimensional data analysis, this study integrates sparse principal component pa ge 12 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 127-140, 2025 regression (spcr) with regularization techniques such as ridge, lasso, elastic net, and scad. the proposed twostep sparse learning approach combines the strengths of dimensionality reduction (spcr) with the variable selection and regularization capabilities of these methods, ensuring both interpretability and predictive accuracy. step 1: dimensionality reduction using sparse principal component regression (spcr) workflow compute sparse principal components t=xw by solving: minimize‖y-xwα‖2 2+λ1 ‖w‖1+λ2 ‖w‖f’ 2 where w is the matrix of sparse component weights, α is the regression coefficient vector, and λ1 and λ2 control sparsity and shrinkage. i. select the top k components based on the proportion of variance explained and their relevance to y. ii. output the reduced dataset t, a sparse representation of x. step 2: regularized regression on reduced components after dimensionality reduction, apply regularized regression techniques (ridge, lasso, elastic net, and scad) to the reduced dataset t to build predictive models while managing overfitting and multicollinearity. this means that the resulting equations involve combining the dimensionality reduction framework with the respective penalty functions of the chosen regularization technique. ridge regression with spcr objective function: minimize‖y-tβ‖2 2+λ‖β‖2 2 where, t=xw: sparse components derived using spcr, λ: regularization parameter controlling the degree of shrinkage. β: regression coefficients ‖β‖2 2 :l2-norm penalty that shrinks all coefficients toward zero but does not enforce sparsity, lasso regression with spcr objective function: minimize‖y-tβ‖2 2+λ‖β‖1, where: t=xw: sparse components from spcr, ‖β‖1: l1-norm penalty that enforces sparsity by shrinking some coefficients to exactly zero, lasso regularization enhances variable selection by retaining only the most relevant components or predictors. elastic net with spcr objective function: minimize ‖y-tβ‖2 2+λ1‖β‖1+λ2‖β‖2 2 where, t=xw: sparse components from spcr, β: regression coefficients ‖β‖1: enforces sparsity (lasso component), ‖β‖2 2: mitigates multicollinearity and provides stability (ridge component), λ1: controls sparsity, λ2: controls shrinkage. elastic net is particularly effective when predictors are highly correlated, as it selects groups of correlated components. scad with spcr objective function: minimize‖y-tβ‖2 2+∑j=1 ppλ (|βj|), where, t=xw: sparse components from spcr, β: regression coefficients pλ (|βj|) is the scad penalty function, λ controls the penalty’s strength. advantages of combining regularization with spcr i. spcr reduces dimensionality while ridge or lasso enhances the predictive power by handling multicollinearity or enforcing sparsity. ii. scad further refines the predictor selection process, reducing bias for large coefficients while retaining sparse predictors. simulation study the simulation study aims to evaluate and compare the performance of sparse learning methods (lasso, ridge, elastic net, scad, and spcr) under controlled and varied conditions. this study focuses on predictive accuracy, interpretability, and computational efficiency, providing insights into the strengths and weaknesses of these methods in high-dimensional settings. the following sections detail the design, dataset characteristics, evaluation metrics, comparative testing procedures, and the approach for data analysis and interpretation design of the simulation study the simulation study replicates real-world challenges by systematically varying key parameters: sample size, predictor dimensionality, levels of multicollinearity, noise, and sparsity. these variations ensure a comprehensive evaluation of the methods’ performance across diverse conditions, reflecting practical scenarios in highdimensional data analysis. sample sizes four small sample scenarios are considered: n=30, n=50, n=70 and n=100. the small sample sizes (n=30 and n=50) represent the most challenging setting where predictors (p) far exceed observations (p>n), crucial for assessing the methods’ ability to avoid overfitting. while higher small sample sizes (i.e. n=70 and n=100) explore scalability and performance in more balanced or lowdimensional settings. predictor dimensionality predictor dimensionality (p) varies from low (p=20) to high (p=200). low-dimensional scenarios allow methods to demonstrate baseline predictive capabilities pa ge 13 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 127-140, 2025 without dimensionality-related challenges. moderate and high-dimensional settings introduce significant computational and statistical challenges, such as sparsity and multicollinearity. multicollinearity multicollinearity is varied at four levels: low (ρ~0.1): predictors are weakly correlated, minimizing interference among variables. moderate (ρ~0.5): predictors form correlated blocks, testing methods like elastic net and sprc designed to handle such scenarios. high (ρ~0.9): many predictors are extremely interrelated, challenging methods like lasso, which may arbitrarily select variables from correlated groups. sparsity predictor sparsity is varied to test variable selection capabilities: • sparse (10% non-zero coefficients): only a small fraction of predictors are relevant, providing a benchmark for variable selection. • dense (30% non-zero coefficients): many predictors have small, non-zero effects, testing the methods’ capacity to identify subtle contributions. data generation process the data is generated as: y = xβ+ϵ where: x~n(0,σ), with σij=ρ for i≠j), controlling the level of multicollinearity β is a sparse vector with randomly assigned non-zero coefficients drawn from n(0,1), ∈~n(0,σ2) is gaussian noise adjusted to achieve desired r2. evaluation metrics performance will be assessed using the following metrics mean squared error (mse) mse measures the average magnitude of the errors in the predicted values. it is often preferred over mse as it is in the same units as the response variable, making it easier to interpret. the formula for mse is: mse=1/n ∑i=1 n(yi-y^i) 2 the estimation model with lowest mse would be considered the best. afterwards, the r-squared of the returned best model would be assessed. r-squared (r2) indicates the proportion of variance explained by the predictors, with higher values representing better model fit: r2=1-(∑i=1 n(yi-y^i) 2 )/(1/n ∑i=1 n(yi-y¯i) 2 ) results and discussion simulated data presentation and preliminary assessment this section presents and discusses the simulated response variable and explanatory variables at different scenarios of high dimensionalities as shown in table 1, table 2, table 3 and table 4. the simulated response variable and explanatory variables at each scenarios were generated using y = xβ+ϵ; where: x~n(0,σ), with σij= ρ for i≠j), controlling the level of multicollinearity β is a sparse vector with randomly assigned non-zero coefficients drawn from n(0,1), ∈~n(0,σ2)is gaussian noise adjusted to achieve desired r2. table 1: summary statistics of the simulated response variables and last 7-explanatory variables at sample size 30 no of predictors 50 variables y x44 x45 x46 x47 x48 x49 x50 mean 1.324 0.0594 -0.010 0.0731 -0.074 -0.022 0.228 0.147 median 1.966 0.115 -0.060 0.117 -0.104 0.142 0.472 0.090 min. -7.318 -1.916 -2.043 -1.994 -2.250 -2.129 -2.08 -1.615 max. 11.453 2.401 2.479 2.309 2.065 1.410 2.158 2.236 no of predictors 70 variables y x64 x65 x66 x67 x68 x69 x70 mean 1.386 -0.098 -0.025 0.039 0.039 0.038 0.082 0.009 median 2.610 -0.092 0.039 0.090 0.080 0.048 -0.124 -0.055 min. -12.311 -1.823 -1.564 -1.552 -1.609 -1.823 -1.678 -2.005 max. 14.267 1.833 1.956 2.013 1.941 1.833 2.217 1.948 no of predictors 200 variables y x194 x195 x196 x197 x198 x199 x200 mean -0.497 0.036 0.047 -0.006 0.088 0.153 0.194 0.147 median 0.344 -0.202 -0.095 -0.175 -0.152 0.063 -0.048 -0.115 min. -15.905 -2.442 -2.609 -2.907 -2.132 -2.079 -1.888 -2.019 max. 13.879 2.539 2.532 2.787 2.764 2.834 2.566 2.488 source: researchers’ compilations from r-output pa ge 13 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 127-140, 2025 table 2: summary statistics of the simulated response variables and last 7 independent variables at sample size 50 no of predictors 70 variables y x44 x45 x46 x47 x48 x49 x50 mean -0.048 0.062 -0.101 -0.125 -0.099 0.067 -0.106 -0.014 median 0.237 0.065 -0.064 -0.145 0.114 -0.217 0.057 -0.167 min. -8.288 -2.070 -2.035 -2.378 -3.486 -1.716 -3.099 -1.494 max. 7.115 2.594 2.245 2.372 2.079 2.811 1.618 1.872 no of predictors 100 variables y x94 x95 x96 x97 x98 x99 x100 mean -1.232 0.163 -0.106 -0.013 0.159 0.234 0.206 0.054 median -0.968 0.161 -0.023 -0.024 0.066 0.128 0.188 -0.086 min. -20.891 -1.975 -3.081 -1.798 -3.041 -2.014 -2.255 -1.978 max. 25.595 2.494 2.305 1.374 2.568 3.348 2.241 2.529 no of predictors 200 variables y x194 x195 x196 x197 x198 x199 x200 mean -0.375 0.069 0.096 0.071 0.080 0.065 0.063 0.071 median -0.808 0.147 0.235 0.376 0.313 0.349 0.349 0.368 min. -10.372 -2.383 -2.492 -2.409 -2.333 -2.398 -2.475 -2.467 max. 20.619 1.669 1.574 1.607 1.627 1.641 1.577 1.572 source: researchers’ compilations from r-output table 3: summary statistics of the simulated response variables and last 7 independent variables at sample size 70 no of predictors 100 variables y x94 x95 x96 x97 x98 x99 x100 mean -0.472 0.027 0.006 0.085 0.024 0.026 -0.075 0.034 median -1.048 -0.001 0.117 0.107 0.008 -0.079 -0.031 -0.051 min. -10.022 -2.215 -2.177 -2.139 -1.665 -2.279 -3406 -2.084 max. 10.800 2.041 1.928 1.884 2.376 2.274 2.203 2.419 no of predictors 150 variables y x144 x145 x146 x147 x148 x149 x150 mean 0.537 -0.114 -0.148 -0.157 -0.159 -0.178 -0.148 -0.147 median 1.510 -0.245 -0.202 -0.195 -0.243 -0.225 -0.202 -0.215 min. -15.961 -2.112 -1.982 -1.688 -1.980 -1.995 -1.960 -1.995 max. 19.764 2.894 3.079 3.063 2.869 3.028 3.328 3.134 no of predictors 200 variables y x194 x195 x196 x197 x198 x199 x200 mean -0.444 0.022 0.003 0.001 0.013 -0.043 -0.026 -0.0001 median -0.695 -0.124 -0.110 -0.194 -0.109 -0.077 -0.077 -0.101 min. -7.515 -1.702 -1.643 -1.661 -1.711 -1.659 -1.575 -1.492 max. 7.247 2.599 2.542 2.495 2.553 2.492 2.144 2.257 source: researchers’ compilations from r-output pa ge 13 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 127-140, 2025 explicitly, table 1 presents the summary statistics of simulated response-variables and last 7-explanatory variables at different scenarios of p>(n=30), as p was varied across 50, 70 and 200. the n=30 is our first small sample settings representing the most challenging setting where predictors (p) far exceed observations. according to the table at (p=50)>(n=30), the simulated responsevariable has mean of 1.32 ranges between -17.32 and 11.45. also, the table show that at (p=70)>(n=30) the simulated response-variable has mean of 1.39 ranges between -12.31 and 14.27. likewise, table 1 reveals that at (p=200)>(n=30) the simulated response-variable has mean of -0.497 ranges between -15.91 and 13.88. similarly, table 2 presents the summary statistics of simulated response-variables and last 7-explanatory variables at different scenarios of p>(n=50), as p was varied across 70, 100 and 200. the n=50 is our second small sample settings also representing the most challenging setting where predictors (p) far exceed observations. according to the table at (p=70)>(n=50), the simulated response-variable has mean of -0.048 ranges between -8.29 and 7.12. also, the table show that at (p=100)>(n=50) the simulated response-variable has mean of -1.232 ranges between -20.89 and 25.59. in addition, table 2 reveals that at (p=200)>(n=50) the simulated response-variable has mean of -0.375 ranges between -10.37 and 20.62. furthermore, table 3 presents the summary statistics of simulated response-variables and last 7-explanatory variables at different scenarios of p>(n=70), as p was varied across 100, 150 and 200. the n=70 is our first higher small sample size considered to explore scalability and performance in more balanced or low-dimensional settings. according to the table at (p=100)>(n=70), the simulated response-variable has mean of -0.472 ranges between -10.022 and 10.800. also, the table show that at (p=150)>(n=70) the simulated response-variable has mean of 0.537 ranges between -15.961 and 19.764. table 3 further reveals that at (p=200)>(n=70) the simulated response-variable has mean of -0.444 ranges between -7.515 and 7.247. moreover, table 4 presents the summary statistics of simulated response-variables and last 7-explanatory variables at different scenarios of p>(n=100), as p was varied across 120, 150 and 200. the n=100 is our second higher small sample size considered to explore scalability and performance in more balanced or low-dimensional settings. according to the table at (p=120)>(n=100), the simulated response-variable has mean of -0.086 ranges between -24.882 and 23.759. also, the table show that at (p=150)>(n=100) the simulated response-variable has mean of 0.164 ranges between -10.883 and 12.130. table 4 further reveals that at (p=200)>(n=100) the simulated response-variable has mean of 0.449 ranges between -11.441 and 12.423. based on the foregoing it is quite evident that simulated dataset obviously exhibits high-dimensionality problem (i.e. p>n), thus necessitate advanced methods of regression estimation other than the ols. performance assessment of ridge, lasso, elastic net, scad and the novel two-step sparse learning methods under high dimensionality and multicollinearity this section presents and discusses the performances of the celebrated ridge, lasso, elastic net, scad and our table 4: summary statistics of the simulated response variables and last 7 independent variables at sample no of predictors 120 variables y x114 x115 x116 x117 x118 x119 x120 mean -0.086 0.132 0.085 -0.052 0.044 -0.126 -0.065 -0.008 median 1.026 0.158 -0.005 -0.001 0.051 -0.006 0.016 0.089 min. -24.882 -2.411 -2.646 -3.345 -2.594 -2.825 -3.218 -2.975 max. 23.759 1.936 2.944 3.344 2.349 2.028 2.685 3.341 no of predictors 150 variables y x144 x145 x146 x147 x148 x149 x150 mean 0.164 0.047 -0.042 -0.053 0.089 -0.086 -0.202 -0.045 median 0.588 0.020 0.049 -0.178 0.109 -0.144 -0.263 -0.018 min. -10.883 -2.446 -2.409 -2.554 -2.487 -2.239 -2.630 -2.769 max. 12.130 3.384 2.371 2.677 2.796 2.784 2.064 2.635 no of predictors 200 variables y x194 x195 x196 x197 x198 x199 x200 mean 0.449 -0.146 -0.047 -0.079 -0.077 -0.076 -0.070 0.070 median 0.333 -0.125 0.064 -0.089 -0.036 -0.184 0.053 0.082 min. -11.441 -2.284 -2.749 -3.593 -2.269 -1.879 -3.431 -2.265 max. 12.423 2.781 3.419 3.087 1.808 2.134 3.174 3.102 source: researchers’ compilations from r-output pa ge 13 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 127-140, 2025 four novel two-steps sparse regression models towards providing a robust regression model for the simulated response-variables under the high dimensionality scenarios (as presented in the previous section) and multicollinearity problems. table 5 presents the assessment results (i.e. mses) of each ridge, lasso, elastic-net, scad and the novel twostep sparse learning regression models under problem of high dimensionality and multicollinearity at small sample sizes (i.e. 30 and 50). explicitly, for sample size 30 at 10% sparsity table 5 reveals lowest mses of 0.001, 0.0167 and 0.00006 for lasso estimator when p=50 (i.e. low high-dimensional) at low correlation (r=0.1), moderate correlation (r=0.5) and when p=200 at moderate correlation (r=0.5) levels respectively. the table further depicts lowest mses for the novel spcrlasso estimator when p=50; r=0.9 (mse = 0.0143) i.e. low high-dimension with high correlation, p=70; r=0.1 (mse=0.0071),p=70;r=0.5 (mse=0.0025) & p=70; r=0.9 (mse=0.0671) i.e. moderate high-dimensional with any table 5: summary statistics of the simulated response variables and last 7 independent variables at sample n sparsity 10 p 50 70 200 r 0.1 0.5 0.9 0.1 0.5 0.9 0.1 0.5 0.9 30 ridge 22.2261 14.7271 5.5648 4.7699 7.8126 4.7783 2.8892 8.78595 4.3978 lasso 0.0010 0.0167 0.1172 0.0638 0.0211 0.1235 0.000019 0.000063 0.00516 enet 0.0041 0.0269 0.1988 0.0334 0.0192 0.2428 0.000082 0.000372 0.01729 scad 2.5382 0.7175 1.2063 0.1656 0.3210 0.9581 0.1454 0.2581 1.34518 spcr-ridge 1.7273 1.5237 0.5399 0.6035 0.2873 0.5755 0.0539 0.4525 0.17657 spcr-lasso 0.3215 0.1458 0.0143 0.0071 0.0025 0.0671 0.0000004 0.007986 0.003849 spcr-enet 1.0575 0.2012 0.0351 0.0672 0.0342 0.0997 0.000696 0.07211 0.011083 spcr-scad 0.2566 0.0348 0.0577 0.1156 0.0055 0.1057 0.0001347 0.04886 0.044825 sparsity 30 ridge 38.9309 34.5543 5.0801 51.7066 26.5211 11.1444 15.8539 12.43887 4.99299 lasso 0.0078 0.7775 0.7791 0.0516 0.0462 0.0665 0.04935 0.000873 0.00397 enet 0.0871 1.2908 0.4971 0.1924 0.0218 0.1709 0.17285 0.000262 0.01215 scad 3.9459 0.9614 1.2167 1.6763 0.9853 4.9219 0.21469 0.81469 3.17956 spcr-ridge 38.7535 28.7759 17.0134 34.2091 22.8729 175.7768 15.5972 33.1529 15.2773 spcr-lasso 0.9465 1.8579 0.3021 0.0269 0.0173 0.0511 0.04379 0.06934 0.17663 spcr-enet 2.1343 5.9747 0.9162 7.4662 0.0632 0.2739 0.11784 0.30761 0.43285 spcr-scad 0.9149 1.6942 1.1840 1.4363 0.0753 0.4019 0.08979 0.07658 0.26952 sparsity 10 p 50 70 200 50 ridge 14.27529 13.14787 3.99444 7.59169 11.5585 2.51970 10.93393 17.68289 3.68972 lasso 0.024283 0.01111 0.26847 0.001597 0.00047 0.12309 0.000056 0.00016 0.00761 enet 0.035625 0.03055 0.32753 0.002292 0.00199 0.10761 0.005829 0.00089 0.03019 scad 0.137990 0.37474 0.71736 0.135452 0.28281 0.34494 1.51859 0.20688 0.81663 spcr-ridge 1.59547 1.50312 0.69733 0.961469 0.41246 0.3449 1.16773 0.14019 0.19149 spcr-lasso 0.071019 0.20712 0.07296 0.031501 0.00401 0.14790 0.0000021 0.02395 0.00096 spcr-enet 01.80131 0.450867 0.08955 0.065121 0.03633 0.20077 0.009640 0.06199 0.00076 spcr-scad 0.002342 0.20008 0.27585 0.008672 0.00831 0.05008 0.001545 0.00298 0.00113 sparsity 30 ridge 28.72535 24.78267 10.92322 46.11221 39.1727 9.20752 25.78999 27.94949 12.2576 lasso 0.001752 0.04827 0.45687 0.00135 0.00376 0.12701 0.03429 0.05793 0.04912 enet 0.00692 0.12742 0.76926 0.00513 0.01369 0.28181 0.35494 0.20659 0.27805 scad 0.39620 1.59569 4.17981 2.22021 4.83648 1.49247 0.49354 0.36052 23.3093 spcr-ridge 1.790068 2.43327 1.00552 1.90385 0.99053 0.91456 19.86799 2.06924 0.70623 spcr-lasso 0.15863 0.31728 0.25098 0.04437 0.04119 0.01670 0.30157 0.09142 0.00785 spcr-enet 0.08939 0.54537 0.30180 0.26648 0.05336 0.01793 1.04003 0.23314 0.00523 spcr-scad 0.05241 0.79069 0.23878 0.07853 1.32367 0.01216 0.42382 0.08032 0.00686 source: researchers’ compilations from r-outputs pa ge 13 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 127-140, 2025 correlation levels, and p=200; r=0.1 (mse=0.0000004) & p=200; r=0.9 (mse=0.003849) i.e. high-dimensional with low and high correlation levels. in the same vein, figure 1 presents performance ranks of each estimator under varied levels of high dimensionality and multicollinearity for sample size 30 with 10% sparse. the figure similarly, ranks lasso estimator best when p=50 & r=0.1, p=50 & r=0.5, and p=200 & r=0.5 while spcr-lasso returned best rank estimator when p=50 & r=0.9, p=70 & r=0.1, p=70 & r=0.1, p=70 & r=0.5, p=70 & r=0.9, p=200 & r=0.1, and p=200 & r=0.9. figure 1: performance ranks of each estimator under varied levels of high dimensionality and multicollinearity for sample size 30 with 10% sparse figure 2: performance ranks of each estimator under varied levels of high dimensionality and multicollinearity for sample size 30 with 30% sparse similarly, for sample size 30 at 30% sparsity table 5 reveals lowest mses of 0.0078, 0.7775, 0.00087 and 0.00397 for lasso estimator when p=50 (i.e. low high-dimensional) at low correlation (r=0.1), moderate correlation (r=0.5) and high correlation (r=0.9) levels respectively. while when p=200 at moderate correlation (r=0.5), the table returned elastic net (enet) estimator with lowest mse of 0.00026. the table also depicts lowest mses for the novel spcrlasso estimator when p=50; r=0.9 (mse = 0.3021) i.e. low high-dimension with high correlation, p=70; r=0.1 (mse=0.0269),p=70;r=0.5 (mse=0.0173) & p=70; r=0.9 (mse=0.0511) i.e. moderate high-dimensional with any correlation levels, and p=200; r=0.1 (mse=0.04379) i.e. high-dimensional with low correlation level. in the same vein, figure 2 presents performance ranks of each estimator under varied levels of high dimensionality and multicollinearity for sample size 30 with 30% sparse. the figure similarly, ranks lasso estimator best when p=50 & r=0.1, p=50 & r=0.5, and p=200 & r=0.9. it also ranks elastic net estimator best when p=200 & r=0.5 while spcr-lasso returned best rank estimator when p=50 & r=0.9, p=70 & r=0.1, p=70 & r=0.1, p=70 & r=0.5, p=70 & r=0.9, and p=200 & r=0.1. pa ge 13 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 127-140, 2025 figure 4: performance ranks of each estimator under varied levels of high dimensionality and multicollinearity for sample size 50 with 30% sparse figure 3: performance ranks of each estimator under varied levels of high dimensionality and multicollinearity for sample size 50 with 10% sparse furthermore, considering small sample size of 50 at 10% sparsity table 5 reveals the lasso estimator with least mses of 0.0111, 0.0016, 0.0005 and 0.00016 when p=70 & r=0.5, p=100 & r=0.1, p=70 & r=0.5, and p=200 & r=0.5 respectively. meanwhile the table depicts the novel; spcr-lasso estimator with least mses when p=70 & r=0.9 (mse=0.07296) and p=200 & r=0.1 (mse=0.0000032), spcr-scad estimator with least mses when p=70 & r=0.1 (mse=0.002342) and p=100 & r=0.9 (mse=0.05008), and spcr-enet estimator with lowest mse when p=200 & r=0.9 (mse=0.00076). similarly, figure 3 presents the performance ranks of each estimator under varied levels of high dimensionality and multicollinearity for sample size 50 with 10% sparse. according to the figure, the lasso estimator was ranked best (i.e. 1st) on four occasions namely, p=70 & r=0.5, p=100 & r=0.1, p=70 & r=0.5, and p=200 & r=0.5. the novel spcr-lasso estimator was ranked best on two occasions namely, p=70 & r=0.9 and p=200 & r=0.1 . also, the novel spcr-scad was ranked best on two occasions namely p=70 & r=0.1 and p=100 & r=0.9. as well as our novel spcr-enet was ranked best when p=200 & r=0.9. considering small sample size of 50 at 30% sparsity table 5 and figure 4 reveal lasso estimator returned with least mse and 1st ranking on six occasions namely p=70 & r=0.1 (mse=0.001752), p=70 & r=0.5 (mse=0.04827), p=100 & r=0.1 (mse=0.00135), p=100 & r=0.5 (mse=0.00376), p=200 & r=0.1 (mse=0.03429) and p=200 & r=0.5 (mse=0.00376). additionally, table 5 and figure 4 depict our novel spcr-scad estimator returned with least mse and 1st ranking on two occasions namely p=70 & r=0.9 (mse=0.23878) and p=100 & r=0.9 (mse=0.01216). also, according to table 5 and figure 4 our novel spcr-enet returned with least mse and ranked 1st when =200 & r=0.9 (mse=0.00523). moreover, table 6 presents the assessment results (i.e. mses) of each ridge, lasso, elastic-net, scad and the novel two-step sparse learning regression models under problem of high dimensionality and multicollinearity at pa ge 13 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 127-140, 2025 higher small sample sizes (i.e. 70 and 100). according to table 6 and figure 5 when considering sample size 70 with 10% sparsity, lasso estimator returned with least mse and ranked 1st on three occasions namely p=100 & r=0.1 (mse=0.01239), p=150 & r=0.1 (mse=0.000253) and p=150 & r=0.5 (mse=0.00860). also, our novel spcrenet returned with the least mse and ranked best (1st) on two occasions namely, p=150 & r=0.9 (mse=0.01112) and p=200 & r=0.9 (mse=0.01801). similarly, our novel spcr-scad returned with the least mse and ranked best (1st) on two occasions namely, p=200 & r=0.1 (mse=0.0000029) and p=200 & r=0.5 (mse=0.00497). table 6 and figure 5 reveal our novel spcr-lasso with the least mse and 1st ranking when p=100 & r=0.1 (mse=0.04099). table 6: mse of ridge, lasso, elastic-net, scad and the novel two-step sparse learning regression models under problem of high dimensionality and multicollinearity at higher small sample size n sparsity 10 p 100 150 200 r 0.1 0.5 0.9 0.1 0.5 0.9 0.1 0.5 0.9 70 ridge 16.2612 12.23781 4.03865 11.26075 11.60848 3.39042 8.64446 9.76361 2.67325 lasso 0.01239 0.06057 0.18933 0.000253 0.00860 0.08186 0.00108 0.00857 0.11078 enet 0.04216 0.04236 0.30014 0.008587 0.02651 0.16821 0.00337 0.01337 0.10399 scad 0.91239 0.42240 1.54271 0.061469 0.09442 0.70653 0.05592 0.04387 0.37114 spcr-ridge 7.81776 2.56198 0.65102 20.5621 0.10744 0.48016 0.17769 0.77086 0.37926 spcr-lasso 0.25674 0.26353 0.04099 0.39296 0.05496 0.01196 0.01360 0.01134 0.05829 spcr-enet 0.52689 0.39883 0.05059 0.55724 0.12907 0.01112 0.02494 0.05916 0.01801 spcr-scad 0.19465 0.60541 0.14201 0.14136 0.05721 0.01919 0.0000029 0.00497 0.23454 sparsity 30 ridge 38.13883 37.66447 12.62623 24.65487 36.8325 11.61202 43.27625 40.24778 14.48422 lasso 0.00662 0.00338 0.37802 0.000527 0.00093 0.06559 0.00039 0.00097 0.03899 enet 0.01231 0.01996 0.58596 0.01063 0.00397 0.15603 0.00169 0.01392 0.10182 scad 0.54454 1.58249 7.48471 0.18944 0.75494 0.74484 0.38007 1.28358 1.70183 spcr-ridge 1.05483 1.59691 1.40662 1.38173 2.20109 0.63355 20.95064 41.6646 0.58520 spcr-lasso 0.01037 0.11502 0.10606 0.11955 0.08147 0.00544 0.37343 0.42448 0.00979 spcr-enet 0.10300 0.23069 0.04797 0.17829 0.14697 0.01071 1.25418 0.87494 0.01865 spcr-scad 0.01718 0.06238 0.12671 0.15924 0.14211 0.06971 0.52191 0.50069 0.02307 sparsity 10 p 120 150 200 10 0 ridge 7.76100 12.6273 2.51623 15.30857 12.1868 3.17887 12.09962 12.77974 2.54699 lasso 0.03929 0.09684 0.50299 0.01276 0.00601 0.20818 0.00835 0.00496 0.08117 enet 0.04664 0.10807 0.51229 0.03871 0.02988 0.32757 0.01998 0.01209 0.13614 scad 0.09555 0.26393 0.97131 0.28428 0.18236 0.61504 0.07291 0.19182 0.60169 spcr-ridge 0.60385 2.70444 0.80414 1.10457 0.45551 0.67572 3.83138 0.55069 0.42212 spcr-lasso 0.10859 0.32131 0.04616 0.04605 0.11171 0.10353 0.27173 0.03944 0.00243 spcr-enet 0.20375 0.86351 0.15666 0.05838 0.08879 0.24179 0.74755 0.04253 0.14797 spcr-scad 0.11849 0.47327 0.37146 0.03035 0.23513 0.42612 0.29195 0.00853 0.00307 sparsity 30 ridge 44.73326 43.83492 8.42825 25.9745 36.23507 9.66694 40.51713 26.12172 10.96855 lasso 0.00553 0.02191 0.25439 0.00293 0.00363 0.16858 0.00104 0.00111 0.12742 enet 0.01770 0.05789 0.38731 0.00866 0.01271 0.30531 0.00383 0.00347 0.26067 scad 0.42485 1.01778 1.44601 0.76685 0.50686 2.20988 0.28645 0.66346 0.88463 spcr-ridge 4.84146 1.15390 0.93489 1.80877 3.69042 0.79279 9.05412 3.75005 0.97242 spcr-lasso 0.09466 0.10141 0.07998 0.09151 0.19978 0.05461 1.69562 0.75744 0.05299 spcr-enet 0.63696 0.06535 0.16816 0.18488 0.50735 0.06652 4.53791 0.79444 0.06279 spcr-scad 0.50354 0.32955 0.15189 0.20898 0.28904 0.06717 0.00176 0.46458 0.06440 source: researchers’ compilations from r-outputs pa ge 13 7 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 127-140, 2025 figure 6: performance ranks of each estimator under varied levels of high dimensionality and multicollinearity for sample size 70 with 30% sparse figure 5: performance ranks of each estimator under varied levels of high dimensionality and multicollinearity for sample size 70 with 10% sparse in addition, considering sample size 70 with 30% sparsity, table 6 and figure 6 reveal lasso estimator with lowest mse and ranked 1st on six occasions namely p=100 & r=0.1 (mse=0.00662), p=100 & r=0.5 (mse=0.00338),p=150 & r=0.1 (mse=0.000527), p=150 & r=0.5 (mse=0.00093), p=200 & r=0.1 (mse=0.00039), and p=200 & r=0.5 (mse=0.00097). the table and figure further depict our novel spcrlasso estimator with the lowest mse and best ranking estimator when p=150 & r=0.9 (mse=0.00544), and p=200 & r=0.9 (mse=0.00979) as well as spcr-enet when p=100 & r=0.9 (mse=0.04797). figure 7: performance ranks of each estimator under varied levels of high dimensionality and multicollinearity for sample size 100 with 10% sparse pa ge 13 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 127-140, 2025 figure 8: performance ranks of each estimator under varied levels of high dimensionality and multicollinearity for sample size 100 with 30% sparse furthermore, considering sample size 100 with 10% sparsity, table 6 and figure 7 reveal lasso estimator with lowest mse and ranked 1st on six occasions namely p=120 & r=0.1 (mse=0.00662), p=120 & r=0.5 (mse=0.09684),p=150 & r=0.1 (mse=0.01276), p=150 & r=0.5 (mse=0.00601), p=200 & r=0.1 (mse=0.00835), and p=200 & r=0.5 (mse=0.00496). the table and figure establish our novel spcr-lasso with the least mse and best ranking estimator when p=120 & r=0.9 (mse=0.07998), p=150 & r=0.9 (mse=0.10353) and p=200 & r=0.9 (mse=0.00243). similarly, considering sample size 100 with 30% sparsity, table 6 and figure 8 reveal lasso estimator with lowest mse and ranked 1st on six occasions namely p=120 & r=0.1 (mse=0.00553), p=120 & r=0.5 (mse=0.02191),p=150 & r=0.1 (mse=0.00293), p=150 & r=0.5 (mse=0.00363), p=200 & r=0.1 (mse=0.00104), and p=200 & r=0.5 (mse=0.00111). the table and figure establish our novel spcr-lasso with the least mse and best ranking estimator when p=120 & r=0.9 (mse=0.04616), p=150 & r=0.9 (mse=0.05461) and p=200 & r=0.9 (mse=0.05299). findings summary, discussion of findings, and conclusion findings by small sample sizes and dimensionality at extremely small sample sizes (n=30), spcr-lasso consistently outperformed all other estimators, especially when dimensionality was high (p=7 or p=200). this demonstrates the strength of spcr-lasso in smallsample, high-dimensional contexts, where traditional lasso, ridge, or elastic net tend to become unstable. at moderately small sample sizes (n=50), results showed variation across conditions: spcr-scad excelled in contexts of low sparsity and low correlation. spcr-lasso and spcr-enet provided superior performance under higher correlation and dimensionality. as sample sizes increased further (n≥70), spcr-lasso and spcr-enet emerged as the most consistent and robust estimators across both moderate and high correlations. notably, spcr-enet showed particular strength in very highdimensional scenarios (p=200), reflecting its ability to balance shrinkage and group variable selection. findings by multicollinearity and sparsity the findings also highlight clear interactions between predictor correlation and sparsity: • under low correlation (r=0.1), traditional lasso sometimes matched or exceeded spcr-based methods in low-dimensional settings, suggesting spcr hybridization may not always be necessary in weakly collinear designs. • under moderate (r=0.5) or high correlation (r=0.9), spcr-lasso and spcr-enet decisively outperformed alternatives, confirming the necessity of the spcr step for mitigating multicollinearity. • with respect to sparsity, spcr-scad performed best in highly sparse, low-correlation conditions, while spcr-lasso and spcr-enet proved more adaptable across both sparse and dense regimes. table 7: overview