35 © 2021 by the authors; licensee Asian Online Journal Publishing Group Economy Vol. 8, No. 2, 35-48, 2021 ISSN(E) 2313-8181/ ISSN(P) 2518-0118 DOI: 10.20448/journal.502.2021.82.35.48 © 2021 by the authors; licensee Asian Online Journal Publishing Group Trade Liberalization and Economic Growth: The Scenario of the MINT Economies Innocent. U. Duru Department of Economics, Renaissance University Ugbawka, Enugu State, Nigeria. Email: iud3x@yahoo.com Tel: +234-8154827934 Abstract This study investigated the impact of trade liberalization on economic growth for Mexico, Indonesia, Nigeria and Turkey from 1986 to 2020. The Autoregressive Distributed Lag Bounds approach to cointegration and Toda and Yamamoto causality test were utilized for this study. The long-run results revealed that there is no relationship between trade liberalization and real gross domestic product per capita except for Mexico and in this situation, the significance level was at 10%. The results of the causality test showed that no causality was detected between real gross domestic product per capita and trade liberalization for Mexico and Indonesia. A bidirectional causality between real gross domestic product per capita and trade liberalization was found for Nigeria whereas a unidirectional causality from trade liberalization to real gross domestic product per capita was revealed for Turkey. The no causality results for Mexico and Indonesia means that the policy objectives of trade liberalization and economic growth can be pursued independently in both economies. In addition, the bidirectional causality detected for Nigeria suggests that the policy objectives of trade liberalization and economic growth can be pursued together in Nigeria. Furthermore, the unidirectional causality from trade liberalization to real gross domestic product per capita found for Turkey implies that she employs trade liberalization policies effectively for objectives of economic growth, thus trade liberalization causes economic growth. Keywords: Trade liberalization, Economic growth, Cointegration, ARDL, Toda and Yamamoto Multivariate Causality, Nigeria. JEL Classification: C22; F19; O50. Citation | Innocent. U. Duru (2021). Trade Liberalization and Economic Growth: The Scenario of the MINT Economies. Economy, 8(2): 35-48. History: Received: 19 October 2021 Revised: 22 November 2021 Accepted: 14 December 2021 Published: 27 December 2021 Licensed: This work is licensed under a Creative Commons Attribution 3.0 License Publisher: Asian Online Journal Publishing Group Funding: This study received no specific financial support. Competing Interests: The author declares that there are no conflicts of interests regarding the publication of this paper. Transparency: The author confirms that the manuscript is an honest, accurate, and transparent account of the study was reported; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. Ethical: This study follows all ethical practices during writing. Contents 1. Introduction ...................................................................................................................................................................................... 36 2. Literature Review and Theoretical Framework ........................................................................................................................ 36 3. Methodology and Model Specification........................................................................................................................................ 38 4. Data Presentation, Analysis and Discussion of Results ........................................................................................................... 40 5. Conclusion and Policy Implications ............................................................................................................................................. 43 References .............................................................................................................................................................................................. 43 Appendices ............................................................................................................................................................................................. 44 mailto:iud3x@yahoo.com http://creativecommons.org/licenses/by/3.0/ http://creativecommons.org/licenses/by/3.0/ https://www.doi.org/10.20448/journal.502.2021.82.35.48 https://orcid.org/0000-0001-8958-0686 https://www.doi.org/10.20448/journal.502.2021.82.35.48 https://orcid.org/0000-0001-8958-0686 https://www.doi.org/10.20448/journal.502.2021.82.35.48 https://orcid.org/0000-0001-8958-0686 Economy, 2021, 8(2): 35-48 36 © 2021 by the authors; licensee Asian Online Journal Publishing Group Contribution of this paper to the literature This study contributes to existing literature by investigating the impact of trade liberalization on economic growth for Mexico, Indonesia, Nigeria and Turkey from 1986 to 2020. 1. Introduction The link between trade liberalization and economic growth has attracted extensive research in the parlance of international economics. Trade liberalization is believed to be the engine of economic growth and development in economies of the world, especially the developing ones. Chile and Talukder (2014) and FAO (2005) maintained that liberalized international trade affects long-run economic growth. No wonder, the unrelenting and general divergences in the performance of the economy among economies, particularly developing economies has heightened the attention given to trade liberalization-economic growth nexus in recent times (Lall, 2004). Dollar and Kraay (2001) declared that economies such as China, India, Malaysia and Mexico considered post- 1980 globalizers realized quicker rates of growth than the affluent economies. However, in a similar vein, economies that pursued a protectionist regime of trade suffered a declining rate of growth. Evidence from numerous literature infers that barriers to trade decreases growth (see, for instance, (Dollar, 1992; Dollar & Kraay, 2003; Edwards, 1992; Frankel & Romer, 1999; Sachs & Warner, 1995)). However, as a result of the narrow openness measure utilized and other methodological deficiencies, Rodriguez and Rodrik (2000) were doubtful about the findings. Food and Agriculture Organization (2003) argued that many economies have embarked on trade policy reforms to further open their economies and broaden their access to the international market and increase the competitiveness of export. Trade liberalization campaigners are of the view that it will increase the small domestic market; accelerate the