




































 
 

 

41 
© 2019 by the authors; licensee Asian Online Journal Publishing Group 
 

Economy 
Vol. 6, No. 2, 41-55, 2019 

ISSN(E) 2313-8181/ ISSN(P) 2518-0118 
DOI: 10.20448/journal.502.2019.62.41.55 

© 2019 by the authors; licensee Asian Online Journal Publishing Group 

    
 

 
 
 
A Small Macroeconometric Model of Nigeria 

 
Alarudeen Aminu1    

Joshua Adeyemi Ogunjimi2    

 

 
( Corresponding Author) 

 
1,2Department of Economics, University of Ibadan, Nigeria. 

 

 
Abstract 

This study presents a small macroeconometric model to forecast and simulate policy options for 
the Nigerian economy. The model consists of ten behavioural equations and five identities made 
up of ten endogenous variables and thirteen exogenous variables. Autoregressive distribution lag 
(ARDL) framework is used to estimate the behavioural equations using annual time-series data for 
the period 1981-2014. The predictive ability of the model is evaluated and found to be satisfactory 
as the mean absolute error (MAE), root mean square error (RMSE) and Theil inequality 
coefficient are considerably small. Policy simulations to quantify the impact of shocks to 
government expenditure, exchange rate and crude-oil price on the economy are analysed. The 
results shows that a positive shock in government expenditure raises aggregate output, total 
exports, total import, gross fixed capital formation, exchange rate, consumption, and inflation rate 
while interest rate falls; a negative shock to exchange rate has a negative effect on gross fixed 
capital formation and a positive effect on aggregate national output, consumer price level, interest 
rate, consumption, total export and total imports; and a negative shock in oil prices results in an 
increase in total imports, total exports, consumption, exchange rate, gross fixed capital formation 
and aggregate national output. Hence, the study recommends that the monetary authorities 
employ a managed-floating exchange rate to address the volatility in exchange rate and 
government should formulate and implement policies aimed at diversifying the economy to 
cushion the shocks that result from oil price volatility in the international market. 
 

Keywords: Behavioural equations, Shocks, Macroeconometric model, Autoregressive distributed lag (ARDL), Simulation, Nigeria. 

JEL Classification: C32; C53; E27; N17. 
 

Citation | Alarudeen Aminu; Joshua Adeyemi Ogunjimi (2019). A 
Small Macroeconometric Model of Nigeria. Economy, 6(2): 41-55. 
History:  
Received: 4 June 2019 
Revised: 10 July 2019 
Accepted: 12 August 2019 
Published: 20 September 2019 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher:  Asian Online Journal Publishing Group 
 

Acknowledgement: Both authors contributed to the conception and design of 
the study. 
Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm 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 ...................................................................................................................................................................................... 42 
2. The Nigerian Economy ................................................................................................................................................................... 43 
3. A Review of Macroeconometric Models ..................................................................................................................................... 44 
4. The Structure of the Model ........................................................................................................................................................... 45 
5. Conclusion and Policy Recommendations .................................................................................................................................. 54 
References .............................................................................................................................................................................................. 55 
 

 

 

 

 

 

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Contribution of this paper to the literature 
This paper contributes to the existing literature by constructing an operational and up-to-date small 
macroeconometric model for the Nigerian economy wherein the impact of shocks to monetary policy, 
fiscal policy and oil price are examined so as to devise the appropriate policy with which to achieve 
desired outcomes in the Nigerian economy. 

 
1. Introduction 

Apparently, there are complexities in the real world and inter-relationships among economic variables thus, 
making it an uphill task to appropriately ascertain the effects (direct and indirect) of economic policies. 
Nevertheless, building macroeconomic models provides a plausible basis for inference making as regards the 
direction of impact of policy interventions. An economic model is a simplified representation of a system and 
abstraction of the real world. Thus, a model can be judged relevant by its ability to replicate real world features, 
the degree to which it explains the observed interactions among economic agents, the extent of its ability to 
accommodate indirect effects of policy interventions, and its ability to provide alternative policy directions through 
sensitivity analyses (Nwaobi, 2011). The Nigerian economy is plagued with structural inadequacies which are the 
primary roadblocks to the achievement of the developmental objectives in the country. From independence, various 
forms of macroeconomic instabilities constrained the performance of the economy. The country is faced with some 
fundamental issues which include: persistent fall in the crude oil price (our main export product) in the 
international market, inability of the Nigerian government (especially state governments) to pay the minimum 
wage, a persistent fall in the standard of living, incessant increase in the cost of living, high rate of unemployment, 
infrastructural and institutional decadence, high inflation rate and high level of corruption. 

Nigerians place a high premium on imported products at the expense of locally produced goods, hence the 
reason for the high dependence on imports. Unfortunately, we also import foreign policies without taking 
cognizance of the peculiar nature of our economy. This has had devastating effects on the economy as a whole as 
policy makers are frustrated by the ineffectiveness of economic policies in the country. An economic policy that 
works perfectly well in an economy might fail in another due to the different institutional and economic 
frameworks, among other reasons, in the economies. Economic policies are contextual, hence the reason for their 
failure if taken out of context. Thus, it is highly imperative that government agencies and macroeconomic modelers 
understand the intricacies of their domestic economy. 

For instance, the adoption of the structural adjustment programme (SAP) in 1986 had a debilitating effect on 
the Nigerian economy. This is because the World Bank and IMF, who instigated the programme, had a poor 
perception of the Nigerian economic problem. Thus, what was intended to serve as an economic panacea led to 
more devastating situations. One of the major effects of SAP is that it has eroded the value of the domestic currency 
overtime. Most economies undergoing adjustment have experienced a drastic fall in the value of their currency 
relative to other currencies. SAP, by its nature, is inflationary because it raises the amount of local currency used in 
buying units of local goods and import. 

Another notable example is the recent issue on devaluation following the incessant fall in the value of naira 
which has brought representatives from the World Bank and IMF to persuade the Nigerian government to devalue 
her currency. The effects of devaluation on a country like Nigeria will be devastating for the following reasons: 
Nigeria is highly import-dependent, her export (mostly primary products) prices are quoted in foreign currencies 
and so is her import. Apparently, a devaluation of the domestic currency will further worsen the situation of the 
economy. This policy stance came due to a relatively good understanding of the structure of the Nigeria economy. 
A more comprehensive knowledge of the Nigerian situation will help policy makers formulate beneficial policies 
and not policies that will further worsen the economic situations of the country. 

Hence, to tackle the existing and impending problems facing the Nigerian economy, an appropriate framework 
that will be an accurate representation of the domestic economy and also serve as a point of reference is imperative. 
It is also essential to study the nature of relationship between different macroeconomic variables in Nigeria in order 
to formulate well-informed policies. However, in building this model, it is also important that the modelers have a 
sound knowledge of the basic structure of the economy to aid the determination of the various sets of policy 
interventions that will correct the structural inadequacies in the economy. They should also be aware of the 
linkages among the various sectors of an economy and the impacts (direct and indirect) of policy coordination on 
individual sectors. This study identifies few research gaps. Several attempts have been made at building an 
operational and up-to-date macroeconometric model for Nigeria, the most recent being Central Bank of Nigeria 
(CBN) (2010); Olofin et al. (2014) and Nkoro and Uko (2018). However, there are a number of research gaps in 
these studies. For instance, CBN (2010) estimated only short-run equations for each of its stochastic equations in 
the model neglecting the long-run equations which should form the basis for simulation and forecast. Also, Olofin 
et al. (2014) developed a small-scale macroeconometric model which focused primarily on the response of key 
macroeconomic variables to changes in the monetary policy rate (MPR) in Nigeria neglecting the impact of fiscal 
policy and exchange rate movement on the performance of key macroeconomic variables in Nigeria. Moreover, 
Nkoro and Uko (2018) only focused on the impact of oil price shocks and monetary policy on macroeconomic 
performance without accounting for the impact of fiscal policy. Hence, this study fills these research gaps.  