of best estimators under different considered small sample sizes, high-dimensionality, multicollinearity and sparsity levels n r 0.1 0.5 0.9 0.1 0.5 0.9 0.1 0.5 0.9 p 50 70 200 30 10% lasso lasso spcrlasso spcrlasso spcrlasso spcrlasso spcrlasso lasso spcrlasso 30% lasso lasso spcrlasso spcrlasso spcrlasso spcrlasso spcrlasso enet lasso pa ge 13 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 127-140, 2025 70 100 200 50 10% spcrscad lasso spcrlasso lasso lasso spcrscad spcrlasso lasso spcrenet 30% lasso lasso spcrscad lasso lasso spcrscad lasso lasso spcrenet 100 150 200 70 10% lasso enet spcrlasso lasso lasso spcrenet spcrscad spcrscad spcrenet 30% lasso lasso spcrenet lasso lasso spcrlasso lasso lasso spcrlasso 120 150 200 100 10% lasso lasso spcrlasso lasso lasso spcrlasso lasso lasso spcrlasso 30% lasso lasso spcrlasso lasso lasso spcrlasso lasso lasso spcrlasso source: researchers’ compilations discussion of findings theoretical and methodological insights the results validate the rationale for hybridizing spcr with regularization penalties. spcr effectively reduces dimensionality while preserving predictive features, and the addition of regularization stabilizes estimates in the presence of multicollinearity. together, this hybrid approach delivers stronger predictive accuracy and interpretability than either dimension reduction or regularization alone. the study also demonstrates that penalty choice must be data-dependent. specifically: • spcr-lasso and spcr-enet are best suited for highdimensional, correlated designs. • spcr-scad retains value under extreme sparsity with low correlation. • spcr-ridge, while stabilizing, offers limited benefits compared to its sparse counterparts. these findings align with empirical evidence in highdimensional statistics but extend prior work by systematically comparing multiple regularizers within an spcr framework across diverse simulation conditions. practical implications for applied research for applied researchers working in genomics, finance, climate science, and social sciences, the study’s findings provide clear practical guidance: • use spcr-lasso or spcr-enet when predictors are highly correlated or dimensionality is large. • employ spcr-scad in cases of extreme sparsity with weak predictor correlation. • expect interpretability benefits from spcr, as sparse principal components link outcomes to identifiable subsets of predictors rather than opaque linear combinations. this guidance equips researchers with a decision-making framework to select the most effective hybrid estimator given the structural characteristics of their data. policy and applied modeling implications the findings also have implications for applied modeling in policy-relevant domains. policymakers and analysts working with high-dimensional, multicollinear data (e.g., in economic forecasting, climate modeling, or epidemiological surveillance) can adopt spcr-based methods to achieve more reliable predictions. by improving both accuracy and interpretability, these methods enhance the credibility of evidence-based policy decisions. implications for future research the findings suggest several avenues for further inquiry: i. extending the hybrid spcr framework to nonlinear models (e.g., kernel methods, deep learning). ii. applying spcr-regularization pipelines to realworld datasets in genomics, finance, and environmental science to validate simulation results. iii. investigating stability selection and uncertainty quantification after spcr to improve robustness of variable selection in practice. iv. exploring time-series extensions of spcr hybridization for forecasting applications. conclusion this section has discussed the findings of the simulation study and their implications for statistical methodology, applied practice, and policy. the results confirm that hybrid spcr estimators substantially outperform traditional penalization methods in small-sample, high-dimensional, and multicollinear conditions. among these, spcrlasso and spcr-enet emerge as the most versatile and reliable, while spcr-scad shows targeted advantages in sparse, low-correlation settings. collectively, the findings highlight the significance and necessity of hybrid spcr approaches as a methodological advancement for highdimensional data analysis. references ali, h., shahzad, m., sarfraz, s., sewell, k. b., alqalyoobi, s., & mohan, b. p. 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(2024). the impact of regularization on linear regression based model. journal of artificial intelligence and computer science, 1(1). priya, a. k., gnanasekaran, l., rajendran, s., qin, j., & vasseghian, y. (2022). occurrences and removal of pharmaceutical and personal care products from aquatic systems using advanced treatment-a review. environmental research, 204, 112298. song, j., xu, l., & wang, x. (2024, july). a regularization method for enhancing the robustness of regression networks. in 2024 43rd chinese control conference (ccc) (pp. 8524-8529). ieee. tibshirani, r. (1996). regression shrinkage and selection via the lasso. jrss-b. witten, d. m., tibshirani, r., & hastie, t. (2009). a penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. biostatistics. zhang, x., sun, q., & kong, d. (2024). supervised principal component regression for functional responses with high dimensional predictors. journal of computational and graphical statistics, 33(1), 242-249. zou, h., & hastie, t. (2005). regularization and variable selection via the elastic net. jrss-b. zou, h., hastie, t., & tibshirani, r. (2006). sparse principal component analysis. journal of computational and graphical statistics. pa ge 1 pa ge 94 american journal of applied statistics and economics (ajase) novel neural network state switching models for returns predicting with regime switching: a monte carlo’s simulation approach oluwasegun agbailu adejumo1*, omorogbe joseph asemota1, samuel olayemi olanrewaju1 volume 4 issue 1, year 2025 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v4i1.5514 https://journals.e-palli.com/home/index.php/ajase article information abstract received: june 10, 2025 accepted: july 17, 2025 published: september 05, 2025 this study proposes new neural network state switching models that will improve the regime predictive performance of nonlinear time series returns, in small sample size contexts. in this study, we developed four new neural network state switching models namely recurrent neural network state switching model (rnnssm), generalized regression neural network state switching model (grnnssm), radial basis function network state switching model (rbfssm) and multilayer perceptrons state switching model (mlpssm). the study presents comparative results of the estimations of the novel models alongside the traditional markov state switching model as well as the regime prediction performances of the models using the akaike information criterion (aic), log-likelihood and prediction accuracy measures under a monte carlo simulation study. evidence from the models’ estimation results particularly the aic and log-likelihood statistics established the novel multilayer perceptrons state switching model (mlpssm) as the parsimonious model for the simulated returns at small sample sizes. results from the models’ regime predictions evaluation, precisely the rmse and mae, evidently affirmed the novel mlpssm model as superior in its ability to predict or forecast market returns, particularly the bull and bear regimes, at small sample sizes. therefore, this study concludes that the novel mlpssm is the best-fitted model for market returns at small sample sizes with excellent ability of market returns’ regimes/states (i.e., bull and bear) prediction. this study recommends adoption of the novel multilayer perceptrons state switching model in modelling and regime predictions of time series returns. keywords markov process, mlp, neural network, regime switching, returns 1 department of statistics, faculty of science, university of abuja, abuja, nigeria * corresponding author’s e-mail: agbailuoa@gmail.com introduction forecasting returns is essential in financial modeling and decision-making. as a measure of volatility in time series, returns exemplify a stochastic process that indicates how much variables fluctuate over time. accurate return prediction is vital for assessing risks in key economic activities such as value at risk, asset pricing, and exchange rate management (liao et al., 2020; adejumo et al., 2020; roy & sarkar, 2024). in finance, returns exhibit three important features: clustering property (cont, 2007), asymmetry (nelson, 1992), and nonlinearity (maheu & mccurdy, 2002). researchers often encounter difficulties when modeling and interpreting modern time series data from diverse fields because traditional assumptions—such as linearity, normality, and stationarity—are frequently inadequate. the origins of returns modeling trace back to engle (1982), and bollerslev (1986), who introduced discrete-time garch and stochastic return processes for autoregressive conditional heteroskedasticity. however, as noted by hamilton (1989), nguyen et al. (2014), aliyu and wambai (2018), adejumo et al. (2020), xiuqin et al. (2023), al-sulaiman (2024) and others, standard garch and stochastic models cannot fully capture all key features of financial markets. this results in complex nonlinear dynamics and irregular regime shifts. a promising approach to address these challenges is switching state modeling. before switching state modeling was used in returns forecasting, the hidden markov model (hmm) was the primary approach. the hmm is a bivariate discrete-time process, denoted as {st,yt}(t≥0), where {st} is an underlying markov chain, and {yt} is a sequence of independent random variables. the conditional distribution of yt depends only on st. since st is hidden, only the stochastic process yt is observable. in other words, the process’s state is not directly visible, but the output, which depends on the state, is observable. therefore, all statistical inference must be based solely on yt, as st cannot be directly observed (rydén 2008). an hmm has a distinctive dependence structure, which is useful when analyzing financial time series. to better understand this dependence, it is illustrated here (figure 1) with a graphical model. figure 1: the hmm’s dependence structure according to figure 1, the distribution of a variable s(t+1) conditional on the history of the process s0,s1,…,st, is determined only by the value of the preceding variable st. this is all according to the markov property, where future events are completely independent of the past, pa ge 95 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 94-107, 2025 depending only on the present state. in addition, the distribution of yt conditionally on the past observations y0,y1,…,y(t-1) and the past values of the state, s0,s1,… ,st, is determined by st only (rydén 2008). putting this into mathematical terms, we get f(s(t+1)│st,s(t1),…,s1)=f(s(t+1)│st) and f(yt│s(t-1),s(t-2),…s1)=f(yt|st. cconsequently, markov state switching model (mssm) was introduced by hamilton to address the hidden states weakness of the hmm (hamilton, 2005). to describe the mssm, the number of states (or regimes) is assumed n, i.e. st ω={1,…,n}. this implies that; for instance the log returns of financial time series are drawn from n distinct normal distributions, depending on what state the hmm is currently in. this resolve to below models: y(t=1)=μ1+ϵt; where ϵt~(n, σ2 1) for state 1 y(t=2)=μ2+ϵt; where ϵt~(n,σ2 2) for state 2 ⋮ (1) y(t=n)=μn+ϵt; where ϵt~(n,σ2 n) for state n by implication, when the state of the hmm for time t is 1, then the expectation of the dependent variable is μ1 with variance of innovations σ2 1, similarly when the state of the hmm for time t is 2, then the expectation of the dependent variable is μ2 with variance of innovations σ2 2 and so on. since the underlying markov chain is hidden one cannot observe what state the hmm is in directly, but only deduce its operation through the observed behaviour of yt. in order to attain the probability law governing the observed data yt a probabilistic model of what causes the change from state st=i to state st=j is required. this can be specified using the transition probabilities of an n state hmm; ρ(i,j)=pr (st=j│s(t-1)=i) i,j∈ω={1,2,…,n}. the transition probability i.e. ρ(i,j)=pr (st=j│s(t-1)=i)=pr (st=j} s(t-1)=i,s(t-2)=k,…,s1=l) is dependent of the past only through the value of the most recent state. this is one of the central points of the structure of a markov regime switching model, i.e. the switching of the states of the underlying hmm is a stochastic process itself. the state-switching modelling approach has proven to be more reliable in this aspect in that it models all observed salient features of returns. the approach has documented the distinctiveness and forecasting capabilities of regime switching models against the commonly used garch models. thus, as a result its application has widely increased over time. furthermore, machine learning (ml) techniques also possess high capabilities in modelling all observed salient features of financial markets’ returns. ml methods have been used in many other fields for many years. for example, chen et al. (2019) exploit ml method in the estimation of stochastic discount factor and gu et al. (2020) showed superior performance of ml models for empirical asset pricing. also, is christensen et al. (2023) employed ml in returns forecasting of index stocks. similarly, nelson et al. (2017), shah et al. (2018), yao et al. (2018), and sunny et al. (2020) studied stock market returns based on the machine learning model. ml models especially the long-short term memory (lstm) and recurrent neural network (rnn) types have been famous for most time series data. the machine learning models are well-known for predicting the time series data without considering the much assumptions of the parameters. however, despite both approaches significant values to returns and regime forecasting, they both suffer from either a severe modelling misspecification or a lack of effective identification of meaningful stochastic regimes especially for small sample sizes. one way to handle these problems is the switching state space modelling approach. thus, there is a need for a well-designed modelling approach that allows the disturbance to be realistically represented, and at the same time does not lead to over frequently switching. given the criticality of such regime identification, not only for its economic implications but also for the profound comprehension of underlying phenomena, this study aims to propose new neural network state switching models that will improve the regime predictive performance of nonlinear time series returns, in small sample size contexts. the specific objectives include to: ⅰ develop novel neural network state switching models (using markov algorithms) for time series returns predicting in distinct regimes; ⅰⅰ examine the estimations of the novel neural network state switching models towards modelling time series market returns; ⅰⅰⅰ compare the predicting performances of the novel neural network state switching models using prediction accuracy measures under a monte carlo simulation study. literature review regime-switching model is an unusual case of a more general framework called hidden markov model (zucchini & macdonald, 2009). regime-switching models were early presented to econometric literature by hamilton (1989) and