transfer of technology; encourage foreign direct investment; generate greater competition; create marketing networks; supply technical and managerial skills; increase the flow of knowledge, resources, goods and services causing higher growth of the economy (Annabi, 2006; Corbett & Winebrake, 2008; Henry, Kneller, & Milner, 2009; McCulloch, Winters, & Cirera, 2003; Stone & Shepherd, 2011; Zhang, 2008). Thus, it is not surprising that Corbett and Winebrake (2008) argued that many economies have witnessed shocking economic growth in the recent past as a result of their readiness to open their borders and markets to foreign investment and trade. Again, Krueger (1997) maintained that the swift industrialization and development in Hong Kong, Singapore, South Korea and Taiwan considered as the four East Asian “tigers”, is a model of beneficiaries of thriving policies of trade liberalization since the beginning of the 1960s. Among the prominent features of the world economy in the last thirty-six years was that developing economies went through fast trade liberalization either individually or as a component of multilateral proposals with the World Bank (WB), World Trade Organization (WTO) and the International Monetary Fund (IMF). Hence, in the early 1980s, most developing economies were encouraged to effect trade reforms besides the Structural Adjustment Programmes (SAPs), imposed by the IMF, the WB and other international organizations, as a necessary step for a free-market economy. Thus, in the mid-1980s and early 1990s, trade reforms were effected and trade liberalization was entrenched in nearly all the developing economies. The view on whether trade liberalization is a prerequisite for fast and sustained economic growth or not remained mixed. While some authors believe that trade liberalization is a must for fast and sustained economic growth (Berg & Krueger, 2003; Edwards, 1993; Edwards, 1997; Edwards, 1998; Krueger, 1990; Krueger, 1998; Winters, McCulloch, & McKay, 2004), others contest this assertion claiming that there is small evidence indicating that trade liberalization is meaningfully related to economic growth (Harrison & Hanson, 1999; Rodriguez & Rodrik, 2001). Regardless of the programmes applied to boost openness to trade in the four developing market economies of Mexico, Indonesia, Nigeria and Turkey, regarded as MINT economies, trade barriers still exist. Irrespective of important trade reforms in MINT economies, some disagreements concerning the role of trade liberalization in them persist. In light of the above, this study seeks to examine the impact of trade liberalization from the perspective of the MINT economies. This research question was addressed in this study: What is the causal link between trade liberalization and economic growth in MINT countries? The general objective of this study is to investigate the causal link between trade liberalization and economic growth in the MINT countries. The rest of the paper is structured as follows. Section 2 focuses on the literature review and theoretical framework. Section 3 discusses the methodology. Section 4 dwells on data presentation, analysis and discussion of results while the conclusion and policy implications are presented in section 5. 2. Literature Review and Theoretical Framework 2.1. Empirical Literature An extensive body of theoretical and empirical literature has investigated the relationship between trade liberalization and economic growth with mixed results. For instance, Nduka (2013) employed the Ordinary Least Square (OLS) methodology from 1970-2008 to examine empirically the nexus between trade openness and economic growth in Nigeria. The findings of the test of cointegration indicated the presence of a long-run equilibrium link between the variables. The findings indicated that trade openness had a positive and significant relationship with economic growth in Nigeria. In a similar vein, Mercan, Gocer, Bulut, and Dam (2013) applied the panel data technique to investigate the impact of openness on economic growth for the fast-emerging economies of Brazil, Russia, India, China and Turkey considered as BRIC-T countries from 1989-2010. The results revealed a positive and statistically significant relationship between openness and economic growth according to apriori expectations. In addition, Dao (2014) applied the panel data techniques and pooled OLS regression to examine the link between trade openness and economic growth for a panel of 71 countries over the globe from 1980 to 2010. The results revealed a positive and significant link between trade openness and economic growth. Hamad, Burhan, and Stabua (2014) used the OLS methodology to investigate the impact of trade liberalization on economic growth in Economy, 2021, 8(2): 35-48 37 © 2021 by the authors; licensee Asian Online Journal Publishing Group Tanzania using annual time series data from 1970 to 2010. This period was decomposed into an era of the closed economy (1970-1985) and an era of the open economy (1986-2010). The results showed that openness to trade had a positive and significant impact on economic growth in Tanzania. However, this impact was comparatively greater in the closed economy era than the era of the open economy. Equally, Manwa (2015) applied the Autoregressive Distributed Lag (ARDL) methodology and fixed effects panel data estimations from 1980 to 2011 to empirically examine the nexus between trade liberalization and economic growth in Southern African Customs Union (SACU) economies of Namibia, Swaziland, Botswana, South Africa and Lesotho. The findings revealed that trade liberalization measured through adjusted trade ratios, tariffs, the real effective exchange rate and trade ratios exerted an insignificant effect on economic growth in Swaziland. Botswana, Namibia and Lesotho. However, in the case of South Africa, the results unearthed that trade liberalization exerted an impact on economic growth consistently. In another similar study, Qazi (2015) employed the ARDL methodology on data from 1971-2013 to examine the effect of financial and trade liberalization on economic growth in Pakistan through the conduits of private saving and investment. The determinants of capital account liberalization were investigated in the study as well. The findings based on the equation of economic growth revealed that the index of financial liberalization, capital stock and labour force had a positive relationship with economic growth. However, the index of financial openness and openness to trade had a negative relationship with economic growth. In addition, the results showed that real deposit rate, per capita real private income, public