The novelty of this study lies in its contribution to the theoretical, methodological and empirical literature. 
Theoretically, this study contributes to the extant literature by adopting the standard Keynesian approach used by 
Asteriou et al. (2011) and Khan (2014) for Greece and India respectively. This approach is holistic in that it covers 
all sectors of the economy. To the best of our knowledge, this approach has not been used in developing a 
macroeconometric model in Nigeria. With respect to methodological contribution, many studies favour the choice 
of ordinary least square (for example the works of Olayide et al. (1981); CBN (2010); Hanif et al. (2011); and 
Egwaikhide et al. (2012) among others), the seemingly unrelated regression equations (see e.g. Akanbi and Du Toit 
(2011)) the two-stage least square technique (see e.g. Khan (2014)). Most of these studies did not take account of 
the stationarity properties of the macroeconomic variables, a practice which results in spurious regression. In 
addition, the long-run relationship, an important basis for forecast, of the macroeconomic variables was not 



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ascertained before estimation by several studies. These inadequacies render the findings from the model estimation 
unfit for policy analysis. However, to circumvent these inadequacies, this study adopts a relatively more robust 
model with inherent cointegration test technique: the autoregressive distributed lag (ARDL). Gurara (2013) have 
used same methodology for similar study in Rwanda. The empirical contribution of this study lies in the fact that 
most studies (CBN, 2010; Egwaikhide et al., 2012; Olofin et al., 2014) on macroeconometric model of Nigeria 
examined the effects of monetary and fiscal policy on the Nigerian economy. Since Nigeria doubles as a net oil 
exporter and importer, it is needful to evaluate the transmission mechanism through which changes in oil price in 
the international market filters into the aggregate economy, this analysis is missing in the literature. However, this 
study will fill this knowledge gap by examining the impact of changes in government expenditure (fiscal policy), oil 
price and exchange rate (monetary policy) on the overall performance of the Nigerian economy so as to devise the 
appropriate policy with which to achieve desired outcomes in the Nigerian economy. 

This study aims at developing and estimating a model that explains the relationships between major 
macroeconomic variables and to operationalise the model by using it to forecast and simulate policy options for the 
economy. Friend and Taubman (1964) argued that small models are the best at explaining the economy more 
efficiently as against large models that make analysis of the economy more difficult and cumbersome due to the 
many equations in the model. Thus, they suggested that economic modellers should “Keep it Sophisticatedly 
Simple (KISS)”. This principle forms the premise on which this study develops a small macroeconometric model of 
Nigeria. 

The rest of this paper is structured as follows: Section two takes an overview of the Nigerian economy while 
Section three contains the review of relevant literatures. Section four presents the methodology and empirical 
results of this study and Section 5 concludes this study with policy recommendations. 
 

2. The Nigerian Economy 
Nigeria, with a population of over 170 million, is the most populous black nation with total land area of 923,773 

square kilometres, covering five different vegetation zones. Nigeria‟s economy is second to none in Africa in terms 
of key macroeconomic indicators. Nigeria‟s gross domestic product (GDP) was estimated at N251.05 billion in 
1981 and it grew to N328.61 billion, N412.33 billion, N776.33 billion and N950.11 billion in 1990, 2000, 2010 and 
2013 respectively (see CBN (2014)). The Nigerian economy is dominated by agricultural and crude oil production 
which are both primary products. The oil and gas sector is the main driver of the economy, in terms of its share in 
government revenue, foreign exchange and foreign investments inflows. The contribution of the primary sector to 
GDP in 1981 was 33.6%, 38.5% for secondary sector and 27.9% for tertiary sector. In 1986, the year in which 
structural adjustment programme (SAP) was introduced, the share of primary sector to GDP stood at 41.4% and it 
rose to 42.1% in 2002 but fell to about 40% in 2014. However, the share of the secondary sector to GDP reached an 
unprecedented level of 40% in 1990 and further fell to 21.3% in 2014. This is because Nigeria is heavily dependent 
on imports at the expense of local production. The tertiary sector‟s share of GDP rose markedly from 29% in 2002 
to 40.3% in 2012. This implies that the service sector grew markedly in the new millennium which was traceable to 
telecom investment by firms in the communication sector thereby leading to a rapid development of the sector. 

Government expenditure has been changing and volatile overtime. Between 1981 and 1983, the capital 
expenditure was more than the recurrent expenditure. The reverse was the case between 1987 and 1996 as 
recurrent expenditure was greater than capital expenditure. This implies that capital projects were not adequately 
provided during those periods. Between 1996 and 1999, however, government spent more on capital expenditure 
than on recurrent expenditure. From the new millennium to 2014, recurrent expenditure outweighs capital 
expenditure as recurrent expenditure was N3417.58 billion while capital expenditure stood at N783.12 billion in 
2014. 

Nigeria‟s trade interaction with the rest of the world is categorized mainly into oil and non-oil due to the dual 
nature of the economy. A significant difference exists in the export of oil and non-oil products. The ratio of oil to 
non-oil export was N10.680.5 to N342.8 in 1981, N106626.5 to N3259.6 in 1990, N1920900.4 to N24822.9 in 2000 
and N14326518.7 to N913708.4 in 2011. This clearly shows that the mainstay of the Nigerian economy is crude-
oil; it is the major source of foreign exchange; and Nigeria depends heavily on the proceeds from oil which is a 
primary product susceptible to fluctuations in the international market.  

Implicit price deflator rose from double-digit of 37.57 in 1981 to about 1108.76 in 1991, before declining to 
1026.97 in 1998. The figure again jumped to about 1190.32 in 1999 and it continued to grow until it reached 
3614.44 and 4561.28 in 2008 and 2012 respectively. It can be observed that the implicit price deflator grew steadily 
overtime. The increase in the implicit price deflator was attributed to increases in the domestic pump-price of 
petroleum products. Another notable reason for the increase in the implicit price deflator especially in 2008 and 
2009 is the effects of the global financial crisis which led to naira depreciation and a reduction in general credit 
creation.  

The monetary policy rate which substituted the minimum rediscount rate (MRR) in 2006 is the official interest 
rate of the Central Bank of Nigeria (CBN) and is the anchor rate for other interest rates in Nigeria. MRR was 
highly regulated in the period between 1970 and 1986. It was 4.5 percent between 1970 and 1975 before it 
experienced marginal increases in subsequent years and become stable again at 10 percent between 1984 and 1986. 
The CBN fixed the MRR and removed all controls on interest rate in 1987 to depict the direction it intends 
interest rate to go. MRR stood at 12.75 percent in 1987 and1988 and 18.5 percent in 1989 and 1990. It however 
plummeted to 13.5 percent from 1994 to 1997 and fluctuated till 2006 when MRR was change to MPR. This 
change had an almost immediate effect as interest rate fell from two-digits to one-digit between 2007 and 2011 
before it rose to 12 percent in 2012 and 2013 and to 12.25 percent in 2014. 