have become very prevalent particularly in applied works. applications of regime switching models range over a broad range of research areas, such as modeling shifts in inflation, exchange rates and interest rates (piger 2013), altug and bildirici (2010), changes in government policy (valente, 2003; owyang & ramey, 2004; sims & zha 2006) and shifts in exchange rate (bekaert & hodrick, 1992; bollen et al., 2000). the regime-switching modelling approach provides a completely new approach to the modeling of financial returns which it conceives as a multiplicative, hierarchically structured process (frommel et al., 2005). over the years, there have been several extensions to the state switching modelling by introducing nonlinear structures such include chow and zhang (2013), johnson et al. (2024), dong et al. (2020), and farnoosh et al. (2021). the regime switching state space model was proposed by chow and zhang (2013), the model adopts a pre-specified nonlinear transition function. the johnson et al. (2024) model called svae, parameterizes the emission function by neural networks, while the transition function remains linear. the dong et al. (2020) switching non-linear dynamical systems (snlds) pa ge 96 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 94-107, 2025 extension model parameterizes both the emission and transition functions with nonlinear neural networks. the deep switching autoregressive factorization (dsarf) proposed by farnoosh et al. (2021), approximates highdimensional time series with a multiplication of latent factors and latent weights, where the latent weights are modeled by a nonlinear autoregressive model, switched by a markov chain of discrete latent variables. most of the above-mentioned studies assumed that the discrete latent variables only influence the transition of state zt. the discrete switching variables in the slds are assumed to be markov, i.e. dt depends on dt-1 only. the recurrent slds (rslds) proposed in linderman et al. (2017) and becker-ehmck et al. (2020) extends the open-loop markov dynamics and makes dt depending on the hidden state zt-1. in nassar et al. (2018), a tree structure prior is imposed on the switching variables of rslds, where the dynamics of the switching variables behave similarly in the same sub-trees. the deep rao-blackwellised particle filter proposed in kurle et al. (2020) also allow dt to depend on zt-1. the snlds model dong et al. (2020) extended the open-loop markov dynamics by making dt depends on last observations. such recurrent structures serve as a presence of disturbance to the switching dynamics. recently, mari and mari (2023) introduced a regimeswitching model to analyze how market prices change over time. their model features a mean-reverting diffusion process for the basic regime, while the alternate regime is driven by predictions from a deep neural network trained on market log-returns. to estimate the model using market data, they proposed a statistical method based on simulated moments. xiuqin et al. (2023) developed the deep switching state space model (dsssm), a new framework designed to tackle challenges in modeling, inference, and understanding stochastic processes. the dsssm aims to deliver accurate forecasts and detect hidden regimes that carry significant economic implications and deepen understanding of market dynamics. it employs discrete latent variables for regimes and continuous ones for random influences, combining an rnn with a nonlinear switching state space model to capture nonlinear dependencies and regime shifts driven by a markov process. xiuqin et al. demonstrated dsssm’s effectiveness through forecasting tests on various simulated and real datasets across sectors such as healthcare, economics, traffic, meteorology, and energy. later, antulov-fantulin et al. (2024) proposed a new method for regime detection using a deep learning architecture called the gated recurrent straight-through unit (grstu). their extensive simulations showed that the grstu outperformed traditional statistical jump models, especially in regime classification on smaller datasets, while performing comparably on larger datasets. from the above, it is clear that many studies have focused on modeling nonlinear time series to address issues like significant modeling misspecification or difficulty in identifying meaningful stochastic regimes. as a result, the field of deep learning, especially recurrent neural networks with gate structures such as the long-short term memory (lstm), gated recurrent unit (gru), transformers, and temporal convolution networks, has become the new standard for modeling complex nonlinear dependencies. however, the small size of real-world data samples and, more importantly, the stochastic nature of regime switching, make traditional deep learning methods computationally challenging. in other words, these methods require large sample sizes for reliable estimation, which is often unrealistic because many disciplines do not have large volumes of time series data. in light of these challenges, dong et al. (2020), farnoosh et al. (2021), and xiuqin et al. (2023) developed switching non-linear dynamical systems (snlds), deep switching autoregressive factorization (dsarf), and deep state switching model (dssm), respectively, to better address issues of misspecification and the difficulty in identifying meaningful stochastic regimes in nonlinear time series. nonetheless, computing the feasibility of these models, especially with small sample sizes, remains a challenge. therefore, this research aims to propose new neural network-based regime switching models that can improve regime prediction for nonlinear time series data, such as returns, particularly when working with small sample sizes. materials and methods the novel neural network state switching models to improve the regime prediction performance of famous state switching model described in equation (1) for nonlinear time series data, especially returns, in small sample size contexts, this study integrates deep neural networks these include; recurrent neural network (rnn), radial basis function network (rbf), generalized regression neural network (grnn) and multilayer perceptrons (mlp) with markov two-state switching modelling technique. the proposed neural network state switching modeling approach combines the strengths of deep neural networks (i.e. efficient in high complexity dataset) and markov two-state switching capabilities of ensuring both interpretability and predictability of two financial state of market returns (i.e. bull and bear states). the models are developed in two phases. 1st phase: generative network using rnn, grnn, rbf and mlp given time series dataset of yt, the generative network procedure for the dataset include the following: ⅰ define of training and testing dataset of yt i.e. by setting training dataset at time step tk, and testing dataset at time step t(t-k) ⅰⅰ at time step t_k, either rnn, grnn, rbf or mlp is used to process the input data (i.e. training dataset) such as ht~fh (yt ) where f_h is the function of rnn, grnn, rbf or mlp. pa ge 97 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 94-107, 2025 2nd phase: integration of generative network ht into the markov two-state switching model subsequently, the generative network ht is integrated in the markov two-state switching model alongside the defined training dataset to develop recurrent neural network state switching model (rnnssm), generalized regression neural network state switching model (grnnssm), radial basis function network state switching model (rbfssm) and multilayer perceptrons state switching model (mlpssm). this would give us the following model to work with: y(tk~ht)=gt=c(st)+β1(g(t-1)-c(st-1))+β2 (g(t-2)-c(st-2)) +ϵt (2) where c(st)=c0 s0t+c1 s1t+c2 s2t σ2 (st)=σ2 1 s1t+σ2 2 s2t ϵt~i.i.d(0,σ2 (st)) c(st) is the state dependent mean, σ2 (st) is state dependent variance and the coefficients are β1or β2; which could be different for different subsamples. the proposal will be to model the state st as the outcome of an unobserved two-state markov chain with st independent of ϵt for all t. the transitions of the st, are presumed to be ergodic and intricate first order markov-process. this means impacts of earlier observation(s) for the gt and state(s) is/are completely captured in the recent gt state(s) observations as represented in (3); ρij=prob(st=j⁄s(t-1) =i) ∀i, j=1, 2∑2 (i=1) ρij =1 (3) matrix p captures the probability of switching which is known as a transition matrix; (4) the second element of the numerator is simply the previously mentioned initial probability of the markov chain, i.e. pr (s1=i)=πi, and it will henceforth be denoted as such. one can at this point notice that α(i,1) is the normalized value of the product between the initial probability and the conditional probability function f(y1 |s1 =i), and can therefore be written as follows: where p11+p21=1, and p12+p22=1 the nearer the probability ρij is to one the longer it takes to shift to the next regime. consider the model given by equation (2), i.e. a markov regime-switching model with 2-regimes. the estimation will be performed using hamilton’s filter, where the main idea is to calculate each state’s filter probabilities by making inferences on each state’s unknown probabilities based on the available information. when the filter probabilities are obtained, we have the probabilities one needs for calculating the log likelihood of the model. subsequently, the estimation of the model filter probabilities are discussed. the model’s filter probabilities are calculated by utilizing the model’s iterative relations by means of recursion. this can be done using a combination of the relation between observations and hidden states, and the endogenous relation between hidden states. begin from the starting value in our recursion, i.e. with the probability of being in state i at time t = 1: now, assume that we know the filter probability at time t-1, namely α(i,t-1). following the same strategy as for t=1 leads to the following recursion: data source: simulation setups to demonstrate the main idea behind the developed neural network state switching models towards enhancement of the prediction level of nonlinear time series data such as market returns particularly in small sample sizes, we consider a two states (regimes) model demonstrating the bull and bear regimes of daily market returns. y(t=1)=μ1+ϵt; where ϵt~(n,σ2 1) for state 1 y(t=2)=μ2+ϵt; where ϵt~(n,σ2 2) for state 2 parameters settings μ1= 0.01; is the mean returns of bull regime, μ2= -0.02; is the mean returns of bear regime, σ1= 0.05; is the standard deviation of bull regime of the market returns, σ2= 0.1; is the standard deviation of bear regime of the market returns, matrix(c(0.9,0.1,0.2,0.8)= is the transition matrix for the aforementioned regimes, t = n is categories of small sample sizes sets at 30, 50, 70, and 100. number of replication r = 1000times. models’ estimation procedure and prediction performance evaluation prior to the estimation of proposed neural network state switching models (i.e. rnnssm, grnnssm, rbfssm and mlpssm) against the traditional markov state switching model (ssm), the simulated market returns (yt) are tested for nonlinear modelling suitability. nonlinear models are employed where the financial system suggests nonlinearity in the system (adejumo et al., 2020; mendy & widodo, 2018). we utilized the most widely used tests known as bds test by brock, dechert and scheinkman. also, the simulated returns were tested for stationarity and presence of volatility using augmented dickeyfuller (adf) test and arch test respectively. also, this study utilized the frequently used model selection principle akaike information criterion (aic). aic = t ln(residual sum of squares) + 2n, where t is pa ge 98 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 94-107, 2025 the numeral of operational observations, while n is the number of parameters to be evaluated. the loglikelihood of the fitted model was also utilized. hence, the outperform model is one with the smallest aic value and highest log-likelihood. frequent error measures are available for model prediction evaluation; we evaluate the prediction ability of the proposed models against the markov state switching model by means of several performance measures such as root mean square error (rmse) and mean absolute error (mae). results and discussion simulated data presentation and preliminary assessment this section presents and discusses the summary statistics and some time series features of simulated market returns data. figure 2 and fig 3 reveal the time series trend plots and the regimes plots of the simulated daily market returns. explicitly, fig 2 depicts the time series and regimes plots of simulated daily market returns of sample sizes 30 and 50 while fig 3 shows the time series and regimes plots of simulated daily market returns of sample sizes 70 and 100. as observed, all the sample sizes’ time series trend plots depict relative stationarity i.e. constant means, variances and autocorrelation however relative to some external factors or trend. also, the time series plots depict clustering feature within the plots indicating presence of volatility. where at is the actual value in time t, and ft is the prediction value in time t. the models’ performances would be assessed using the earlier described monte carlos simulation study under four categories of small sample sizes. figure 2: time series plots of simulated daily market returns of sample sizes 30 and 50 figure 3: time series plots of simulated daily market returns of sample sizes 70 and 100 pa ge 99 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 94-107, 2025 furthermore, table 1 depicts the summary statistics, stationarity test, non-linearity test and volatility tests of the simulated market returns data over four (4) categories of small sample sizes. according to the table, the simulated market returns returned average returns of -0.0085±0.06, 0.0092±0.06, 0.0047±0.077 and 0.0153±0.06 for sample size 30, 50, 70 and 100 respectively. based on the aforesaid, it can be observed that the simulated data depicts a relative uniform variation (i.e. standard deviation at around 0.06, which is close to induced 0.05) of the simulated returns across the sample sizes. also, consequent to the observed features in the time series plots, the simulated data were tested for stationarity, non-linearity and presence of volatility. according to the adf test results, all the returns series as expected are stationary at level. as observed in the bds test results, the p-values depict significant at 0.05 level suggesting the non-acceptance of linearity assumption for the simulated returns series across all the sample sizes. similarly, the arch test p-values results reveal significant at 0.05 level suggesting the non-acceptance of assumption of no presence of volatility in the simulated returns series across all the sample sizes. based on the foregoing, is quite evident that the simulated returns at all the sample sizes significantly exhibit non-linearity and volatility features. hence, the simulated returns data necessitate non-linear and volatility models such as the state switching model and novel neural network state switching models. table 1: summary statistics and preliminary tests of the simulated data 30 50 70 100 summary statistics mean -0.0085 0.0092 0.0047 0.0153 median -0.0150 0.0053 0.0063 0.0174 min. -0.1203 -0.1470 -0.1935 -0.1567 max. 