saving and index of financial liberalization had a positive relationship with private saving in the long run. On the other hand, capital account liberalization, openness to trade and index of financial openness had a negative relationship with private saving in the long run. Furthermore, the findings revealed that per capita real private income, index of financial liberalization and public investment had a positive relationship with private investment in the long run. However, real interest rates and openness to trade had a negative relationship with private investment in the long run. Finally, the findings of the effect of trade liberalization/openness on capital account liberalization/openness highlighted a positive relationship between trade openness and capital account liberalization. Additionally, the findings further revealed that trade liberalization and openness to trade have a positive association with financial openness. Summing up, the general results revealed that the index of financial liberalization had a positive relationship with economic growth, investment and private saving. Kalu, Nwude, and Nnenna (2016) as well utilized the Classical Linear Regression Model (CLRM) from 1991- 2013 to analyze the effect of trade openness on economic growth in Nigeria. The findings showed that export and net export both had positive and significant links with economic growth. On the other hand, imports exerted a positive and significant impact on economic growth. In another similar study, (Hozouri, 2016) employed the dynamic panel data methodology and data from 2000 to 2013 to examine the effect of trade liberalization on economic growth in 17 Middle East and North Africa (MENA) countries of Iran, Yemen, Algeria, United Arab Emirates (UAE), Bahrain, Tunisia, Djibouti, Syria, Egypt, Saudi Arabia, Jordan, Qatar, Kuwait, Oman, Lebanon, Morocco and Libya. The findings revealed that the sensitivity of economic growth has a significant and negative link with tariff changes, however, its link with the volume of trade was positive. Keho (2017) used the ARDL methodology and the Granger causality test proposed by Toda and Yamamoto in another related study to investigate the effect of openness to trade on economic growth in Cote d‟Ivoire from 1965-2014. The results revealed that openness to trade had a positive impact on economic growth in the short-run and long-run respectively. Furthermore, the findings suggest a positive and strong balancing link between trade openness and capital formation in stimulating economic growth. Employing the Error Correction Model (ECM) methodology and time-series data from 1980-2016, Bekele (2017) investigated the link between trade liberalization and economic growth in Ethiopia. The findings showed that trade openness exerted a positive and significant impact on economic growth. In addition, Moyo and Khobai (2018) employed the ARDL approach to cointegration and the Pooled Mean Group (PMG) model in a similar study to investigate the link between trade openness and economic growth in Southern African Development Cooperation (SADC) economies of Botswana, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, South Africa, Swaziland, Tanzania and Zambia using data from 1990-2016. The results showed that trade openness had a negative effect on economic growth in the long run. In addition, using the ECM and data from 1980 to 2016, Elijah and Musa (2019) explored the dynamic impact of trade openness on economic growth in Nigeria. The findings of the short-run and long-run indicated that openness to trade is harmful to economic growth in Nigeria. Additionally, Ajayi and Araoye (2019) employed the Vector Error Correction Model (VECM) to analyze the effect of openness to trade on economic growth in Nigeria from 1970 to 2016. The findings of the test of cointegration indicated the presence of a long-run link between the variables. The results showed that openness to trade exerted a negative impact on Nigeria‟s economic growth. Furthermore, using the ARDL methodology, Duru, Okafor, Adikwu, and Njoku (2020) examined empirically the causal relationship between trade liberalization and economic growth in Nigeria from 1981 to 2018. The results showed a unidirectional causality running from trade liberalization to economic growth (trade-led growth) during the study period. Evidence from the past works reviewed revealed that irrespective of many works that tried to shed light on the impact of trade liberalization on economic growth, the argument, among economists, researchers, policymakers, academics within the realm of policymaking and academia on the relationship between trade liberalization and growth is still open. Based on the results of empirical studies, economies that are more outward-oriented record better performance in terms of economic growth in the long run ((Dollar & Kraay, 2004; Edwards, 1998; Frankel & Romer, 1999; Lee, Ricci, & Rigobon, 2004; Sachs, Warner, Åslund, & Fischer, 1995), among others). However, Rodriguez and Rodrik (2001) argued that these results had been questioned continuously based on at least two limitations: The first flaw is associated with the measurement technique of countries‟ openness to trade. On the other hand, the second flaw has to do with the methodology of estimation. Furthermore, evidence shows that there is no macroeconomic study that had separated the MINT countries and explored the impact of trade liberalization on their economies from the context of general equilibrium. Past Economy, 2021, 8(2): 35-48 38 © 2021 by the authors; licensee Asian Online Journal Publishing Group researches merely confronted this debate from a cross-country context. This part of the investigation is novel. Thus, the nexus between trade liberalization and economic growth to the best of my knowledge has been generally unnoticed and unstudied for the MINT countries. This study fills this gap in knowledge by investigating the impact of trade liberalization on economic growth for the MINT economies. The dependence of MINT countries on trade, the dearth of research on the issue in the context of MINT economies and the methodological flaws previously spotted provide the rationalizations for embarking on this study. In addition, the study made a methodological contribution through the use of the Autoregressive Distributed Lag methodology that makes it possible for the short-run and long-run effects of trade liberalization on economic growth to be ascertained. 