Nigeria is financially indebted to Paris Club, London Club, Multilateral creditors, promissory note creditors, 
bilateral and private sector creditors. Nigeria‟s external debt stock profile stood at N2.33 billion in 1981. The 
figure increased significantly to N100.79 billion and N633.02 billion in 1987 and 1998 respectively. It further 
increased markedly to N2577.37 billion, a 307% increase. This happened at a time when there was a change in 
government from military to democratic rule. External debt further rose tremendously to N4890.27 billion in 2004 



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but plummeted to N438.89 billion in 2007. However, the figure started increasing as there was a change of 
government and it stood at N1631.52 in 2014. 

Nigeria‟s stock of external reserves depends largely on the world price of crude oil. The reserve derives from 
the excess of receipts on export of crude oil on import. The reserve grew persistently because of growing price of 
crude oil in the world market. Nigeria‟s external reserves trended downwards from US$4682.9 million in 1981 to 
US$456.6 million in 1984 before it rose gradually to US$981.8 million. Since then the figure has trended upwards 
till 1988 when it stood at US$6022.2 million and then declined marginally to US$3662 million in 1989. However, 
there was a persistent increase in the stock of external reserve from 1989 to 2008 when the stock reached an 
unprecedented US$58472.8 million before it started falling again until it reached US$37220.3 million in 2014.  

The exchange rate in Nigeria has been fluctuating overtime. Between 1974 and 1980 the exchange rate of naira 
in relation to the US dollar stood between N0.54 and N0.71 with the naira appreciating against the dollar during 
this period, after 1980, the exchange rate started depreciating. However, the value of naira in relation to the US 
dollar has been rising over the years as a result of the various policies of the CBN. The rate was fixed by the then 
military government and it increased by about 300% from N21.89 in 1998 to N92.69 in 1999 with inflation rate 
rising by 6.6%. The exchange rate hit a triple digit in year 2000 with the rate at N102.11 and it has been increasing 
since then till it hit N150.31 to a dollar in year 2010 and N158.55 in 2014. 

Stock/securities, debt/bonds and equities are the major financial instruments traded in the Nigeria stock 
exchange market. The stock exchange market has experienced tremendous growth overtime from N5 billion in 
1980 to N10 billion in 1988. It later increased by about 162% from N180.4 billion in the 1995 to N472.3 billion in 
2000. It later increased to four-digit of N1359.3 billion in 2003 and grew astronomically to N13181.7 billion in 
2007, a 870% increase. However, the figure fell to N9563 billion and N7030.4 billion in 2008 and 2009 respectively. 
This decline can be attributable to the effects of the global financial crises of that period. However, the figure 
increase to reach an unprecedented level of N19077.4 billion in 2013 before it fell to N16875.1 billion in 2014.The 
persistent rise in the market capitalization depicts how the Nigerian stock exchange market evolved overtime. The 
recapitalization of commercial banks, regulation of the market, and improved confidence in the market, among 
other reasons, contributed immensely to rise in stock prices. 
 

3. A Review of Macroeconometric Models 
Efforts have been made by individuals and government institutions to develop a macroeconometric in order to 

understand the transmission mechanism through which policy changes affects different macroeconomic variables of 
interest. For instance, Gurara (2013) analysed the macroeconomic impact of different policy interventions by 
developing a macroeconometric model for Rwanda. The ARDL framework was employed to estimate the individual 
macroeconomic equations. The result showed that the model effectively tracked historical data given its low 
biasness and desirable Theil‟s inequality coefficient. The simulation results showed raising expenditure on 
infrastructure will lead to an increase in inflation and the scenario of cutting aid flows will have a devastating effect 
on growth.  

With the purpose of analyzing the response of macroeconomic variables to changes in monetary policy in the 
Pakistan economy, Hanif et al. (2011) constructed a small macroeconometric model. The model contains 17 
equations including 11 behavioural equations and 6 identities. Annual time-series data for the period 1973-2006 
was estimated using the ordinary least squares method. The findings revealed that the most effective monetary 
policy transmission mechanism is the credit channel; government investment crowds-in private investment; and 
demand for narrow money is relatively stable.  

Khan (2014) developed a macroeconometric model to forecast the supply and demand of food in India from 
2012 to 2013. Six equations (3 structural equations and 3 identities) were specified and estimated using the two-
stage least square (2SLS) method and the projection was based on compound average growth rate (CAGR). The 
results suggest an increase in both demand for and supply of food items by 2030 however, the government will 
have to make concerted efforts at increasing investment in the agricultural infrastructure and encouraging labour 
participation in the agricultural sector. 

In a bid to account for model validation and bridge the theory-data gap in, Spanos and Papadopoulou (2013) 
constructed a small macroeconometric model for Cyprus. The model contains 8 endogenous variables and 20 
exogenous variables. Quarterly time-series data from 1995Q1 to 2012Q4 was estimated and the estimates were 
used to forecast from 2013Q1 to 2020Q4. The results showed a less severe recession in Cyprus in 2013 and a 
positive GDP growth rate in 2017.  

In their study, Asteriou et al. (2011) developed and estimated a small macro-econometric model for Greece 
purposely to examine various economic policy scenarios and their effectiveness in the debt crisis confronting the 
Greek economy. The study adopted a standard post-Keynesian approach to model the Greek economy. More 
precisely the model contains behavioral equations for investment, consumption, prices, imports and exports, labor, 
wages, factors demand and potential GDP. The data for the macroeconomic variables are annual data for the period 
between 1980 and 2010. The results of the estimation of the equations showed that no particular policy can 
effectively tackle the high public debt to GDP ratio in Greece. Thus, it was recommended that the Greek 
government boost competitiveness and create jobs in order to raise GDP growth rates beyond the EU average. 

Several individuals and institutions have made efforts geared towards building a macroeconometric model for 
the Nigerian economy in the past. The purpose of their construction varied from purely academic exercise, to 
practical policy applications. Recent efforts have been made to improve the macroeconometric model for Nigeria as 
the previous studies were not simulated for numerical solution and their common features are their emphasis on 
demand side and neglect of micro considerations. For instance, Akanbi and Du Toit (2011) developed all-inclusive 
macroeconometric models for Nigeria to bridge the gap between growth and poverty in Nigeria. The models 
examined the existing demand-side and supply-side constraints hampering growth and it identified socio-economic 
constraints as the major sources of poverty in Nigeria. Using annual time-series data for the period 1970-2006, the 
models were estimated using the Engle-Granger two-step cointegration technique to capture the dynamic short-
run and long-run properties of the Nigerian economy. The models were subjected to policy scenarios to find the 



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appropriate policy options capable of stimulating growth and reducing poverty. The result revealed that the 
supply side will in no small way help devise suitable policies to address the high and sticky poverty level in 
Nigeria. 