0.1387 0.1349 0.2717 0.1699 std. dev. 0.0604 0.06199 0.0772 0.0618 preliminary tests adf test i(0) 0.00* i(0) 0.00* i(0) 0.00* i(0) 0.00* bds test 0.0302* 0.0310* 0.0386* 0.0309* arch test 0.0068* 0.0272* 3.21e-06* 4.70e-06* note: * denotes significant at 0.05 level source: researchers’ compilations from r-output models estimations this section presents and discusses the estimations of the novel models and the traditional ssm. the models were fitted for training dataset that consisted of 80% of the simulated returns at different sample sizes. the diagnosis of the goodness of fit of the estimated models for the return series depicts, the q–statistics p-values greater than 0.05, indicating that there is no statistically significant trace of dependency or autocorrelation left in the squared standardized residuals, indicating that all the estimated models are adequately specified. table 2 present the summary of models’ estimations for the simulated market returns at a sample size of 30. according to table 2, among the estimated models the novel mlpssm and rbfssm returned with the least aic of -314.6393 and -115.7039, respectively as well as higher log-likelihood of 161.3196 and 61.8519 respectively. thus, mlpssm returns to be the most parsimonious model among the estimated ssms. table 2: summary of models estimations at 30 sample size ssm rnnssm rbfssm grnnssm mlpssm regime 1 intercept 0.0461* -0.4385 589.59* 0.0087 -1437.70 training_network rnn= 0.7062* rbf=-1.18e+07* grnn=1.1443 mlp=1443.53* regime 2 intercept -0.0626* 0.2849 -922.40* 0.0754 -1443.05 training_network rnn=-0.4932 rbf=1.84e+7* grnn=0.6202 mlp=1448.90* transition prob. state 1 state 2 0.3484 0.6578 0.3461 0.3138 0.3423 0.6648 0.000 0.7007 0.9468 0.2294 state 2 state 1 0.6516 0.3422 0.6539 0.6862 0.6577 0.3352 1.000 0.2992 0.0532 0.7705 aic -64.31498 -59.7527 -115.7039 -56.3426 -314.6393 loglik 34.1575 33.8764 61.8519 32.1713 161.3196 note: * denotes significant at 0.05 level source: researchers’ compilations from r-output pa ge 10 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 94-107, 2025 in addition, table 2 depicts the transition probabilities for the two identified states (namely bull and bear). the transition probabilities for mlpssm model show that there is a high probability that the market returns’ system remains in the same state hence implying limited switches in the state or regime. the results also indicate that the mlpssm has an 95% probability of staying in the bull state and a 5% probability of switching to the bear state. when the system is in a bear state, it has a 77% probability of remaining in bear state and lower probability of 23% to switch to bull state. the transition probability results highlighted shows that only extreme/ great events can switch the returns between states i.e. state 1(bull phase) to state 2 (bear phase), (figure 4). it further indicates that not any of the state is lasting since all transition probabilities are below one. figure 4: models’ transition probabilities of returns at sample size 30 furthermore, table 3 depicts the summary of models’ estimations for the simulated market returns at sample size of 50. the table reveals mlpssm and rbfssm returned with the least aic values of -543.7162 and -190.5718, respectively as well as higher log-likelihood values of 275.8581 and 99.2859 respectively. similarly, the results returned mlpssm with the lowest aic and highest loglikelihood values as the most parsimonious model among the estimated ssms at sample size 50. additionally, table 3 illustrates the transition probabilities between the two identified states: bull and bear. the transition probabilities for the mlpssm model suggest a low likelihood of the market return system remaining stable, indicating frequent transitions between bull and bear states. specifically, the results show that the mlpssm has a 32% chance of remaining in the bull state and a 68% chance of transitioning to the bear state. in a bear state, there is a 38% probability of staying in that state, while the chance of switching to the bull state is lower at 62%. the highlighted transition probability results indicate that minor events can trigger changes in returns between states, such as from state 1 (bull phase) to state 2 (bear phase) or the other way around (see figure 5). this suggests that neither state is permanent since all transition probabilities are less than one. pa ge 10 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 94-107, 2025 table 3: summary of models estimations at 50 sample size ssm rnnssm rbfssm grnnssm mlpssm regime 1 intercept 0.0032 0.0851 -505.86* 0.0342 -4218.13* training_network rnn= -0.1698 rbf= 2.02e+07* grnn=-0.1689 mlp=4231.02* regime 2 intercept -0.0036 -0.3103 -555.89* 0.0295 -4167.55* training_network rnn=0.6503 rbf= 2.22e+07* grnn=-1.4262 mlp=-4180.3* transition prob. state 1 state 2 0.7489 0.2135 0.5193 0.9275 0.7376 0.7779 0.522 0.9997 0.3202 0.3813 state 2 state 1 0.2511 0.7865 0.4807 0.0725 0.2624 0.2221 0.478 0.0003 0.6798 0.6187 aic -110.943 -108.51 -190.5718 -98.6980 -543.7162 loglik 57.4715 58.2549 99.2859 53.3490 275.8581 note: * denotes significant at 0.05 level source: researchers’ compilations from r-output figure 5: models’ transition probabilities of returns at sample size 50 moreover, table 4 shows the summary of models’ estimations for the simulated market returns at sample size of 70. the table reveals mlpssm and rbfssm returned with the least aic values of -659.263 and -320.2873 respectively as well as higher log-likelihood values of 333.6315 and 164.1436 respectively. similarly, the results returned mlpssm with lowest aic and highest log-likelihood as the most parsimonious model among the estimated ssms at sample size 70. subsequently, table 4 depicts the transition probabilities for the two identified states (namely bull and bear). the transition probabilities for mlpssm model show that there is a high probability that the market returns’ system remains in the same state hence implying low or limited switches in the bull state and bear state. explicitly, the results indicate that the mlpssm has an 86% probability of staying in the bull state and a 14% probability of switching to the bear state. when the system is in a bear pa ge 10 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 94-107, 2025 state, it has a 65% probability of remaining in bear state and lower probability of 35% to switch to bull state. the transition probability results highlighted shows that only extreme/great events can switch the returns between states i.e. state 1(bull phase) to state 2 (bear phase) or vice versa, (see figure 6). these results therefore, indicate that no any of the state is lasting since all transition probabilities are below one. table 4: summary of models estimations at 70 sample size ssm rnnssm rbfssm grnnssm mlpssm regime 1 intercept 0.0330* 0.0509 4.32+02* 0.1226* -12577.37* training_network rnn= -0.0493 rbf= -1.7e+07* grnn=-6.6674 mlp=12597.9 regime 2 intercept -0.1010* 0.0469* 3.36e+02* 0.0117 -12094.81 training_network rnn=-0.3404* rbf= -1.34e+07* grnn=-4.0712 mlp=12114.5 transition prob. state 1 state 2 0.8561 0.6539 0.9024 0.7165 0.5097 0.1724 0.0079 0.5004 0.8610 0.6472 state 2 state 1 0.1439 0.3461 0.0976 0.2835 0.4903 0.8276 0.9921 0.4996 0.1390 0.3528 aic -140.2999 -153.0316 -320.2873 -133.6245 -659.263 loglik 72.1499 80.51579 164.1436 70.8122 333.6315 note: * denotes significant at 0.05 level source: researchers’ compilations from r-output figure 6: models’ transition probabilities of returns at sample size 70 pa ge 10 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 94-107, 2025 to conclude, table 5 presents the summary of models’ estimations for the simulated market returns at sample size of 100. table 5 depicts mlpssm and rbfssm returned with the least aic values of -594.7593 and -387.7917 respectively as well as higher log-likelihood values of 301.3796 and 197.8958 respectively. similarly, the results returned mlpssm with lowest aic and highest log-likelihood as the most parsimonious model among the estimated ssms at sample size 100. table 5: summary of models estimations at 100 sample size ssm rnnssm rbfssm grnnssm mlpssm regime 1 intercept 0.0395* -0.1989 6.13+02* -0.2097* -114443.95* training_network rnn= 0.6103* rbf= -2.5e+07* grnn=3.6865* mlp=114617.88 regime 2 intercept -0.0100 0.1085* -8.82e+02* 0.0395 -94142.78 training_network rnn=-0.2464 rbf= 3.53e+07* grnn=-0.1143 mlp=9428.86 transition prob. state 1 state 2 0.5108 0.7283 0.3991 0.3916 0.6214 0.7046 1.4e-07 0.1696 0.5705 0.5839 state 2 state 1 0.4892 0.2717 0.6009 0.6084 03786 0.2954 0.9999 0.8304 0.4295 0.4160 aic -223.3181 -226.2772 -387.7917 -218.4201 -594.7593 loglik 113.659 117.1386 197.8958 113.2101 301.3796 note: * denotes significant at 0.05 level source: researchers’ compilations from r-output figure 7: models’ transition probabilities of returns at sample size 100 subsequently table 5 depicts the transition probabilities for the two identified states (namely bull and bear). the transition probabilities for mlpssm model show that there is a fair probability that the market returns’ system remains in the same state hence implying almost equal chances of state switching. explicitly, the results indicate that the mlpssm has an 57% probability of staying in the bull state and a 43% probability of switching to the bear state. when the system is in a bear state, it has a 58% probability of remaining in bear state and lower probability of 42% to switch to bull state. the transition probability results highlighted shows that either weak or extreme/ great events can switch the returns between states i.e. state 1(bull phase) to state 2 (bear phase) or vice versa, (figure 7). these results also, indicate that no any of the state is lasting since all transition probabilities are less than one. pa ge 10 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 94-107, 2025 consequently, figures 8 and 9 present line plots for each of the models’ aic and log-likelihood estimates, respectively. as observed from the figures, two out of four introduced novel models i.e. rbfssm and mlpssm outperformed the traditional ssm and the rest introduced models by returning with lower aic and higher loglikelihood estimates across all four small sample sizes examined. furthermore, according to figure 8, the novel mlpssm returned the least aic estimates across all four small sample sizes examined. similarly, as observed in figure 9, the novel mlpssm returned the highest loglikelihood estimates across all four small sample sizes examined. also, it is important to note that the aic estimate inclines to increase and log-likelihood estimate tends to decline when the sample size is 100. this result suggests that the novel mlpssm is best fitted for sample sizes less than 100. thus, the novel mlpssm returned as the most parsimonious model among the estimated ssms. evidence from the aic and log-likelihood line plots established multilayer perceptrons state switching model (mlpssm) as the best-fitted model for the simulated returns at all the considered small sample sizes. evaluation of models’ regime prediction performances this section presents and discusses the performance of the fitted models for the training dataset. each of the model performances was assessed based on insample predictions of the testing dataset at 20% of each sample size, i.e., 30, 50, 70, and 100. root mean square error (rmse) and mean absolute error (mae) were employed to assess the prediction accuracy errors of the models. figure 10 depicts the models’ prediction performances results. according to the figure, the novel mlpssm returned the least rmse and mae for the insample predictions across all four sample sizes examined. therefore, our introduced novel model, i.e., mlpssm, returned as the optimal model for regime predictions of the stimulated returns. figure 8: models’ aic estimates across the sample sizes figure 9: models’ loglikelihood estimates across the sample sizes figure 10: in-sample prediction performances of the fitted models pa ge 10 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 94-107, 2025 furthermore, table 6 presents the mlpssm in-sample daily regime mean predictions of 20% of the simulated market returns sample sizes. the table reveals a stable or static regime means across all the sample sizes considered. explicitly, for sample sizes 30 and 50, the novel mlpssm predicted respectively a bear regime mean of -0.00746 and -0.00068 across the days predicted at each scenario. the predicted bear regime in sample size 30 and 50 returned high transition probability (i.e. 0.77 and 0.62) to bull regime. this implies the predicted bear regimes in sample size 30 and 50 has little probability to remain in the state, i.e., weak events can switch the bear regime to bull regime. on the other hand, for sample sizes 70 and 100, the novel mlpssm predicted respectively a bull regime mean of +0.007425 and +0.01948 across the days predicted at each scenario. the predicted bull regime in sample size 70 returned a very low transition probability (i.e. 0.14) to bear regime while the predicted bull regime in sample size 100 returned a relatively moderate transition probability (i.e. 0.43) to bear regime. this implies the predicted bull regimes in sample size 70 has a great probability to remain in the state, i.e., only extreme events can switch the bull regime to bear regime. meanwhile, for sample size 100, the result implies that the predicted bull regimes has a relatively fair or moderate probability of remaining in the bull state, i.e. not too weak or too extreme events can switch the bull regime to bear regime. table 6: mlpssm 20% in-sample daily regime mean predictions of small sample sizes considered 30 p_12=0.23,p_22=0.77 50 p_12=0.38,p_22=0.62 70 p_11=0.86,p_21=0.14 100 p_11=0.57,p_21=0.43 1 -0.00746 -0.00068 +0.007425 +0.01948 2 -0.00746 -0.00068 +0.007425 +0.01948 3 -0.00746 -0.00068 +0.007425 +0.01948 4 -0.00746 -0.00068 +0.007425 +0.01948 5 -0.00746 -0.00068 +0.007425 +0.01948 6 -0.00746 -0.00068 +0.007425 +0.01948 7 -0.00068 +0.007425 +0.01948 8 -0.00068 +0.007425 +0.01948 9 -0.00068 +0.007425 +0.01948 10 -0.00068 +0.007425 +0.01948 11 +0.007425 +0.01948 12 +0.007425 +0.01948 13 +0.007425 +0.01948 14 +0.007425 +0.01948 15 +0.01948 16 +0.01948 17 +0.01948 18 +0.01948 19 +0.01948 20 +0.01948 note: + and denote bull and bear regime respectively source: researchers’ compilations from r-output conclusions this paper centers on introducing novel neural network state switching models using the combination of strengths of deep learning neural networks and traditional markov state switching approaches for regime predictions of time series returns. the paper shows the comparative results of the estimations of the novel models as well as the regime prediction performances of the novel models using prediction accuracy measures under a monte carlo simulation study. evidence from the models’ estimation results particularly the aic and log-likelihood statistics established the novel multilayer perceptrons state switching model (mlpssm) as the best fitted model (against the traditional markov state switching models and other introduced neural network state switching models) for the simulated returns at small sample sizes especially for sample sizes lower than 100. moreover, results from the models’ regime predictions evaluation precisely the rmse and mae evidently affirmed the novel mlpssm model as superior in its ability to predict or forecast market returns particularly the bull and bear regimes at small sample sizes. base on the aforementioned, this study therefore concludes that the novel mlpssm is best fitted model for market returns at small sample sizes with excellent ability of market returns’ regimes/states (i.e. bull and bear) prediction. pa ge 10 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 4(1) 94-107, 2025 in view of the aforementioned, this study recommends adoption of the novel multilayer perceptrons state switching model in modelling and regime predictions of time series returns. references adejumo, o. 