2.2. Theoretical Framework In the contention of Thindwa and Seshamani (2014), three theories account for the gains of trade liberalization to countries. These are the comparative advantage theory proposed by David Ricardo, Heckscher-Olin (H-O) neoclassical factor endowment theory of trade and the endogenous growth theory. The endogenous growth theory would form the theoretical foundation for this study. This is based on the premise that the new growth theory or the endogenous growth theory proposed by Romer (1986); Lucas (1988); Romer (1990); Grosman and Helpman (1991) and Barro and Sala-i-Martin (1997) is the main theoretical platform employed by academics for analyzing the link between trade liberalization and economic growth. The comparative advantage theory proposed that there would be the realization of benefits from trade if every country concentrates on the production of the goods in which it has a comparative advantage (Salvatore, 2007). The benefits from trade are decomposed into static and dynamic gains. In the opinion of Thindwa and Seshamani (2014), „‟static gains from trade stem from the fact that countries have different factor endowments and therefore, the opportunity cost of production varies between countries‟‟ (p.964). However, dynamic gains from trade stem from the increased output of resources. Labour is the only input of production in the Ricardian comparative advantage theory. The right of entry to export markets is increased through international trade, and economies would benefit if increasing returns are presumed to hold. These benefits from trade could accrue to the MINT economies under the platform of trade liberalization through the enhanced right of entry to international markets. The foreign reserves of the MINT countries and by implication its import cover could be improved through the foreign exchange gotten from trade. Improved output of labour, attainment of ground-breaking knowledge and technology could spur dynamic gains. The main limitation of this classical model is that it is a static model based on one input of production. Thus, it is subjected to restrictions on how the economy operates today. The H-O model builds upon the Ricardian model of comparative advantage. However, it added extra input of production. The land was incorporated as a second input of production to reflect the endowment of resources. It believes that economies can embark on international trade by exporting products in which they have a comparative advantage. In the contention of this model, comparative advantage is expressed in terms of factor abundance and intensity in a given country. Thindwa and Seshamani (2014) maintained that „‟a country has a comparative advantage if it has a particular resource in abundance and if the ratio of that resource to others is high in production (factor intensity)‟‟ (p.965). Hence, Salvatore (2007) claimed that a state has a comparative advantage in manufacturing a product that employs the resource that shows these features. Trade between economies and resources concentration in the manufacturing of comparatively few products breeds a high standard of living for the economies concerned. The MINT economies are labour-abundant countries as most emerging economies with an endowment of natural resources. Based on these resources, the MINT countries have engaged formal and informal labours together to work on these resources in developing their economies. The endogenous growth theory postulates that growth results from domestic factors in an economy such as innovation, knowledge and investment in human capital. Investment in these internal factors would be of great benefit to economies of the world. This is partly based on the premise that economies of scale in production can stem from such decisions. Reduction in the alterations of prices paves way for effective distribution of internal resources to different sectors of the economy. However, Howitt (1998) maintained that externalities resulting from the engagement of advanced technology in manufacturing in economies of the globe are responsible for economic development. The MINT economies employ resources internal to their economies for the promotion of economic growth. Hence, on the ground of theory, the MINT countries are expected to realize gains from trade in particular and attain economic growth and development in general as a result of trade liberalization and openness. However, the condition in these MINT countries may not be in line with theoretical expectations. This is based on the premise that there are other determinants of economic growth like trade liberalization. Thus, from the standpoint of macroeconomics, trade liberalization may not be the significant element influencing economic growth. However, a principal limitation of the endogenous growth theory is that it has continued to rely on some long-established neoclassical assumptions that are repeatedly unsuitable for emerging economies (Todaro & Smith, 2015). Despite this limitation, it had continued to be the main model of choice among academics in the analysis of the link between trade liberalization and economic growth. 3. Methodology and Model Specification This study used annual data from 1986-2020 to examine the nexus between trade liberalization and economic growth in MINT countries. The data were obtained from World Bank World Development Indicators (WDI) database. The datasets are depicted in Table 1, Table 2, Table 3, and Table 4 respectively in the appendix. The dependent variable is the real Gross Domestic Product (GDP) per capita in constant 2015 US$. The time series characteristics of the variables were checked for unit root using the Augmented Dickey-Fuller (ADF) test. The goodness of fit and model adequacy of our specification was checked through diagnostic and stability tests. The Autoregressive Distributed Lag (ARDL) Bounds test to cointegration suggested first by Pesaran and Shin (1999) and advocated by Pesaran, Shin, and Smith (2001) was utilized to estimate the economic growth equation. Following Hozouri (2016) with modifications, the economic growth equation that would be estimated to establish the association between trade liberalization and economic growth in MINT economies is stated in Equation 1 as: Economy, 2021, 8(2): 35-48 39 © 2021 by the authors; licensee Asian Online Journal Publishing Group (1) Based on economic theory, gross fixed capital formation and openness to trade are expected to be positive. However, general government final consumption expenditure is expected to be negative. Iterating Equation 1 into the ARDL framework yields: ∑ ∑ ∑ ∑ Where p denotes the lag length, Δ represents the difference operator, α0 is the drift, ɛt is the error term, ø1, ø2, ø3 and ø4 are coefficients of short-run dynamics while 1, 2, 3 and 4 are coefficients of the long-run relationship. Thus, Equation 