In addition, Udah (2009) developed a model broadly classified into six blocks: aggregate demand, fiscal, 
monetary, labour market, production and the external sector block, for Nigeria. The results showed that the 
government‟s debt to the banking system is a medium through which government finance and monetary variables 
are linked. The model was simulated for the period 1970 to 2004 and the results showed that a monetary squeeze of 
20 percent would reduce inflation rate faster than if there was a 10 percent reduction in money supply. As a result 
of this reduction in money supply, employment, output and government expenditure will also reduce which may 
have devastating effects on the domestic economy. The paper thus concluded that Nigeria has to choose between 
higher GDP growth or inflation as a trade-off exists between these variables in Nigeria. 

Moreover, CBN (2010) constructed a medium macroeconometric model which disaggregated the Nigerian 
economy into five sectors (i.e. the real, the external, the fiscal, the monetary and the price sectors) discussed under 
six blocks namely supply, private demand, government, external, monetary/financial  and price blocks. The 
linkages of the six blocks were identified and the model solved simultaneously to incorporate those linkages. The 
model was simulated and model evaluation tests were performed. Single-equation analyses indicated that the 
stochastic equations were well specified and in-sample performance was satisfactory. The dynamic simulation 
results showed that the simulated and actual data are very close. 

Evaluating the response of some macroeconomic variables to changes in fiscal and monetary variables, 
Egwaikhide et al. (2012) developed a structural macroeconomic and estimated it using the ordinary least squares 
technique. The result showed that monetary policy is more effective in stabilizing and managing counter-cyclical 
output in the Nigerian economy than fiscal policy. Specifically, interest rate is a very tool for stimulating aggregate 
output. However, fiscal policy play more important role than monetary policy in the long-run as monetary policy 
wanes as time goes by. Similarly, Nworuh and Nwachukwu (2010) developed a macroeconometric model for 
Nigeria. The result showed a desirable variance proportion, bias proportion, covariance proportion and Theil‟s 
inequalities indicating that the model depicts reality and is useful for policy prescription. 

Olofin et al. (2014) built a small macroeconometric model of Nigeria to support the efforts of the Central Bank 
of Nigeria (CBN) in developing a pragmatic model that will help provide evidence-based monetary policy decisions. 
The model is termed „CBN MAC II‟ and is a revised edition of the CBN MAC I. The model was subjected to 
sensitivity analysis and was found to be adequate in tracking developments Nigeria‟s key macroeconomic 
indicators. The results showed that the monetary authority has to choose between the objectives of lowering the 
lending rate and maintaining a stable exchange rate. 

Similarly, Nkoro and Uko (2018) constructed small macroeconometric model for Nigeria to evaluate the 
impacts of oil price shock and monetary policy on the economy. The model contains 19 equations (12 behavioral 
equations, 3 definitional equations and 4 identities) and was estimated using the ordinary least square method 
using data from 1981 to 2012. The results showed that the model tracks historical data well and that an increase in 
monetary policy rate will make private investment, nominal interest rate, inflation, and GDP dwindle while 
unemployment will remain constant. Likewise, a rise in crude-oil price make government revenue and GDP 
increase while lending rate, inflation and exchange rate will remain unchanged.  
 

4. The Structure of the Model 
This study builds a small macroeconometric model of Nigeria. The model comprises ten behavioural equations 

and five identities with ten endogenous variables and thirteen exogenous variables. The autoregressive distribution 
lag (ARDL) framework is used to estimate the behavioural equations in the model using annual data sourced from 
Central Bank of Nigeria Statistical Bulletin, OPEC Annual Statistical Bulletin and World Development Indicators 
(WDI) for the period between 1981 and 2014. The validity of the model is checked through both within-sample and 
out-of-sample forecasts.  
 

4.1. Model Specification 
4.1.1. Aggregate Output 

Following Cobb-Douglas production function, aggregate capital stock and aggregate labour force are the 
major drivers of aggregate output in an economy. Also, following the specification of the aggregate output function 
in the studies by John and Chris (2000) labour force (LABF), human capital measured using expenditure on 
education (HCAP) and physical capital represented by gross fixed capital formation (GFCF) are determinants of 
aggregate output (RGDP). Aggregate output is also a function of exchange rate. Thus, the aggregate output model 
can be specified as follows: 

RGDP = γ1 + γ2HCAP + γ3GFCF + γ4LABF + γ5EXR + µ 

A priori Expectation: γ2, γ3, γ4> 0 γ5 < 0 
 

4.1.2. Interest Rate 
The nominal interest rate equation is assumed to be an inverted Keynes‟ money demand function where 

interest rate is influenced by money demand represented in this study by total monetary assets (M2) and national 
income (RGDP). The studies by Folawewo and Tennant (2008); Ferdinand et al. (2015) and Anthony and 
Babatunde (2012) show that interest rate is influenced by money supply (M2), consumer price index (CPI), 
exchange rate (EXR), reserve requirement (RR), and aggregate output (RGDP). Thus, the nominal interest rate 
can be specified as: 

INT = ɮ1 + ɮ2M2 + ɮ3CPI + ɮ4RR + ɮ5RGDP + ɮ4EXR + µ 

A priori Expectation: ɮ2, ɮ3, ɮ5> 0 while ɮ4, ɮ5 < 0 
 
 



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4.1.3. Consumer Price Index 
Fatukasi (2005) showed that the Nigerian consumer price index is influenced by interest rate (INT), exchange 

rate (EXR) and money supply (M2). Furthermore, Saravanan (2015) included money supply (M2) and government 
expenditure (GEXP) as drivers of consumer price index. Olatunji et al. (2010) incorporated real GDP (RGDP) into 
their CPI model specification. Hence, the CPI model can be specified as:  

CPI = Ʊ1 + Ʊ2INT + Ʊ3EXR + Ʊ4M2 + Ʊ5RGDP + Ʊ6GEXP + µ 

A priori Expectation: Ʊ3, Ʊ4, Ʊ5, Ʊ6 > 0 while Ʊ2< 0 
 

4.1.4. Exchange Rate 
Following the specification of Ajao and Igbekoyi (2013) exchange rate (EXR) is driven by trade openness 

(TROP), interest rate (INT) and money supply (M2). Udousung et al. (2012) also incorporated trade openness into 
their exchange rate model. Ben (2011) in his model of exchange rate, included the price of oil (OILP) to depict that 
the price of oil in the international market determines the value of naira in relation to the US dollars.  

EXR = ƕ1 + ƕ2TROP + ƕ3INT + ƕ4M2 + ƕ5OILP + µ 

A priori Expectation: ƕ2, ƕ3, ƕ5> 0 while ƕ4< 0 

 
4.1.5. Consumption  

Consumption is the largest component of aggregate demand. It can be divided into private consumption 
(PCON) and government consumption (GCON). Following the Keynes‟ absolute income theory of consumption 
and Kuznet‟s theory of consumption, the primary determinants of consumption are income (RGDP). Also, in line 
with the law of demand, price level (measured with CPI) is a major determinant of quantity demanded 
(consumption). Due to the import-dependent nature of the Nigerian economy, the exchange rate (EXR) is also a 
major determinant of consumption expenditure in Nigeria. Because foreign remittances (REM) have become a 
significant source of income for many households in the country, net income from abroad is incorporated into the 
consumption model. The consumption model, which follows (CBN, 2010) model closely, is specified as follows: 

CON = ɮ1 + ɮ2RGDP + ɮ3CPI + ɮ4EXR + ɮ5REM + µ 

A priori Expectation: ɮ2, ɮ5> 0 while ɮ3, ɮ4< 0 
 

4.1.6. Gross Fixed Capital Formation 
Investment is the second key component of aggregate demand after consumption because it is a veritable 

instrument for achieving and sustaining economic growth. Aggregate investment can be decomposed into private 
investment (PRINV) and public investment (PUINV). Following Keynesian and classical investment theories, 
interest rate (INT) and income (RGDP) drive investment. Duruechi and Ojiegbe (2015) incorporated inflation rate 
(CPI), exchange rate (EXR) and interest rate (INT) into their investment model as explanatory variables. 
Investment is measured by gross fixed capital formation (GFCF). Thus, the investment model can be specified as 
follows: 

GFCF = ƕ1 + ƕ2INT + ƕ3RGDP + ƕ4EXR + ƕ5CPI+ µ 

A priori Expectation: ƕ3> 0 while ƕ2, ƕ4, ƕ5< 0 
 

4.1.7. Export  
Nigeria‟s export can be disaggregated into oil export and non-oil export. While oil export dominates Nigeria‟s 

export portfolio, non-oil only constitute a small proportion of the overall export of the country. 