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(2009). hidden markov models for time series: an introduction using r. chapman and hall/crc. pa ge 1 pa ge 16 7 american journal of applied statistics and economics (ajase) optimizing capital allocation and investment decisions in the u.s. economy through data analytics ismoth zerine1*, md mainul islam1, tauhedur rahman2, morium akter3, md rakibul haque pranto4 volume 3 issue 1, year 2024 issn: 2992-927x (online) doi: https://doi.org/10.54536/ajase.v3i1.5698 https://journals.e-palli.com/home/index.php/ajase article information abstract received: september 09, 2024 accepted: october 20, 2024 published: december 20, 2024 today, investment and capital location is a central mechanism of performance in the economic system, but conventional approaches tend to fall short in terms of managing resources, resulting in less than optimal conditions. in the interest of ever increasing availability of big data, advanced data analytics tools integration into capital allocation processes is underdeveloped. this research project fills the gap since it assesses how data analytics can be used to optimize the process of investment decision-making in the u.s. economy. to determine the effect of machine learning, predictive analytics and descriptive analytics tools on capital allocation performance, return on investment (roi), market share change, and financial growth were the primary objectives. there were 300 sampled organizations (both in the public and the private sectors) and the data were gathered by way of surveys, along with secondary financial reports. the data analytics use and the effectiveness of capital allocation were analyzed through pearson correlation, anova, multiple regressions, and t-tests, as statistical methods. they showed that application of machine learning and predictive analytics was highly linked with the increment in roi (mean = 19.2%, p < 0.01), the contribution to the growth of market share (mean = 4.8%, p < 0.01), and financial growth (mean = 12.6%, p < 0.01). moreover, the organizations applying these tools demonstrated better performance in comparison with the ones that did not incorporate data analytics, even emphasizing the impressive role of advanced analytics in achieving better financial performance. these results indicate that a data-driven solution of multiple parties can complement capital allocation and provide high value on economic decisions. the study adds to an emerging knowledge on data analytics applied to economic decisions and aids policymakers and businesses interested in enhancing a company investment strategy. keywords capital allocation, data analytics, economic growth, investment decisions, machine learning 1 college of graduate and professional studies, trine university, usa 2 dahlkemper school of business, gannon university, usa 3 information technology and project planning management, st. francis college, usa 4 department of management, st. francis college, usa * corresponding author’s e-mail: ismothmona7171@gmail.com introduction the distribution of capital and investment decisions is a very crucial process that directly affects the economic image of any country. they concern allocation of the financial resources to the different projects and issues and dictate the perspectives and evolution of the economies and their competitiveness and sustainability (sarkutė et al., 2024). such decisions are when it comes to cost-efficiency within such fields as hospital care, manufacturing, and services-and setting them right in the country that plays one of the leading roles in the global economy such as the united states has been a matter of fact never as time-sensitive (tallat et al., 2023; mekonnen, 2024). the macroeconomic size and structure of u.s. economy require operationalization of tools and methodologies advanced in order to realize efficient allocation of capital (goodwin et al., 2022). this is especially so in this era of big data when it is possible to find huge amounts of economic, financial and demographic data to help in making decisions. nevertheless, in spite of this type of data, there are still a lot of industries with outdated capital allocation practices that may prove to be less than optimal (ren, 2022). this paper which sets out to bridge that gap has set itself to research on the process of data analytics in the capital allocation process within the economy of the united states, providing a contemporary way of making investment decisions, which may result in a better economic outcome in terms of improved performance (challoumis, 2024). through advanced data-driven outlooks like machine learning, predictive analytics, and big data systems the research endeavour to investigate the future of such practices and how they can support the process of allocating capital both in the government and corporate sectors of the american economy (junaedi, 2024). background the complexities of global economy which are becoming increasingly demanding especially in the advance economies such as the united states have necessitated the reconsideration of classical methods of dealing with capital allocation and investment decisions (challoumis & eriotis, 2024) and subjectivity (wilenius, 2024; challoumis, 2024). although such approaches were efficient in the past, they are disadvantaged in relation to big data processing, pattern recognition and future economic performance prediction. conversely, data analytics has the prospects of overcoming such shortcomings combining the ability to process enormous amounts of data and identifying the patterns hidden by the rest (ikegwu et al., 2022). also known as data analytics, and comprising multiple industry-standard methods like descriptive, predictive, pa ge 16 8 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 167-177, 2024 and prescriptive analytics, data analytics has become prominent in many ways through many sectors (weber, 2023). ranging across financial services to health, industries across the globe have transformed with the capacity to utilize data as a means to make decisions. their potential in the optimization of capital deployment and investment choices are however not well explored especially in the u.s economy (olanrewaju et al., 2024). the gap is tilled in this study because it determines how to apply data analytics practically in capital investment context, which provides theoretical information as well as evidence on how the techniques can be leveraged to promote more efficient and effective capital investment in the u.s (udo et al., 2024). study dimension this study is a national and international study. the main target is the u.s. economy, it is believed that the findings and approaches to be suggested are to be applied to a wider variety of cases. the u.s. is a perfect place to consider since it has highly developed financial markets, substantial capital investment processes, and the availability of numerous sources of data (novak et al., 2022). moreover, the tools used can be extended to other economies cautioning that is already financialized or intends to finance its allocation capital structure. increasingly, various economies across the globe are turning towards data analytics when it comes to making better decisions. the data-driven decision-making elements have been implemented in all sectors in countries like china, germany, and the united kingdom, in terms of their public investments, financial markets, among others (awan et al., 2021). though these four countries have improved in capital allocation, so far, data analytics in the optimization of capital investments is still an evolving area. as such this study is relevant to any other economy since not only will it be contributing to the economy of a country, this study have some information that will result in making decisions based on data analytics by the other economies (abir et al., 2024). literature review there has been an increasing range of literature on the functions of data analytics in different sectors of the economy, and manufacturing. data analytics in financial decision-making have been documented and its effectiveness in enhancing risk management, forecasting, and portfolio optimization has been discussed in most studies (zouo & olamijuwon, 2024). as an example, there was a paper by pandya, (2024) that touched upon the use of machine learning models to forecast stock market trends exemplifying how these methods are more effective than traditional ones in predictive ability (rouf et al., 2021). likewise, williams et al. (2021) highlighted how predictive analytics should be used in managing financial institutions to optimise their investment portfolio, thus making it an efficient investment that would give better returns (owoade et al., 2024). nevertheless, in contrast to literature that has largely been fed by literature on particular sectors or industries, little has been done to survey the whole picture of application of data analytics in streamlining capital allocations at a macroeconomic level more so in national economies. even the study that addresses this issue tends to be narrow and pay attention to the field of the public sector or the allocation of investments in certain lines (infrastructure investment, etc.) (o’neill, 2019). the understanding of how the field of data analytics can potentially be applied to any range capital allocation decisions, both public and private investments with a systematic approach to the whole roadmap is missing to a certain extent (adriaens et al., 2021). significance of this study the study is very important in a number of ways. to begin with, the decision on capital allocations is an original part and parcel of the economic performance of any nation (beck et al., 2024). maximization of resources allocation is a crucial aspect that allows enhancing sustainable economic development, maximizing productivity, and providing fair distribution of resources. second, the global economy is becoming more complex and there is a lot of information that is accessible to decision-making that the traditional methods are no longer sufficient (trunk et al., 2020). the fact that this study has touched on data analytics as a method to optimize capital allocation and utilize it as a feature has already offered a fresh idea that can give a competitive advantage to the u.s economy both locally and internationally. in addition, the study results would be useful to the policymakers, financial institutions and companies by offering evidence-based work on how to optimize investment decisions. as an example, government agencies responsible of managing their nation resources may wish to employ such a strategy as data analytics to carry out priorities of infrastructure planning and project financing, and, the analysts could utilize the results to manage their portfolio and run income-generating risk analysis (lee, 2020). the data analytics feature introduced in the capital allocating procedures in the proposed research can revolutionize the economics of decisionmaking and bring about more effective and efficient investments (haidari, 2023). research gap data analytics has a large number of literature on the application of its methodology across industries, there is still a research gap on its application to the understanding of capital allocation in the u.s economy (ikegwu et al., 2022). majority of the inquiries so far conducted have dwelt on certain industry or investment activities and there is a gap in the investigation of how these methods can be utilized with regard to the larger context of national economic decisions. secondly, although a good number of the literature points to the high levels of promise associated with data analytics in enhancing decision pa ge 16 9 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 167-177, 2024 making, there has been little or no empirical studies to determine its effectiveness in optimizing national-based capital allocation processes (tan & saraniemi, 2023). this study address this gap by developing a detailed discussion on the integration of data analytics into the capital placing procedure with respect to the potential of the former in the optimization of the decision-making process and better economic output (erica et al., 2024). using sophisticated data-driven analytics, the paper aims to show how such tools can be further employed to evaluate huge amounts of data in order to state economic trends and guide investments, and as a result to achieve more effective allocation of capital within the american economy. research questions a combination of research questions that powered this research were constructed to direct the exploration into how data analytics can best be used to generate optimized capital allocations and investment choices in the economy of the united states of america. the essential ones are: 1. what are the ways of applying data analytics tools to the process of capital allocation in the u.s. economy? 2. what can be considered the particular value of using predictive analytics, machine learning, and big data solutions in investment decision-making? 3. what do these tools do to manage the inefficiency in existing capital allocation process? 