2 is the base equation for measuring the short-run and long-run links among the variables. The technique of the bounds test requires employing the F-test to ascertain the existence of a long-run link in levels between RGDPPCAP and its determining factor. The specification of this test is as follows: (absence of long-run relationship among the variables) against the parameters: (presence of long-run relationship among the variables) The hypothesis stated above is judged through asymptotic critical value bounds of the F-statistic proposed by Pesaran et al. (2001). For instance, if the calculated F–statistic is lower than the I(0) critical values bound, the null hypothesis of the absence of a long-run link between RGDPPCAP and its determinants cannot be rejected. On the other hand, If the calculated F–statistic is greater than the I(1) critical values bound, the alternative hypothesis of the presence of a long-run link in levels between the dependent variable and the independent variables will be accepted. Additionally, if the F–statistic falls between the I(0) and I(1) critical values bounds, the test is inconclusive. If the presence of a long-run relationship in levels between the dependent variable and independent variables was not established, the process terminates. However, if the presence of a long-run relationship was established between the variables in the model, the short-run and long-run estimates of the economic growth model would be measured. Based on Equation 3, the long-run elasticities can be calculated using OLS. ∑ ∑ ∑ ∑ The last step would be the estimation of short-run elasticities. The estimates of the short-run were obtained through an ECM. ∑ ∑ ∑ ∑ ) Where ø1, ø2, ø3 and ø4 in Equation 4 are the parameters of the short-run dynamics, π is the speed of adjustment and ecmt-1 is the error correction term. The parameter π is expected to be negative and significant to corroborate the long-run link between the dependent variable and the independent variables. Table-1. ADF Test Results. Country Variable Augmented Dickey-Fuller (ADF) Level First Difference I(d) Mexico RGDPPCAP -5.0764*** - I (0) GFCF -2.4626 -6.2528*** I (1) OPEN -0.6965 -4.9125*** I (1) GGOVFCE -0.5261 -5.0627*** I (1) Indonesia RGDPPCAP -3.9512*** - I (0) GFCF -1.7972 -3.4376** I (1) OPEN -2.6699 -8.7883*** I (1) GGOVFCE -2.5230 -6.1109*** I (1) Nigeria RGDPPCAP -3.7055*** - I (0) GFCF -1.5707 -4.6706*** I (1) OPEN -3.5378*** - I (0) GGOVFCE -0.7973 -4.9806*** I (1) Turkey RGDPPCAP -6.1037*** - I (0) GFCF -1.7972 -3.4376** I (1) OPEN -2.6699 -8.7883*** I (1) GGOVFCE -2.2298 -6.1676*** I (1) Note: ** and *** indicate statistical significance at 1% and 5% levels. Economy, 2021, 8(2): 35-48 40 © 2021 by the authors; licensee Asian Online Journal Publishing Group 4. Data Presentation, Analysis and Discussion of Results 4.1. Results of Augmented Dickey-Fuller (ADF) Test. The ADF unit root results are depicted in Table 1. The results revealed that most of the series were stationary in first differences. On the other hand, some series such as RGDPPCAP for all MINT economies and OPEN for Nigeria were stationary at levels. Evidence from Table 1 shows that the application of the ARDL methodology is justified since the variables of interest were integrated at different orders. Table-2. Diagnostic Results Country Test Type of Statistic Test Statistic P-value Mexico Breusch-Godfrey Serial Correlation LM Test Ramsey RESET test Jarque-Bera normality test Heteroskedasticity Test: ARCH 𝜒2 F 𝜒2 𝜒2 0.4763 16.1527 7.1447 9.7094 0.7881 0.0007 0.0281 0.6414 Indonesia Breusch-Godfrey Serial Correlation LM Test Ramsey RESET test Jarque-Bera normality test Heteroskedasticity Test: ARCH 𝜒2 F 𝜒2 𝜒2 7.8331 33.5222 2.5720 13.7343 0.0199 0.0000 0.2764 0.3180 Nigeria Breusch-Godfrey Serial Correlation LM Test Ramsey RESET test Jarque-Bera normality test Heteroskedasticity Test: ARCH 𝜒2 F 𝜒2 𝜒2 0.6018 0.2083 4.3560 9.8542 0.7402 0.6533 0.1133 0.6287 Turkey Breusch-Godfrey Serial Correlation LM Test Ramsey RESET test Jarque-Bera normality test Heteroskedasticity Test: ARCH 𝜒2 F 𝜒2 𝜒2 10.4453 0.8033 0.8882 15.8064 0.0054 0.3813 0.6414 0.2003 4.2. Results of Diagnostic Tests Even though there was no cointegration among the variables for Nigeria and Turkey, diagnostic tests were executed based on the estimated short-run models. However, for Mexico and Indonesia that we considered both long run and short run models based on the results of the bounds tests, diagnostic tests were also executed. All the results were reported in Table 2. Based on the results, none of the models had problems of heteroscedasticity. Except for Mexico's economic growth model, the Jarque-Bera normality test revealed that the residuals were normally distributed for the growth models of Indonesia, Nigeria and Turkey. The Histogram for the normality of residuals for the MINT economies are depicted in Figure 1a, Figure 1b, Figure 1c, and Figure 1d respectively in the appendix. In addition, the results revealed that the growth models for Mexico and Nigeria had no problems of serial correlation. However, the economic growth models for Indonesia and Turkey had problems of serial correlation. Furthermore, the results of the Ramsey Reset test shows the possibility of the growth models not being correctly specified for Mexico and Indonesia. This is because the probability values of 0.0007 and 0.0000 against the Ramsey Regression Equation Specification Error Test (RESET) test for Mexico and Indonesia respectively were less than the suggested 5 per cent level of significance. Hence, the null hypothesis that the model was correctly specified was rejected. However, the economic growth models for Nigeria and Turkey were correctly specified. Table-3. Bound Test Results. Country F-statistics Significance Level Lower Critical Value Bound I(0) Upper Critical Value Bound I(1) Mexico 3.9648 1% 5% 10% 5.17 4.01 3.47 6.36 5.07 4.45 Indonesia 4.4956 1% 5% 10% 5.17 4.01 3.47 6.36 5.07 4.45 Nigeria 1.3850 1% 5% 10% 5.17 4.01 3.47 6.36 5.07 4.45 Turkey 2.2671 1% 5% 10% 5.17 4.01 3.47 6.36 5.07 4.45 Note: Critical value bounds for the F-statistic from Pesaran et al. (2001). 