 
4.1.7.1 Oil Export 

Nigeria‟s major export product is crude-oil whose price is exogenously determined at the world market and 
whose quota is regulated by OPEC. Thus, the barrels of crude oil extracted per day determine the volume of crude 
oil Nigeria will supply to the world market. United States of America is the major buyer of Nigeria‟s export 
product as she imports about 40 percent of Nigeria‟s crude-oil thus a change in US‟ national income and the 
naira/US$ exchange rate directly affects the Nigerian economy. From the foregoing, Nigerian oil-exports (OILX) 
can be said to be influenced by price of crude-oil in the world market (OILP), OPEC quota (OPEC), foreign 
demand of crude-oil represented as United States GDP (USGDP) and exchange rate (EXR). This specification is in 
consonance with that of CBN (2010). Thus, the oil-export equation can be specified as follows: 

OILX = δ1 + δ2OILP + δ3OPEC + δ4USGDP + δ5EXR + µ 

A priori Expectation: δ2, δ3, δ4, δ5 > 0 
 

4.1.7.2. Non-Oil Export  
Prior to the discovery and exploration of crude oil in commercial quantities in the early 1970s, the mainstay of 

the Nigerian economy was agriculture. Although, crude-oil dominates the Nigerian exports profile, non-oil 
products and other natural resources are still being exported to other nations of the world but at a relatively lower 
rate than what obtained before the 1970s. Non-oil export is influenced by production in the non-oil sector (NOILY) 
and exchange rate (EXR). This specification follows (CBN, 2010) specification of the non-oil sector equation. Thus, 
the non-oil export (NOILX) equation can be specified as follows: 

NOILX = φ1 + φ2NOILY + φ3EXR + µ10 

A priori Expectation: φ2, φ3> 0 
 

4.1.8. Import 
Nigeria is highly import-dependent such that we import both consumer and capital goods. Imports constitute a 

significant share of inputs for both domestic production and final consumption. This study disaggregates import 
into oil and non-oil import. 



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4.1.8.1. Oil Import  
Nigeria exports crude-oil and imports its refined products. Nigeria‟s refineries refine crude-oil but not 

efficiently thus, the little production is augmented with import to meet the growing demand for crude-oil products. 
Exchange rate and price of crude-oil in the international are other important determinants of oil import. Hence, 
demand for oil imports (OILM) is influenced by domestic production of crude oil (DPRO), the price of crude-oil 
(OILP) in the international market and the exchange rate (EXR). Thus, the oil-import equation can be specified as 
follows: 

OILM = Ω1+ Ω2DPRO + Ω3EXR + Ω4OILP + µ 

A priori Expectation: Ω2, Ω3, Ω4< 0 
 
4.1.8.2. Non-Oil Import  

Usually, countries import goods and service they cannot produce, goods in which they do not have comparative 
advantage, and to augment domestic production, among other reasons. The latter reason implies that the volume of 
Nigeria‟s imports depend on the country‟s non-oil output (NOILY). The tariffs (TAR) levied on imported goods 
also influence the volume of import together with exchange rate (EXR) and domestic interest rate (INT). This 
specification follows closes that of CBN (2010). Thus, non-oil imports model can be specified as follows: 

NOILM = §1 + §2NOILY + §3TAR + §4EXR +§5INT+ µ 
A priori Expectation: §2, §6> 0 while §3, §4, §5< 0 
 
Identities 
CON = PCON + GCON 
GFCF = PRINV + PUINV 
EXP = OILX +NOILX 
IMP = OILM +NOILM 
RGDP = CON + GFCF + GEXP + EXP – IMP 
 
4.2. Empirical Results 
4.2.1. Augmented Dickey Fuller Unit Root Test 

The results of the augmented dickey-fuller unit root test are presented in Table 1. The results show that the 
first difference of most of the variables were taken before they became stationary thus they are integrated of order 
1, that is, I(1). A few variables like interest rate, non-oil export, OPEC quota and tariff are found to be stationary 
without differencing their series. Hence, it is necessary to check if long-run relationship exists among the variables. 
The autoregressive distributed lag (ARDL) Bounds test approach to cointegration is employed to investigate if 
these variables converge in the long-run. The choice of this approach is premised on the fact that the series are a 
combination of I(0) and I(1) without the inclusion of I(2). 
 

Table-1. Augmented dickey fuller unit root test result. 

Variables Level 1st difference I(d) Variables Level 1st Difference I(d) 

INT -2.979575a** -6.027197c* I(0) LNOILY -1.068062b -3.850615a* I(1) 

LCON -2.777769b -3.065783c* I(1) LOILM -1.579322a -7.106029a* I(1) 
LCPI -1.670254a -2.706235a*** I(1) LOILP -2.104341b -6.089483c* I(1) 

LDPRO -1.103451b -5.763333c* I(1) LOILX -0.967106a -6.246777a* I(1) 
LEXR -2.319449a -4.945028b* I(1) LOPEC -3.450675a** -6.566612c* I(0) 

LGEXP -0.960402a -4.349583b* I(1) LREM -1.967389a -5.645840c* I(1) 
LGFCF -2.994514b -2.953288c* I(1) LRGDP -1.873552b -4.247826a* I(1) 
LHCAP -1.842259b -6.693017b* I(1) LRR -2.056679b -4.758919a* I(1) 
LLABF -1.760340b -5.093305a* I(1) LTAR -4.599778b* -7.910454c* I(0) 

LM2 -2.909531b -3.730620a* I(1) LTROP -1.390444b -6.572491a* I(1) 

LNOILM -2.255197b -7.094259a* I(1) LUSGDP -3.028256a** -5.371590b* I(0) 
LNOILX -4.245504b** -7.130983a* I(0)     

Source: Author‟s computation using eviews9. 
Note: *, ** and *** implies statistical significance at 1 percent, 5 percent and 10 percent respectively. 
a, b and c implies model with intercept, trend and intercept and none respectively. 
I(0) and I(1) implies that the time series is stationary at level and first difference respectively. 