4. what are the limitations and challenges of data analytics data when used in capital allocation and how can the challenges be addressed? the following questions significantly affected the structure of the research methodology used in the current study by dictating data sources and analysis methods, as well as the way the research will be conducted. objectives this study set the following main objectives considered the existing capital allocation practices in the u.s. economy and made identification of strong and weak points of conventional approaches. the study methodology was to include both qualitative and quantitative approach such as interviews with the authorities in the field and analysis of data by highly sophisticated statistical methods. tested the efficiency of data analytics tools to streamline capitalusing capital allocating criteria. to reach this objective, it has applied the principles of regression analysis and machine learning algorithm to historical data in terms of evaluating the role of data analytics in investment results. coming up with a model of data analytics support to the work of capital allocation. this was achieved through the synthesis of the findings of the foregoing objectives and the development of a cohesive plan of actionable implementation of data analytics as a means of capital allocation decision. materials and methods research problem and objectives the main research issue that the study attempted to answer was the inefficiency of the capital distribution and investing practices of the us economy and the use of data analytics to maximize the process. the purpose of the study was to examine the possibility of raising the quality of the process of making decisions based on data and resulting in more robust economic results. this study had the following objectives the study examined what is employed in the capital allocation and investment decision in the u.s economy. this was with an aim of determining the gaps and inefficiencies that were cardinal in its current practices especially with regards to the application of data analytics. other articles covered the absence of systematic strategies based on the involvement of large-scale data in investments. in order to measure the performance of data analytics approaches to optimize capital allocation decisions. this aim was concerned with evaluating different instruments of data analytics like machine learning algorithms and predictive models and their efficiency on investment performance. the analysis was supposed to offer an understanding of how decisionmaking based on data can improve the performance of the economy. in a bid to devise a protocol in the incorporation of data analytics in the decision making process in order to have better investment returns. this framework attempted to fill the gap in the published literature regarding the use of such technologies in the investments of both the public and private sectors like it has been advocated. research design type of study: the correlational study was used in the present study, since only relations were to be investigated between data analytics methods and the outcomes of capital allocation. the study was to determine the extent and the kind of the said associations without any control over the variables. design justification: a correlational design was chosen where it was possible to study the available information, and draw patterns and relationships between variables without a researcher interfering with experiments. it was the perfect design to answer these questions since the research sought to investigate the interrelation between facts analytics and investment decision-making processes. study parameters strategy of sampling population in this research work, the population was all the financial institutions, investment companies and government agencies that carryout capital allocation in the economy of the united states. these establishments produced a lot of data on the decision making in investments, market trends and economic performance which made them a suitable organization of analysis. sampling method it was decided to deploy the stratified random sampling pa ge 17 0 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 167-177, 2024 method thereby guaranteeing that variations in types of investment sectors (i.e. public sector, private sector, large corporations, small firms etc.) were represented appropriately in the sample. this guaranteed that the sample represented the different variety of investmentdecision oriented individuals within the u.s economy. sample size 300 organizations were used to carry out the study. the selection of this sample size was guided by the power of the analysis that sought to achieve a power of 80 percent in identifying any significant relationships between the use of data analytics and the performance of investments with a margin of error or 5 percent. this sample was comparable to those involved in similar research works of the same discipline (brown & anderson, 2020). inclusion/exclusion criteria the inclusion criteria demanded that the organizations shall have been actively involved in capital allocation or investment decisions over a period of at least three years and they shall also have the necessary financial data. companies that did not have a data analytics infrastructure were not eligible to the study, since the study was based on determining how data analytics can help in decision making. methods of collecting data instruments: the online survey with a mixture of closed and open questions was the main instrument of data collection. the survey was aimed at collecting information about the categories of used data analytics tools, decision-making processes, and the estimations of influence on the outcomes of investments. financial reports and organizational records were used as secondary data in the survey. procedure the survey was sent to the picked organizations and reminds were read to them to increase the attendance. the responses were gathered within three months which is enough time to have a good response rate. the considerations of ethics were also taken seriously and none of the activities of the collection of data affected the normal operations of the organizations. pilot testing: the reliability and validity of the survey was tested on a sample of 30 organizations in terms of pilot testing. the reasons behind the pilot study were the found insignificant problems in terms of question clarity and these were noted prior to the launching of the fullscale survey. measurement and variables operational definitions independent variable: nature and quality of data analytics in capital allocation. this was also a 5-point likert scale where the frequency and sophistication of data analytics tools (e.g., machine learning, predictive analytics) were measured. dependent variable the effectiveness of the capital allocation decisions that was determined through the key performance measures that included roi, financial growth, and market share expansion. measurement tools the survey contained questions that measured both the independent and dependent variables in addition to the secondary data extraction of the financial reports to carry out stronger analysis. reliability and validity it was evaluated with the help of the survey by means of expert reviews and pilot testing. high internal consistency of the scales used in the survey was established by the employment of cronbach alpha that was set to 0.85. program of data analysis laboratory techniques multiple methods were applied in data analysis including regression analysis to help in determining the effect of data analytics on the outcome of investment decisions. the descriptive statistics were also statistic computed to give an overview of the data and this was followed by correlation analysis to determine significant relationship between the variables. software spss version 28.0 and r studio software were used to analyze the data and graphics at complex levels in statistical calculation. these tools have been chosen on the basis of their high stability and their ability to operate large data sets. rationale multiple regression analysis was selected since it enabled the study of the relationship that existed between one dependent variable and a number of independent variables, which suited the study of this research very well. this methodology allowed determining the most powerful factors in the decision of capital allocation. limitations the only feasible bias of the study would have been a bias of self-reporting because the organizations were being asked to overreport their data analytics tools. the researchers confined the research to organizations based in the u.s and therefore, it may not be applicable to other economies that are characterized by different investment habits. these shortcomings might have clutched the meaning of the outcomes of the study, especially concerning the participation of the sample that was the most representative of the wide use of capital allocation. results and discussions the purpose of the study was to determine how data analytics has influenced streamlining capital distribution pa ge 17 1 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 167-177, 2024 and investment decision of the economy of the u.s. an evaluation of the relationship between the application of data analytics tools (machine learning, predictive analytics, and descriptive analytics), and key performance metrics (kpis) that included return on investment (roi), market share change, and financial growth, of 300 organizations spanning more than a hundred organizations in various sectors that included financial institutions, government agencies, and corporation was considered. the findings below provide elaborate information on the trends and patterns of the data that are observed and the role of data analytics in the enhancement of efficiency of capital allocation. descriptive statistics the descriptive statistics showed that there was a significant difference in the outcomes of capital allocation and the use of data analytic tools in the sample. the average roi 15.3, (sd = 5.6) of the sample, is showing a somewhat favorable gain of proceeds on investments among organizations. the average in market share change (sd = 2.5) was 4.8 that points out towards the fact that on average, the organization showed some increase in its market share. there was also positive growth in financial aspect with a mean of 12.6% (sd = 3.8). there was a significant disparity in the application of data analytics tools among other organizations. machine learning tools and predictive analytics were reported to be used more than descriptive analytics on average (mean = 3.4, sd = 1.1 and mean = 3.6, sd = 1.0 respectively compared with mean = 2.9, sd = 1.2 respectively). such distribution shows that though the advanced analytics tools were rather well integrated in an organizational practices, descriptive analytics was also a commonly used technique in capital allocation decision-making. the average profit margin also was 18.0 percent (sd = 5.2), and the organizations varied in sizes where the average was 1,200 employees (sd = 350) and this further contributed to the variety in the dataset. pearson corrrelation analysis they queried the relationships using pearson correlation matrix and gave key variables. the considerable and statistically significant correlations were established between the use of capital allocation tools and results of capital allocations. the use of machine learning demonstrated a strong positive association with roi ( r = 0.54, p < 0.01), change in market share (r = 0.50, p < 0.01) and financial growth (r = 0.52, p < 0.01). this is an indication that machine learning tools in the context of capital allocation decision making provided greater returns on investments, improved market share performance and improved financial growth. in a similar fashion, predictive analytics had a positive correlation with all three performance measures, which were roi (r = 0.60 p < 0.01), market share change (r = 0.58 p < 0.01), and financial growth (r = 0.63 p < 0.01). such results have been used to emphasize the significance of predictive modeling in predicting the level of economic performance and making optimal investment decisions. descriptive analytics on the other hand had moderations as related to the market share change (r = 0.47, p < 0.01) and financial growth (r = 0.52, p < 0.01) but the correlations with the roi (r = 0.34, p < 0.05) were less that of machine learning and predictive analytics. anova: effect of roi type of data analytics to answer whether the various kinds of data analytics tool were affecting the roi significantly, a one-way analysis of variance was done. the findings stated that there was a strong comparison of roi amongst the organizations utilizing various forms of data analytics (f(3, 296) = 12.35, p < 0.01). having performed posthoc tests, it was revealed that organizations, which used machine learning (mean roi = 19.2%, sd = 4.2) demonstrated much higher roi than organizations that did not use data analytics (mean roi = 7.2%, sd = 3.5). moreover, the roi of organizations using predictive analytics (mean roi = 16.4%, sd = 5.0) also indicated a significant increase in roi as opposed to non users. the intermediate-performing group was descriptive analysis user (mean roi = 14.8, sd = 4.7) and is better compared to non-users. these findings indicate that, by implementing advanced data analytics tools, especially, machine learning and predictive analytics, organizations obtained a better roi than those, which still use traditional decision-making techniques or not use data analytics at all. multiple linear regression the association between the application of data analytics tool and the outcome of capital allocation (roi, market share rise/decrease, and financial growth) was done through multiple linear regression. the regression model came out to be significant (f(5, 294) = 15.23, p < 0.01), which means that the predictors accounted to a large extent the variability in the capital allocation performance. the strongest prediction of roi involved machine learning usage (beta = 1.5, p < 0.01) and predictive analytics usage (beta = 0.9, p < 0.01), indicating that their utilization contributed to substantial augmentations in returns on investments in case of their usage by an organization. other significant factors influencing roi were profit margin (b = 0.7, p < 0.01) and organizational size (b = 0.03, p < 0.05), pointing out that the larger organizations with higher profits had a higher probability of being able to derive the benefits of the use of advanced data analytics tools. the r 2 value of this model was 0.46 which means that the variance of roi was explained by predictors as 46 per cent. the equivalent results were found in the case of market share change and financial growth where machine learning and predictive analytics played an important positive role as predictors of both indicators. this is another indication of the importance of advanced data analytics in contributing to high performance in diverse organizations with regard to their economic performance. pa ge 17 2 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 167-177, 2024 data analytics users vs non-users independent t -test the independent t-test was carried out by comparing the roi in the organization using data analytics tools and the organizations without utilizing data analytics tools. the findings indicated a huge disparity between the two groups (t(298) = 7.45, p < 0.01). a greater roi was experienced by those organizations that implement data analytics (mean = 18.1%, sd = 4.7), as opposed to nonusers (mean = 8.2%, sd = 3.2). this finding confirms the prediction that the implementation of data analytics tools has a positive effect on the decisions related to capital allocation and results in financial improvement. it also points to the eventual cost-effectiveness and long-term advantages of taking data-driven initiatives in decisionmaking regarding investments. regression analysis forecasting of financial growth a second regression analysis used to predict financial growth providing that the use of machine learning and predictive analytics together with other control names like profit margin and organization size are also examined. the regression equation was significant, f (5, 294) = 10.56, p < 0.01 and the predictors explained 44 percent of the variation in financial growth(r2 = 0.44). the significant positive predictors of financial growth were the use of machine learning (1.2, p < 0.01) and predictive analytics (0.8, p < 0.01), which indicated that these tools had a direct impact on the financial performance of organizations. two other drivers were profit margin ( 0.6, p < 0.01) and the size of an organization ( 0.05, p < 0.05), with the largest companies and those with higher profit margins being more financially inclined to grow. this once again strengthens the notion that data analytics tools have the potential to become potent tools contributing to economic prosperity. chi-square test of categorical data the relationship between organization type (public, private, large corporation, small firm) and using some data analytics tools was