4.3. Results of Bound Test Table 3 depicts the results of the bounds F-test. For Mexico and Indonesia, the F-values falls between the lower critical value bound I(0) and the upper critical value bound I(1). Thus, the test was considered inconclusive. However, considering the empirical illustration as depicted in Table 2, the F-statistic obtained for Mexico (3.9648) and Indonesia (4.4956) falls between the lower critical value bound I(0) and the upper critical value bound I(1). Hence, we considered both long-run and short-run models for Mexico and Turkey. On the other hand, the calculated F statistics for Nigeria and Turkey falls below the lower critical value bound I(0). This implies the absence of a long-run relationship or cointegration among variables. Hence, there was no estimation of the ARDL- ECM for Nigeria and Turkey as a result of the absence of cointegration among the variables. However, the short- run model was estimated for Nigeria and Turkey due to no cointegration among variables. Economy, 2021, 8(2): 35-48 41 © 2021 by the authors; licensee Asian Online Journal Publishing Group Table-4. Results of short-run estimates for the short-run model. Country: Nigeria Dependent Variable: RGDPPCAP Variable Coefficient Std. Error t-Statistic Prob. RGDPPCAP(-1) 0.2539 0.2085 1.2177 0.2375 RGDPPCAP(-2) 0.2117 0.2192 0.9661 0.3455 GFCF -0.3211 0.2129 -1.5080 0.1472 GFCF(-1) 0.2466 0.3262 0.7560 0.4585 GFCF(-2) -0.3519 0.2883 -1.2208 0.2364 OPEN 0.0349 0.0914 0.3812 0.7071 OPEN(-1) 0.1498 0.1030 1.4542 0.1614 OPEN(-2) -0.1546 0.0897 -1.7246 0.1000 GGOVFCE -0.4772 0.6641 -0.7185 0.4807 GGOVFCE(-1) 0.5943 0.8779 0.6769 0.5062 GGOVFCE(-2) -0.4240 0.6371 -0.6655 0.5133 C 22.2635 14.7968 1.5046 0.1481 R-squared 0.4962 Adjusted R-squared 0.1940 Schwarz Criterion 6.2160 F-statistic 1.6418 Durbin-Watson Stat 1.8389 Prob (F-statistic) 0.1579 Country: Turkey RGDPPCAP(-1) 0.0057 0.2270 0.0251 0.9802 RGDPPCAP(-2) -0.0165 0.2103 -0.0785 0.9382 GFCF -0.5234 0.8925 -0.5865 0.5641 GFCF(-1) 1.7294 1.2253 1.4115 0.1735 GFCF(-2) -1.0741 0.6625 -1.6213 0.1206 OPEN -0.0660 0.1237 -0.5335 0.5996 OPEN(-1) -0.0643 0.2269 -0.2833 0.7798 OPEN(-2) 0.1848 0.1704 1.0849 0.2909 GGOVFCE -2.5242 1.7138 -1.4728 0.1564 GGOVFCE(-1) 1.7028 2.2886 0.7441 0.4655 GGOVFCE(-2) -0.1501 1.4973 -0.1002 0.9212 C 6.7454 20.7135 0.3257 0.7481 R-squared 0.3017 Adjusted R-squared -0.1173 Schwarz Criterion 6.7699 F-statistic 0.7200 Durbin-Watson Stat 2.1620 Prob (F-statistic) 0.7167 Note: ***, ** and * denote significance at 1%, 5% and 10% respectively. 4.4. Results of the Short-Run Model Because there was no cointegration among the variables for Nigeria and Turkey, the short-run model was estimated for both countries. The estimated coefficients for real GDP per capita at first lag were positive and statistically insignificant for both Nigeria and Turkey. The insignificance of this estimate means that the real GDP of the previous year does not impact on real GDP of the current year in the short run. In addition, the estimates of gross fixed capital formation and general government final consumption expenditure for Nigeria and Turkey were negative and statistically insignificant. The insignificance nature of these estimates suggests that both gross fixed capital formation and general government final consumption expenditure do not impact economic growth in the short run. Based on the results of the short-run model reported in Table 4, openness to trade utilized as a proxy for trade liberalization had a positive and insignificant impact on economic growth for Nigeria in the short run. The insignificant estimate implies that it does not have an impact on Nigeria's economic growth in the short run. However, in the case of Turkey, openness to trade exerted a negative and insignificant relationship with economic growth in the short run. Table-5. Long-run estimates for economic growth model. Country GFCF OPEN GGOVFCE C Mexico 0.4828 [0.8912] (0.3833) 0.2771 [1.7578*] (0.0941) 0.5150 [0.5168] (0.6109) -19.1853 [-1.1727] (0.2547) Indonesia 0.0076 [0.0375] (0.9705) 0.0374 [0.3002] (0.7672) 1.1617 [1.2604] (0.2220) -7.5152 [-0.5173] (0.6106) Note: Probability Values are in bracket - ( ). t-statistics are in [] * denote significance at 10% level. 4.5. Results of Estimated Long-Run Coefficients The estimated long-run coefficients for gross fixed capital formation was positive and statistically insignificant for both Mexico and Indonesia. This results contravenes the findings of Duru and Ezenwe (2020); Duru et al. (2020) and Duru et al. (2021). The coefficient for trade openness used as a proxy for trade liberalization had a positive and significant relationship with economic growth for Mexico. This implies that trade liberalization contributes to economic growth in Mexico in the long run. This result is in line with the submissions of Nduka (2013); Mercan et al. (2013); Dao (2014); Hamad et al. (2014) and Bekele (2017). However, it is contrary to the submissions of Duru et al. (2020). However, trade liberalization had a positive and insignificant impact on economic growth in Indonesia. The insignificance of this estimate implies that it does not impact economic growth in the long run. This result is contrary to the submissions of Duru et al. (2021). Furthermore, the coefficients of general Economy, 2021, 8(2): 35-48 42 © 2021 by the authors; licensee Asian Online Journal Publishing Group government final consumption expenditure for both Mexico and Indonesia was positive and insignificant as reported in Table 5. This result finds an advocate in Duru et al. (2020). Table 6: Results of estimated short-run error correction model. Country: Mexico Dependent Variable: RGDPPCAP Variable Coefficient Std. Error t-Statistic Prob. Δ(RGDPPCAP(-1)) -0.0037 0.1597 -0.0235 0.9815 Δ(GFCF) 1.8696 0.3566 5.2433*** 0.0000 Δ(GFCF(-1)) 0.0847 0.5704 0.1486 0.8834 Δ(OPEN) 0.1223 0.1361 0.8989 0.3794 Δ(OPEN(-1)) -0.1443 0.1567 -0.9206 0.3682 Δ(GGOVFCE) -2.2661 1.0936 -2.0721** 0.0514 Δ(GGOVFCE(-1)) -0.0969 1.1515 -0.0841 0.9338 ECMt-1 -1.1413 0.2958 -3.8578*** 0.0010 ECM = RGDPPCAP + 0.4828*GFCF + 02771*OPEN + 0.5150*GGOVFCE – 19.1853*C – 0.5417*D Country: Indonesia Δ(RGDPPCAP(-1)) 0.4643 0.2201 2.1096** 0.0477 Δ(GFCF) 0.5511 0.4227 1.3037 0.2072 Δ(GFCF(-1)) 0.2538 0.3800 0.6680 0.5117 Δ(OPEN) -0.3020 0.0626 -4.8237*** 0.0001 Δ(OPEN(-1)) 0.0162 0.1016 0.1598 0.8746 Δ(GGOVFCE) -2.0284 1.1360 -1.7856* 0.0893 Δ(GGOVFCE(-1)) 0.2400 1.1596 0.2069 0.8382 ECMt-1 -0.9966 0.2461 -4.0494*** 0.0006 ECM = RGDPPCAP + 0.0076*GFCF + 0.0374*OPEN + 1.1617*GGOVFCE – 7.5152*C – 0.0823*D Note: ***, ** and * denote significance at 1%, 5% and 10% respectively. The results of the short-run dynamic model are illustrated in Table 6. For Mexico, change in gross fixed capital formation and change in general government final consumption expenditure was merely the variables that had a statistically significant effect on the change in real GDP per capita in the short run. Thus, the change in the gross fixed capital formation of the previous year exerted a positive and significant effect on economic growth. In addition, change in general government final consumption expenditure had a negative and