 

4.2.2. Autoregressive Distributed Lag (ARDL) Bounds Test Approach to Cointegration 
Sequel to the result of the unit root test, cointegration test is carried out using ARDL Bounds Test approach to 

cointegration. The choice of this approach is premised on the fact that our variables are not integrated of the same 
order, thus negating the use of Engle-granger and Johansen Cointegration test approach. Pesaran and Shin (1999) 
and Pesaran et al. (2001) developed the ARDL cointegration approach which has three major advantages over other 
traditional cointegration approaches. Firstly, ARDL does not require that all the variables under study have the 
same order o integration; it can be used if the series are I(0) or I(1) or both. Secondly, it is relatively more efficient 
using small sample sizes. Thirdly, unbiased estimates of long-run model are obtained using ARDL method (Harris 
and Sollis, 2003). 

Cointegration test is carried out to determine the existence of a long-run relationship between the dependent 
and explanatory variables. The rule of ARDL Bounds test of cointegration states that the null hypothesis be 
rejected if the value of the computed F-statistic is greater than the upper bounds value and accepted if the F-
statistic is less than the lower bounds value. The ARDL cointegration test will be said to be inconclusive should 
the computed F-statistic falls between the lower and upper bound.  

The result of ARDL bound test is presented in Table 2. The result shows that the null hypothesis for CPI 
model, consumption model, GFCF model and non-oil imports model should be rejected since the value of their 
computed F-statistic is greater than the upper bound critical value at 1 percent level of significance and at 10 



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percent level of significance for the interest rate model. This implies that there is a long-run relationship among 
the endogenous variables and their respective explanatory variables. However, real GDP model, exchange rate 
model, oil export model, non-oil export model and oil import model are found not to be cointegrated because the 
values of their computed F-statistic are less than 5 percent, 1 percent, 5 percent, 10 percent and 10 percent level of 
significance respectively. We will proceed to estimating the ARDL error correction model (short run) and their 
respective long run models for each of the models. 
 

Table-2. Results of ARDL bounds test approach to cointegration. 

Endogenous variables Significance Lower 
bound 

Upper 
bound 

Computed 
F-statistic 

Cointegration 
status 

 
LRGDP 

10% 2.45 3.52  
2.49 

Not cointegrated 
5% 2.86 4.01 
1% 3.74 5.06 

 
INT 

10% 2.26 3.35  
3.39 

 
Cointegrated 5% 2.62 3.79 

1% 3.41 4.68 
 

LCPI 
10% 2.26 3.35  

5.58 
 

Cointegrated 5% 2.62 3.79 
1% 3.41 4.68 

 
LEXR 

10% 2.45 3.52  
3.38 

Not cointegrated 
5% 2.86 4.01 
1% 3.74 5.06 

 
LCON 

10% 2.45 3.52  
6.61 

 
Cointegrated 5% 2.86 4.01 

1% 3.74 5.06 
 

LGFCF 
10% 2.45 3.52  

7.73 
 

Cointegrated 5% 2.86 4.01 
1% 3.74 5.06 

 
LOILX 

10% 2.45 3.52  
2.72 

Not cointegrated 
5% 2.86 4.01 
1% 3.74 5.06 

 
LNOILX 

10% 3.17 4.14  
2.69 

Not cointegrated 
5% 3.79 4.85 
1% 5.15 6.36 

 
LOILM 

10% 2.72 3.77  
2.32 

Not cointegrated 
5% 3.23 4.35 
1% 4.29 5.61 

 
LNOILM 

10% 2.45 3.52  
5.07 

 
Cointegrated 5% 2.86 4.01 

1% 3.74 5.06 
                    Source: Author‟s computation using eviews9. 
 

4.3. Presentation and Interpretation of Results 
The results from the estimation of the autoregressive distribution lag (ARDL) model of each of the endogenous 

variables are presented and interpreted below. The optimal lag lengths for the selected ARDL Error Correction 
representation of each model are determined by the Schwarz Criterion (SC). The results of the long run coefficient 
and error correction representation of the selected ARDL model for individual equation are presented in Table 3. 
 

4.3.1. Aggregate Output 
The result of the estimation of aggregate output (real GDP) equation is presented in Table 4. The result 

reveals that the estimated error correction coefficient is negative and significant at 5 per cent level of significance 
and shows that approximately 32 percent of disequilibrium from the previous year‟s shock of the explanatory 
variables converges back to the long-run equilibrium in the current year. More precisely, the result shows that a 
one percent increase in human capital and gross fixed capital formation will bring about approximately 0.06 
percent and 0.07 percent increase and decrease in aggregate output in the short run respectively. Also, a one 
percent increase in labour force will lead to 0.11 percent fall in aggregate output which implies that marginal 
product of labour fall as output increases in Nigeria. This result is plausible because the service sector which is 
capital-intensive is the booming sector of the economy and in recent times, contributes immensely to the Nigeria‟s 
aggregate output. Furthermore, a one percent depreciation of the exchange rate will decrease aggregate output by 
0.05 percent in the short-run. On the other hand, whereas human capital, gross fixed capital formation and 
exchange rate have a positive relationship with aggregate output in the long-run, labour force is inversely related 
to aggregate output. However, only human capital was found to be significant in explaining changes in aggregate 
output both in the short-run and the long-run. Put differently, human capital is a determinant of aggregate output 
in Nigeria both in the short-run and long-run. The result also shows that the model explains about 99 percent of 
the variation in aggregate output. Interestingly, the long run impacts of each of the explanatory variables on 
aggregate output exceed their short-run impacts. 

 

4.3.2. Interest Rate (Monetary Policy Rate) 
The result of the estimated interest rate short-run equation shows that interest rate will fall by 6.5 percent if 

money supply increases by one percent indicating that interest rate and money supply are inversely related. This 
result is plausible and it supports economic theory which posits an inverse relationship between money supply and 
interest rate. Also, a one percent increase in price level and reserve requirement will lead to approximately 1.06 
percent and 1.67 percent increase in interest rate; a one percent increase in real GDP will raise interest rate by 



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approximately 7.22 percent; and a one percent depreciation of exchange rate will result in about 4.66 percent fall in 
interest rate. This implies that the interest rate is very sensitive to changes in the explanatory variables of the 
model. However, only exchange rate and money supply are significant in influencing interest rate in Nigeria while 
other variables of the model are not significant in the short-run. The result also shows that about 59 percent of the 
variation in interest rate is explained by the explanatory variables of the model. The coefficient of the error 
correction term is very high indicating a high speed of adjustment to equilibrium following short-run shocks that 
is, about 90 percent of the disequilibrium, caused by previous period shocks converges in the long-run. On the 
other hand, the long run results show that exchange rate has a positive and significant relationship with interest 
rate such that interest rate increases by 5.2 percent if exchange rate depreciates (increases) by one percent. Also, 
money supply has a negative but significant relationship with interest rate in the long-run such that interest rate 
falls by 7.26 percent if money supply increases by one percent. In addition, consumer price index, reserve 
requirement real GDP and exchange rate have a positive relationship with interest rate while only money supply is 
inversely related to interest rate in the long run in Nigeria. However, only money supply and exchange rate are 
determinants of interest rate in the long run in Nigeria. Interestingly, the long run impacts of each of the 
explanatory variables on interest rate exceed their short-run impacts.   
 

