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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  



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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 long-
term 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.



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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



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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)t- 0.4912 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. 



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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 long-
term 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.



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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 short-
run 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 



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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 
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Central Bank of  Nigeria. (2021). Annual Statistical 
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International Labour Organization. (2021). Impact of  
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International Monetary Fund. (2021). Nigeria: Staff  
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Jibrin, I., & Mubaraq, I. (2021). COVID-19 Pandemic, 
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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), 
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Nasiru, I., & Muazu, I. (2021). The Economic Impact 
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Rasheed, O. N. (2023). The Effect of  Unemployment 
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Sariakin, Fitria, N., Faiza, C., Amiruddin, & Usman, M. 
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