significant effect on economic growth. Furthermore, change in trade openness used as a proxy for trade liberalization had a positive and insignificant impact on economic growth. However, in the case of Indonesia, the first lag of the dependent variable, change in trade openness and change in general government final consumption expenditure were merely the variables that had a statistically significant effect on the change in real GDP per capita in the short run. Hence, the change in the real GDP per capita of the previous year exerted a positive and significant impact on economic growth. Also, changes in trade liberalization exerted a negative and significant impact on economic growth. This means that trade liberalization does not contribute to economic growth in the short run. Furthermore, change in general government final consumption expenditure had a negative and significant effect on economic growth. The findings showed that all the Error Correction Terms (ECT) were negative and statistically significant. The ECT measures the speed of adjustment back to long-run equilibrium as a result of a shock. For instance, for Mexico, the coefficient of the lagged ECT in the growth equation model was -1.1413. It is negative and statistically significant. Its coefficient of -1.1413 means that 114% of the previous shock to equilibrium in the long-run economic growth was corrected by it within one year. However, in the case of Indonesia, the ECT coefficient of -0.9966 means that 100% of the previous shock to equilibrium in the long-run economic growth was corrected by it within one year. The ECT coefficient was also negative and statistically significant. Table-7. Results of the Granger Causality Test (TY Augmented Lags Methods). Sources of Causation Country/Dependent Variable RGDPPCAP 𝜒2 OPEN 𝜒2 Mexico RGDPPCAP OPEN - 2.7046 3.4141 - Indonesia RGDPPCAP OPEN - 2.3573 4.9858 - Nigeria RGDPPCAP OPEN - 10.9709*** 9.2840*** - Turkey RGDPPCAP OPEN - 1.9548 7.9540** - Note: *** and ** Indicate significance at the 1 per cent and 5 per cent levels respectively. 4.6. The Results of Toda and Yamamoto Multivariate Causality Test The Toda and Yamamoto causality test was employed to find the causality between real Gross Domestic Product (GDP) per capita and trade openness. The results of the causality test are illustrated in Table 7. In the case of Mexico, the findings revealed that there was no causal relationship between real GDP per capita and openness to Economy, 2021, 8(2): 35-48 43 © 2021 by the authors; licensee Asian Online Journal Publishing Group trade. Thus, the findings implied that there is no causal link between trade liberalization and real GDP per capita in Mexico. Just like Mexico, there was no causal relationship between trade liberalization and real GDP per capita for Indonesia. Unlike Indonesia and Mexico, a bi-directional causal relationship between real GDP per capita and trade openness was detected for Nigeria. For Turkey, a unidirectional causal relationship from trade openness to real GDP per capita was identified. This shows that trade liberalization promotes economic growth in Turkey. Our causality results for Nigeria is in line with the submissions of Nwinee and Olulu-Briggs (2016) and Nduka, Chukwu, Ugbor, and Nwakaire (2013). However, it contradicts the findings of Yakubu and Akanegbu (2018) and Tyopev (2019). 5. Conclusion and Policy Implications This study investigated the link between trade liberalization and economic growth in the MINT economies using time series data from 1986-2020. The ARDL methodology and the Toda and Yamamoto Multivariate Causality test were utilized in this study. The long-run results revealed that trade openness had no impact on real GDP per capita except for Mexico. Even in this situation, trade openness became significant at the 10% level of significance. The results of the causality test showed that no causality was detected between real gross domestic product per capita and trade liberalization for Mexico and Indonesia. A bidirectional causality between real gross domestic product per capita and trade liberalization was found for Nigeria whereas a unidirectional causality from trade liberalization to real gross domestic product per capita was revealed for Turkey. 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Year Mexico Indonesia Nigeria Turkey 1986 -5.0968586 3.7422332 -2.5099486 4.888763818 1987 -0.3338109 2.891931495 0.52584855 7.402176226 1988 -0.7083912 3.80170995 4.5469365 0.447775659 1989 2.10829938 5.507034964 -0.7088532 -1.486309099 1990 3.20338291 5.3479457 8.93068727 7.383071281 1991 2.30201264 5.07105896 -2.164465 -0.967257707 1992 1.68311479 4.711831054 2.02582456 3.324226368 1993 0.15792316 4.760224439 -4.4570781 5.931883178 1994 3.15906427 5.839520704 -4.2328183 -6.175858761 1995 -7.8317514 6.562205374 -2.5300523 6.17718775 1996 5.07244944 6.217928008 1.63459401 5.686793303 1997 5.193558 3.189871378 0.40682595 5.886121916 1998 3.58158308 14.35055602 0.05719452 0.806059059 1999 1.24783802 0.605436819 -1.8957202 -4.750586281 2000 3.44097633 3.482210606 2.4191326 5.32213582 2001 -1.7929026 2.235179698 3.29057075 -7.147845597 2002 -1.4029391 3.090635928 12.4574682 4.890330717 2003 0.07226822 3.376532827 4.65778629 4.252446053 2004 2.49483102 3.630908791 6.48960368 8.28603301 2005 0.86952153 4.289591484 3.72162394 7.559697386 2006 2.98443254 4.107514355 3.32621788 5.620645339 2007 0.78256904 4.946468138 3.8220723 3.797987497 2008 -0.357633 4.620033675 3.97251049 -0.382922616 2009 -6.6741654 3.247328238 5.19795441 -6.027912669 2010 3.61719033 4.812273068 5.15854535 6.919606567 Economy, 2021, 8(2): 35-48 45 © 2021 by the authors; licensee Asian Online Journal Publishing Group 2011 2.22716348 4.748318533 2.52532223 9.509983157 2012 2.24713983 4.606485522 1.47285123 3.093101823 2013 0.0294528 4.151428229 3.85372268 6.664883623 2014 1.54403511 3.639072303 3.51397656 3.168090824 2015 2.01903688 3.555062495 -0.0292823 4.328127771 2016 1.40302067 3.758837332 -4.1683884 1.642463409 2017 0.93145654 3.841197264 -1.7888176 5.794264413 2018 1.05031157 3.987824861 -0.6797247 1.428914877 2019 -1.1393691 3.871444142 -0.3797524 -0.40134604 2020 -9.2054 -3.10714219 -4.2601131 0.659370381 Source: World Bank, World Development Indicators Database. Appendix 2. Data Set on Gross Fixed Capital Formation of MINT Economies. Table-2. Data on GFCF of MINT Economies. Year Mexico Indonesia Nigeria Turkey 1986 18.72587 25.551 54.94827 25.551 1987 17.54044 25.17103 50.04989 25.17103 1988 18.67873 26.99156 43.75477 26.99156 1989 17.37131 28.53473 52.48744 28.53473 1990 17.97946 30.55156 53.12219 30.55156 1991 18.73103 29.67142 48.40018 29.67142 1992 19.6226 