4.3.3. Consumer Price Index (CPI) 
The result in Table 3 also shows that the first-period lag of CPI has a positive relationship with CPI such that 

a one percent increase in its lag will bring about, on the average, 0.56 percent increase in the present price level. 
This indicates that CPI in Nigeria follows adaptive expectation in that the previous value of CPI predicts its 
present value. In addition, the result shows that a one percent depreciation of exchange rate will bring about 
approximately 0.1 percent fall in price level; a one percent increase in real GDP will bring about 0.45 percent 
decrease in price level; and a one percent change in money supply and government expenditure will bring about 
approximately 0.18 percent and 0.15 percent change in price level respectively. However, among all the 
explanatory variables, only real GDP, lag of interest rate and lag of CPI are found to be significant in determining 
CPI in Nigeria in the short-run. In sum, whereas first period lag of CPI, interest rate, money supply and 
government expenditure have a positive relationship with CPI in the short-run, first period lag of interest rate, 
exchange rate and real GDP are inversely related to CPI in Nigeria. Furthermore, the result shows that the model 
explains about 99 percent of the variation in the price level. The speed of adjustment of the explanatory variables to 
long-run equilibrium is about 46 percent. However, the long run coefficients result reveals that the estimated 
coefficients of real GDP and government expenditure are significant in determining price level. It shows that in the 
long run, a one percent increase in real GDP will lead to 0.98 percent fall in CPI and a one percent increase in 
government expenditure will lead to approximately 0.81 percent increase in CPI. Also, an increase in interest rate 
and money supply by one percent will lead to 0.03 and 0.39 percent increase in CPI respectively. In sum, interest 
rate money supply and government expenditure have positive impacts on CPI while exchange rate and real GDP 
are inversely related to CPI in the long-run.  
 

4.3.4. Exchange Rate 
Table 3 also shows that the coefficient of the error correction term in the estimated exchange rate equation is 

negative and significant. It reveals that the speed of adjustment of the model to its long run equilibrium is about 42 
percent. The result also shows that, in the short-run, a percent increase in interest rate, money supply and oil price 
will lead to approximately 0.02 percent, 0.58 percent and 0.07 percent depreciation (increase) in exchange rate 
respectively. Also, exchange rate will depreciate by 0.18 percent if trade openness increases by one percent. 
However, it is found that, of all the explanatory variables of the model, only money supply is significant in 
explaining exchange rate movement in Nigeria in the short-run. The positive relationship between oil price and 
exchange rate in Nigeria is plausible because the price of crude-oil is quoted in US dollar and the effect of the 
increase in price on the naira is not direct as the increase in price of oil only increases Nigeria‟s foreign exchange 
earnings. The coefficient of the Adjusted R-square shows that about 98 percent of the variation in exchange rate is 
explained by the trade openness, interest rate, money supply and oil price. However, in the long run, trade 
openness and oil price have a negative impact on exchange rate while interest rate and money supply have positive 
influence on exchange rate in Nigeria. Nonetheless, only money supply and oil price are significant determinants of 
exchange rate in Nigeria in the long-run. In sum, trade openness has an inverse relationship with exchange rate 
both in the short-run and long-run; interest rate and money supply have a direct relationship with exchange rate 
both in the short-run and long-run; and oil price is positively related to exchange rate in the short-run but 
inversely related to it in the long-run. This suggests that if oil price changes persist, it will transit from having a 
positive impact on exchange rate to have a negative impact.  
 

4.3.5. Consumption  
The result of the estimated consumption equation in Table 3 shows that there is a positive relationship 

between real GDP and consumption such that a one percent rise in real GDP will bring about approximately 0.18 
percent increase in consumption. This result parallels the theory of the absolute income which states that 
consumption is a function of income (real GDP in our case). Also, a one percent increase in price level will lead to 
about 0.37 percent fall in consumption indicating that inflation reduces the purchasing power of consumers thereby 
reducing their consumption; a one percent depreciation in exchange rate will bring about 0.1 percent fall in 
consumption; and a one percent increase in remittance will lead to a 0.02 percent increase in consumption. In 
addition, a one percent increase in the lag value of both consumption and exchange rate will lead to 0.39 percent 
and 0.27 percent decline in consumption. However, of the six explanatory variables of the consumption model, only 
the lag value of both consumption and exchange rate are significant in explaining changes in consumption. The 
adjusted R-squared value shows that about 97 percent of the variation in consumption is explained by GDP, CPI, 
exchange rate and remittance and the Durbin-Watson autocorrelation coefficient of 1.97 shows the absence of 
autocorrelation among the explanatory variables. The speed of adjustment of the model to its long-run equilibrium 



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state is about 42 percent. However, in the long run, real GDP, exchange rate and remittance have a positive but 
not significant relationship with consumption while CPI has a negative but insignificant relationship with 
consumption in Nigeria. This implies that only exchange rate is a determinant of consumption in the long-run in 
Nigeria. Interestingly, the long run impacts of each of the explanatory variables on consumption exceed their 
short-run impacts.   
 

4.3.6. Gross Fixed Capital Formation (GFCF) 
The result of the short-run estimation shows that only the lag of interest rate and real GDP are statistically 

significant to influence GFCF such that a one percent increase in lag of interest rate and real GDP will bring about 
0.002 percent and 0.7 percent increase in GFCF. Similarly, interest rate has an insignificant positive effect on 
GFCF such that an increase in interest rate by one percent raises investment by 0.002 percent. This result is 
against theoretical postulation which posits an inverse relationship between interest rate and investment (gross 
fixed capital formation). Also, a one percent increase in CPI and one percent depreciation in exchange rate will 
bring about 0.01 percent and 0.02 percent fall in GFCF indicating that they are both inversely related to GFCF. In 
sum, in the short-run, interest rate and its first period lag as well as real GDP are positively related to GFCF while 
exchange rate and CPI are inversely related to GFCF. Furthermore, the coefficient of the error correction term 
indicates that approximately 51 percent of disequilibrium from the previous year‟s shock of the independent 
variables converges back to the long-run equilibrium in the current year. Furthermore, it is apparent that about 93 
percent of the variation in GFCF is explained by the explanatory variables. In the long run, however, GFCF will 
fall by 0.01 percent, 0.03 percent and 0.02 percent if interest rate, exchange rate and CPI increase by one percent 
respectively. Also, real GDP has a positive and significant relationship with GFCF in the long run such that GFCF 
will increase by 1.37 percent if real GDP increases by one percent. Hence, only real GDP is a driver of investment 
in the long-run in Nigeria. As is the case with previous equations, the long-run impacts of the explanatory 
variables (interest rate, real GDP, exchange rate and CPI) of the GFCF equation exceed their impacts in the short-
run.   
 

4.3.7. Oil Export 
The result of the oil export equation shows that a one percent increase in oil price, OPEC quota and exchange 

rate will bring about approximately 0.75 percent, 0.02 percent and 0.16 percent increase in oil export respectively. 
These results are in line with a priori expectation as exchange rate depreciation will make exports cheaper thus, 
increasing output as well as the volume of oil exports. Also, OPEC gives quota for exports to its member nations 
including Nigeria; an increase in this quota means an increase in the volume of export while an increase in oil price 
will encourage producers and exporters to increase the volume of both production and export thereby, increasing 
their revenue. Furthermore, a one percent increase in United States‟ GDP, which implies an increase in US national 
income, will lead to approximately 1.81 percent increase in oil export. This result implies that oil export responds 
to changes in the explanatory variables of this model. This result is plausible in that a change in the national 
income the United States, which is the major importer of Nigerian crude-oil, will greatly affect the volume of 
Nigeria‟s oil export and revenue. This was evident during the recession in the US in 2008 when Nigeria was also 
badly hit by the recession which originated in the United States. However, only oil price is significant in explaining 
the changes in oil export in Nigeria in the short-run. On the other hand, oil price and OPEC quota have negative 
impacts on oil exports while US GDP and exchange rate have positive effects on oil export in Nigeria in the long-
run. However, none of these variables is a determinant of oil export in the long-run. Furthermore, the model 
explains about 99 percent variation in the volume oil export in Nigeria and the coefficient of the Durbin-Watson 
(1.86) indicates the absence of autocorrelation in the model. Interestingly, the long run impacts of each of the 
explanatory variables on oil exports exceed their short-run impacts.   
 