examined using chi-square test of independence. the findings revealed a significant relationship (c2 (6) = 14.52, p < 0.01) where the disposition of incorporating machine learning and predictive analytics leaned towards large corporations and companies of the private sector. with small firms, on the contrary, there was a higher chance of descriptive analytics or no analytics usage. this points to the importance of the size and field of study in the determination of the degree of implementation of data analytics tools in the capital allocation processes. key findings in summary • usage of data analytics: the findings indicated that machine learning/predictive analytics were highly correlated in terms of increase of roi, market share, and financial development. companies that have been using such tools performed better compared to companies that did not utilize data analytics. • statistical significance: there were significant relationships between the application of data analytics tools and the results of capital allocation. the consistently best predictors of a high financial performance were machine learning and predictive analytics. • sectoral variation: it was most likely to see a higher utilization of advanced analytics tools, which includes, machine learning, predictive analytics among large corporations and the private firms and focusing on smaller firms, a concentration was seen on using descriptive analytics or not using any analytics tools. this would imply that firm size and industry can also affect the degree to which data analytics are applied to the process of investment decisions. general conclusion the evidence substantiates the idea that the involvement of advanced data analytics in making capital allocation decisions can contribute instead to the improvement of organizational performance (in respect to roi, market share increase and financial development) to a significant extent. these outcomes point to how the use of data analytics in the u.s. economy has the potential to create significant positive change in terms of the effectiveness of capital allocation and investment decisions. this is made possible through application of advanced data-driven approaches that help an organization to streamline its decision making process thus leading to better financial performance and thus generation of better competitive advantage to organizations in domestic and foreign markets. table 1: descriptive statistics for key variables variable mean median standard deviation minimum maximum roi (%) 15.3 14.5 5.6 5.0 25.5 market share change (%) 4.8 4.2 2.5 1.0 10.5 financial growth (%) 12.6 12.2 3.8 3.6 18.0 machine learning usage (1–5 scale) 3.4 3.5 1.1 1.0 5.0 predictive analytics usage (1–5 scale) 3.6 4.0 1.0 1.0 5.0 profit margin (%) 18.0 17.0 5.2 8.0 25.0 organization size (employees) 1200 1000 350 50 20000 duration of investment (years) 8 7 3 3 15 pa ge 17 3 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 167-177, 2024 table 2: pearson correlation matrix variable roi (%) market share change (%) financial growth (%) ml usage predictive analytics usage profit margin (%) roi (%) 1.00 0.68** 0.72** 0.54** 0.60** 0.60** market share change (%) 0.68** 1.00 0.79** 0.50** 0.58** 0.55** financial growth (%) 0.72** 0.79** 1.00 0.52** 0.63** 0.62** ml usage 0.54** 0.50** 0.52** 1.00 0.87** 0.47** predictive analytics usage 0.60** 0.58** 0.63** 0.87** 1.00 0.50** profit margin (%) 0.60** 0.55** 0.62** 0.47** 0.50** 1.00 table 3: anova impact of data analytics type on roi data analytics type n mean roi (%) std. dev. f-value p-value no analytics 50 7.2 3.5 12.35 0.0001** machine learning 75 19.2 4.2 predictive analytics 80 16.4 5.0 descriptive analytics 95 14.8 4.7 f 3.56 table 5: independent t-test for capital allocation outcomes between data analytics users and non-users group n mean roi (%) std. dev. t-value p-value data analytics users 210 18.1 4.7 7.45 0.0001** non-users 90 8.2 3.2 t-value 7.45 table 6: regression analysis predicting financial growth based on data analytics usage variable b se beta t-value p-value (intercept) 4.5 1.2 3.75 0.0002** ml usage 1.2 0.3 0.32 4.0 0.0001** predictive analytics 0.8 0.25 0.27 3.2 0.002** profit margin 0.6 0.18 0.20 3.3 0.001** org size (employees) 0.05 0.02 0.15 2.4 0.016** duration of investment 0.04 0.03 0.12 2.1 0.039** table 4: multiple linear regression results data analytics and capital allocation efficiency variable unstandardized coefficients s t a n d a r d i z e d coefficients t-value p-value (intercept) 5.1 4.2 0.0001** ml usage 1.5 0.31 4.1 0.0002** predictive analytics usage 0.9 0.25 3.5 0.001** profit margin 0.7 0.26 3.2 0.002** org size (employees) 0.03 0.18 2.9 0.004** duration of investment 0.05 0.15 2.5 0.02** discussion the findings of the current study have a substantial amount of evidence with regard to the fact that incorporation of data analytics into capital allotment and investment decision-making systems within the u.s. economy not only increases economic performance outstandingly (erica et al., 2024). in particular, the application of machine learning and predictive analytics tools was related positively to roi, market share movements and financial development (olayinka, 2019). they found that those organizations that used advanced data-driven tools were performing better than the ones that used standard data-driven tools and even better than the ones that did not use any tools in terms of efficiency in the pa ge 17 4 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 167-177, 2024 decision-making process and financial performance of an organization which provided additional evidence that the implementation of data analytics tools leads to the improvement of the efficiency of the decision-making process and financial performance of an organization (gade, 2021; hossain et al., 2024). the above findings have shown both positive and negative correlations of the use of data analytics with the allocation of capital outcomes indicating that such tools are beneficial in forecasting and optimization of future performance (ojika et al., 2023). machine learning, as an example, had a positive definite correlation to roi, which means that companies utilizing it attained an average roi of 19.2 percent compared to only 7.2 percent for those which are not doing so (presently actively implementing data analytics) (gintalas, 2022). on the same note, predictive analytics was proved to be an effective tool where organizations that use predictive analytics achieved an average financial growth rate of 16.4% as compared to that of 8.5% by its nonusers. these results confirm the assumption that the use of more sophisticated analytics tools contributes to better and more precise capital allocation decisions (fehrenbacher et al., 2023). comparison to past research the results align with the increasing public of literature that justifies the application of data analytics in helping decision-making across some industries especially in the financial and economic sector (sarker, 2021). multiple past research has stated that data analytics is beneficial in improving investment decisions, financial forecast and resource assigning. as an illustration, (sharma & mehta, 2024) proved that the models of machine learning are more viable than a traditional statistical model in the anticipation of the tendencies toward the development of the stock market, which is corroborated by the findings of this research serving as verification of the positive influence of machine learning tools on roi and financial growth. in the same manner, williams et al. (2021) found that predictive analytics improved portfolio optimization figure 1: graphical view of the research pa ge 17 5 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 167-177, 2024 and risk management that resonate well with our result that predictive analytics had a positive correlation with financial growth (aro, 2024). the literature has concentrated a lot on implementation of data analytics in sector specific cases such as the finance and health sectors but little has been done on the macro economic aspect of data analytics in national allocation of capital. brown and anderson (2020) discussed how data analytics can be implemented into the process of public infrastructure investment in the state sector, implying that it would allow streamlining project selection and budgeting in the industry through predictive analytics (rahaman et al., 2024). our research builds on this and demonstrates that data analytics and notably machine learning, and predictive analytics, can be used to conduct allocation of capital whether in the public or the private sectors and that such moves when deployed have immense positive effects both in terms of return on investment and market share (calder, 20210. the findings of this research also reinforce previous studies indicating that superior decision-making is the outcome of the application of data analytics tools. as an example, (zhu & yang, 2021) discovered that introduction of advanced analytics methods had a massive impact on financial performance of investment companies. in our analysis, organizations with data analytics tools such as machine learning, predictive analytics achieved much higher returns on their investments than those without, which validates the fact that data-driven methods are more effective than the traditional ones (ramya et al., 2024). there are a number of scientific attributes that can describe how the positive relation between the application of machine learning and predictive analytics and better capital allocations have been described (wirawan, 2023. moreover, machine learning models, say, identify complex patterns in a large set of data successfully, which the conventional ones cannot. the tools are also capable of virtually reading through enormous volumes of past and current data to make very precise forecasts regarding market trends, customer behaviours, and financial points of calculation (boone et al., 2019). this forecasting opportunity lets the organizations make smarter investment decisions thus maximizing the returns on investment and financial performance. potential future research, practice, and industry the paper has some valuable implications of the study in terms of future work and applications in the area of capital allocation and investment decision-making. to begin with, it underscores the necessity to research the particular mechanisms in accordance with which data analytics tools affect the results of capital allocation. although this research identifies that there is a close relationship between performance improvement and the use of advanced analytics, it requires more research to establish the cause and effect and the underlying reasons as to why this is occurring. practically, the results have indicated that companies need to invest in adopting and integrating the strategies of implementing the algorithms software to improve decision-making. in particular, the most attention should be paid to machine learning and predictive analytics since these solutions offer the highest roi, market share, and financial development potential. policy makers and business leaders ought to look at how they can ease access to such tools and in particular the smaller organization where such a move may not be feasible due to the absence of resources. this may entail the development of education tools to cultivate data literacy, the rewarding of the use of technology with financial incentives, and the collaboration between small and large companies so that big firms share skills and resources. industry-wise, this paper presents the significance of incorporating data-driven decision making into the allocation of capital. the government, the investment firms and the financial institutions must utilize data analytics to optimize their investment portfolios, focus on high leverage projects, and reduce risks. every economy is gradually turning into a data-driven economy thus organizations that cannot adopt data analytics can be left behind by their more technologically advanced rivals. limitations this research is a good indication of a research study, it is important to note, that there are some drawbacks which might have affected the findings. to begin with, organizations studied in the u.s. were confined to those organizations based in the united states and its findings are not directly transferable to other economies that have different investment styles or technology infrastructures. a deeper investigation is required to be able to discover how the findings could be applied to different areas or countries of diverse economical status. also, the research focused on self-reporting of the organizations, which could be biased in the inabilities to overreport on the use of data analytics tools. in future studies, more objective measures of data analytics adoption, like the analysis of the actual data about the employed tools in organizations, should be used. the other limitation is about the study being a cross-sectional study that restricts the conclusions that can be made on causality. although such findings point to a close relationship between the use of data analytics and better capital allocation processes, some longitudinal research should be conducted and used to evaluate the medium and long-term effects of capital use on organizational performance. conclusion this paper has concluded that the use of data analytics tools especially machine learning, and predictive analytics, results in substantial gains in terms of distribution of capital, roi, market share and financial growth. comparing these findings to the literature that may exist, it is evident that data-driven decision making is emerging as a key ingredient of the successful investment planning. pa ge 17 6 https://journals.e-palli.com/home/index.php/ajase am. j. appl. stat. econ. 3(1) 167-177, 2024 these findings are significant to the theory and practice, raising the directions of the future researches on the process of data analytics in capital allocation, as well as promoting organizations to implement these tools to improve their economic results. the study despite some shortcomings creates an opportunity in relation to the u.s. economy as to how there can be optimization of the process of taking investment decisions through the concept of data analytics. conclusion this study has shown how data analytics has had a major effect in maximizing the use of capital and investments in the american economy. analyzing a sample of 300 various organizations, the study determined that the use of high-tech data analytics tools and specifically machine learning and predictive analytics had a direct correlation with enhanced roi, market share improvement, and the entire financial performance. these results achieved the research objectives, which were demonstrating the importance of data-driven decision in getting a better capital allocation outcome. the scientific contribution that this study has is that it explores the systematic application of analytics tools in data collection in the institutions of both the public and the private sectors during capital allocation, which has not been significantly covered in the literature. the findings highlight the necessity of using data analytics to enhance economic decision-making, which can be used in practice by organizations and policymakers. moving forward, the impact of data analytics on capital allocation in industries that have 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