28.00259 43.77439 28.00259 1993 20.84942 26.28067 44.47636 26.28067 1994 21.67319 27.57069 42.06784 27.57069 1995 16.35404 28.42981 37.20593 28.42981 1996 18.41382 29.60236 36.58167 29.60236 1997 19.84665 28.30768 38.42226 28.30768 1998 21.08837 25.42951 40.5534 25.42951 1999 21.11672 20.13876 38.278 20.13876 2000 21.48889 19.85085 34.04928 19.85085 2001 19.9337 19.67266 30.03794 19.67266 2002 19.26942 19.42916 26.76866 19.42916 2003 19.7779 19.50606 28.3709 19.50606 2004 20.47646 22.44862 26.06325 22.44862 2005 20.70371 23.64051 24.96612 23.64051 2006 21.54334 24.13099 26.1665 24.13099 2007 21.94191 24.94694 20.18004 24.94694 2008 23.16439 27.69859 18.85977 27.69859 2009 22.12647 31.11477 21.11545 31.11477 2010 21.5827 30.99941 16.81501 30.99941 2011 22.27391 31.30745 15.67631 31.30745 2012 22.84044 32.71963 14.21112 32.71963 2013 21.25247 31.96578 14.16873 31.96578 2014 20.99786 32.51674 15.08353 32.51674 2015 22.43071 32.81193 14.82718 32.81193 2016 22.80226 32.57773 14.72496 32.57773 2017 22.09217 32.16064 14.71562 32.16064 2018 22.0425 32.2885 19.01838 32.2885 2019 20.66706 32.34713 24.62523 32.34713 2020 18.78994 31.73343 28.64594 31.73343 Source: World Bank, World Development Indicators Database Appendix 3. Data Set on Trade Openness of MINT Economies Table-3. Data on OPEN of MINT Economies. Year Mexico Indonesia Nigeria Turkey 1986 29.60622 41.00954 9.135846 41.00954 1987 31.26232 46.97425 19.49534 46.97425 1988 38.79034 47.25456 16.94061 47.25456 1989 38.32965 49.08188 34.18262 49.08188 1990 38.5197 52.89186 30.92474 52.89186 1991 35.78654 54.83956 37.0216 54.83956 1992 35.5535 57.42743 38.22739 57.42743 1993 27.82791 50.52339 33.71975 50.52339 1994 30.70997 51.8771 23.05924 51.8771 1995 46.32102 53.95859 39.52838 53.95859 1996 50.4192 52.26474 40.25773 52.26474 1997 48.77736 55.99386 51.46101 55.99386 1998 50.99612 96.18619 39.27861 96.18619 1999 50.61797 62.94391 34.45783 62.94391 2000 52.43268 71.43688 48.9956 71.43688 2001 47.16607 69.79321 49.6805 69.79321 2002 46.69791 59.07946 40.03517 59.07946 2003 50.20569 53.61649 49.33496 53.61649 Economy, 2021, 8(2): 35-48 46 © 2021 by the authors; licensee Asian Online Journal Publishing Group 2004 53.48615 59.76129 31.89587 59.76129 2005 53.93813 63.98794 33.05946 63.98794 2006 56.09272 56.65713 42.56657 56.65713 2007 56.79528 54.82925 39.33693 54.82925 2008 57.77703 58.5614 40.79684 58.5614 2009 55.96777 45.51212 36.05871 45.51212 2010 60.76032 46.70127 43.32076 46.70127 2011 63.46968 50.18001 53.27796 50.18001 2012 65.76725 49.5829 44.53237 49.5829 2013 63.76488 48.63737 31.04886 48.63737 2014 64.92536 48.08018 30.88519 48.08018 2015 71.08909 41.93764 21.33265 41.93764 2016 76.06221 37.42134 20.72252 37.42134 2017 77.11574 39.3555 26.3476 39.3555 2018 80.5633 43.07431 33.00783 43.07431 2019 77.91529 37.44878 34.02388 37.44878 2020 77.98212 33.19059 25.39979 33.19059 Source: World Bank, World Development Indicators Database. Appendix 4. Data Set on General Government Final Consumption Expenditure of MINT Economies Table-4. Data on GGOVFCE of MINT Economies. Year Mexico Indonesia Nigeria Turkey 1986 8.756565 11.24248 1.929236 7.588005 1987 8.358128 9.424605 1.632709 7.822874 1988 8.484909 8.976333 1.552698 7.612643 1989 8.327433 9.389376 1.315222 9.343364 1990 8.430745 9.534441 1.220141 10.96102 1991 9.115903 9.138088 1.220982 12.41925 1992 9.942875 9.516266 2.047629 12.92502 1993 9.114783 9.023312 2.148452 12.89387 1994 9.466826 8.114181 1.769021 11.65737 1995 8.55116 7.829064 1.166196 10.7858 1996 8.119714 7.566959 0.911235 11.57077 1997 8.261602 6.842805 0.912571 12.25939 1998 8.543129 5.693508 1.375668 10.61322 1999 9.178512 6.604457 1.383378 12.66856 2000 9.515219 6.531995 2.123442 11.92866 2001 9.881126 6.889059 1.990621 12.64592 2002 10.37772 7.257458 1.340488 12.83567 2003 11.03379 8.129486 0.951747 12.59042 2004 10.48729 8.321868 4.787637 12.3152 2005 10.52171 8.109508 4.544547 12.18597 2006 10.34442 8.627169 5.125842 12.90753 2007 10.40538 8.34647 9.44834 13.38969 2008 10.72952 8.423781 9.428957 13.59629 2009 11.91696 9.589178 8.649948 15.65786 2010 11.77136 9.005915 8.8481 14.87456 2011 11.77708 9.058677 8.572152 13.60036 2012 11.94673 9.248788 8.228178 14.12612 2013 12.19139 9.51772 7.155219 14.01838 2014 12.19496 9.425026 6.464486 14.01998 2015 12.3172 9.749414 5.935159 13.80517 2016 12.01041 9.52781 5.384282 14.73321 2017 11.6166 9.120571 4.403315 14.38026 2018 11.57089 9.021232 5.604329 14.68642 2019 11.35833 8.809677 5.572002 15.52729 2020 12.75341 9.289052 8.707691 15.24316 Source: World Bank, World Development Indicators Database Economy, 2021, 8(2): 35-48 47 © 2021 by the authors; licensee Asian Online Journal Publishing Group Appendix 5a: Graph of Diagnostic Test for Mexico 0 1 2 3 4 5 6 7 8 -5 -4 -3 -2 -1 0 1 2 3 Series: Residuals Sample 1988 2020 Observations 33 Mean 3.42e-15 Median 0.459148 Maximum 2.600107 Minimum -4.535895 Std. Dev. 1.776660 Skewness -1.067945 Kurtosis 3.796305 Jarque-Bera 7.144671 Probability 0.028090 Figure-1a. Histogram for normality of residuals for Mexico. Source: Extract from E-views econometric software. Appendix-5b. Graph of Diagnostic Test for Indonesia. 0 1 2 3 4 5 6 7 -4 -3 -2 -1 0 1 2 3 Series: Residuals Sample 1988 2020 Observations 33 Mean -2.56e-15 Median 0.171742 Maximum 2.808352 Minimum -4.472006 Std. Dev. 1.727399 Skewness -0.675743 Kurtosis 3.209787 Jarque-Bera 2.571969 Probability 0.276378 Figure-1b. Histogram for normality of residuals for Indonesia. Source: Extract from E-views econometric software. Appendix-5c. Graph of Diagnostic Test for Nigeria. 0 1 2 3 4 5 6 7 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 Series: Residuals Sample 1988 2020 Observations 33 Mean 3.35e-15 Median -0.216977 Maximum 8.592356 Minimum -4.120166 Std. Dev. 2.761376 Skewness 0.706850 Kurtosis 4.081396 Jarque-Bera 4.355951 Probability 0.113271 Figure-1c. Histogram for normality of residuals for Nigeria. Source: Extract from E-views econometric software Economy, 2021, 8(2): 35-48 48 © 2021 by the authors; licensee Asian Online Journal Publishing Group Appendix-5d. Graph of Diagnostic Test for Turkey. 0 1 2 3 4 5 6 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 Series: Residuals Sample 1988 2020 Observations 33 Mean -2.79e-15 Median 0.433369 Maximum 6.621809 Minimum -9.331744 Std. Dev. 3.642632 Skewness -0.399565 Kurtosis 3.085635 Jarque-Bera 0.888172 Probability 0.641410 Figure-1d. Histogram for Normality of Residuals for Turkey. Source: Extract from E-views econometrics software. Asian Online Journal Publishing Group is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use of the content. Any queries should be directed to the corresponding author of the article.