4.3.8. Non-Oil Export  

Non-oil output and exchange rate are incorporated into Nigeria‟s non-oil export equation. The result shows 
that only non-oil output is significant in explaining the movement in non-oil export Nigeria in the short-run while 
both non-oil output and exchange rate drive non-oil export in the long-run. Also, the result shows that in the short 
run, a one percent change in non-oil output will bring about approximately 1.27 percent change in non-oil export 
while non-oil exports will fall by about 0.09 percent when exchange rate depreciates (increases) by one percent. 
The result implies that non-oil export gives a sharp response to shocks in non-oil export in Nigeria. In the long 
run, however, non-oil exports have a positive relationship with exchange rate such that non-oil exports will 
increase by approximately 0.7 percent when exchange rate appreciates by one percent. This implies that the impact 
of exchange rate on non-oil exports moves from positive to negative as time progresses. Furthermore, the model 
explains about 98 percent variation in the volume non-oil export in Nigeria and the Durbin-Watson coefficient 
reveals that there is no serial correlation in the model. The coefficient of the error correction term reveals that 
approximately 49 percent of disequilibrium from the previous year‟s shock of the independent variables converges 
back to the long-run equilibrium in the current year. Interestingly, the long run impacts of each of the explanatory 
variables on non-oil exports exceed their short-run impacts.   
 

4.3.9. Oil Import  
The result of the estimated oil import equation shows that exchange rate and oil price are positively related to 

oil imports such that one percent exchange rate depreciation and one percent decline in crude-oil price will result in 
approximately 0.78 percent and 0.01 percent increase in oil-import respectively. Also, a one percent increase in 
domestic crude-oil production will lead to about 0.17 percent fall in oil-import. This result is plausible in that an 
increase domestic production should reduce the imports of the same product although this implies that the 
domestic production of crude-oil products is not enough to meet the energy demand of the teeming population of 
the Nigerian economy. However, only exchange rate is found to be significant in influencing oil import in Nigeria 
both in the short and long run. In addition, about 96 percent of the variation in oil import is explained by the 



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explanatory variables of the model. In sum, domestic production of crude-oil is inversely related to imports both in 
the short-run and the long-run while exchange rate and oil price are positively related to oil import in Nigeria both 
in the short-run and the long-run. The coefficient of the error correction term implies that that approximately 48 
percent of disequilibrium from the previous year‟s shock of the independent variables converges back to the long-
run equilibrium in the current year. The coefficient of the Durbin-Watson (2.19) indicates the absence of serial 
correlation in the model. Interestingly, the long run impacts of each of the explanatory variables on oil imports 
exceed their short-run impacts. 
 

Table-3. Results of short-run and long-run coefficients of selected ARDL models. 

Regressors Short-run 
coefficients 

Long-run 
coefficients 

Regressors Short-run 
coefficients 

Long-run 
coefficients 

Aggregate output equation Interest rate equation 
C  27.943437 C  -212.010636 

D(LHCAP) 0.059001** 0.187105** D(LM2) -6.501138** -7.255029** 

D(LGFCF) 0.067482 0.214002 D(LCPI) 1.058286 1.181008 
D(LLABF) -0.113243 -0.359121 D(LRR) 1.674527 0.340235 
D(LEXR) -0.011596 0.219883 D(LRR(-1)) 2.214642***  

D(LEXR(-1)) -0.053117  D(LRGDP) 7.217633 8.054611 
ECM(-1) -0.315333**  D(LEXR) 4.658220* 5.198401* 

Adjusted R-squared 0.9867  ECM(-1) -0.896087*  
Durbin-Watson 1.88  Adjusted R-squared 0.5893  

   Durbin-Watson 2.17  
      

CPI equation Non-oil import equation 
C  26.238690** C  -2.617710 

D(LCPI(-1)) 0.564784*  D(LNOILM(-1)) -0.357448*  
D(INT) 0.003747 0.030354 D(LNOILY) -1.875787 1.235078 

D(INT(-1)) -0.018297*  D(LNOILY(-1)) 1.913407  
D(LEXR) -0.102748 -0.225096*** D(LTAR) 0.296322** 1.541259 
D(LM2) 0.177042 0.387854 D(LEXR) -0.125161 0.494570 

D(LRGDP) -0.447983** -0.981416** D(INT) -0.017603*** -0.187511*** 
D(LGEXP) 0.145423 0.811245* D(INT(-1)) -0.036215**  

ECM(-1) -0.456466*  ECM(-1) -0.192260**  
Adjusted R-squared 0.9981  Adjusted R-squared 0.9940  

Durbin-Watson 1.79  Durbin-Watson 2.62  
      

Consumption equation GFCF equation 
C  -8.627151 C  -19.300394*** 

D(LCON(-1)) -0.388340**  D(INT) 0.001948 -0.006785 
D(LRGDP) 0.182747 0.434877 D(INT(-1)) 0.024696**  

D(LCPI) -0.366074** -0.232034 D(LRGDP) 0.699998* 1.369061* 
D(LEXR) -0.099164 0.405210*** D(LEXR) -0.019461 -0.038061 

D(LEXR(-1)) -0.271237*  D(LCPI) -0.009047 -0.017694 
D(LREM) 0.015245 0.036279 ECM(-1) -0.511298*  
ECM(-1) -0.420227**  Adjusted R-squared 0.9255  

Adjusted R-squared 0.9730  Durbin-Watson 1.69  

Durbin-Watson 1.97     
      

Oil export equation Exchange rate equation 
C  -163.931707 C  -0.518808 

D(LOILP) 0.753157* -0.058075 D(LTROP) -0.181612 -0.436298 
D(LOPEC) 0.016205 0.050296 D(INT) 0.025115 0.060336 

D(LUSGDP) 1.808769 5.614029 D(LM2) 0.579170* 1.391373** 
D(LEXR) 0.158039 0.490519 D(LOILP) 0.069543 -1.530567** 
ECM(-1) -0.322187*  ECM(-1) -0.416257*  

Adjusted R-squared 0.9865  Adjusted R-squared 0.9825  
Durbin-Watson 1.86  Durbin-Watson 2.01  

      

Oil import equation Non-oil export equation 
C  3.496850 C  -13.850723* 

D(LDPRO) -0.173225 -0.361106 D(LNOILY) 1.274462*** 2.602096* 
D(LEXR) 0.777420* 1.620612* D(LEXR) -0.094395 0.703143* 
D(LOILP) 0.009267 0.019318 ECM(-1) -0.489783*  
ECM(-1) -0.479708*  Adjusted R-squared 0.9750  

Adjusted R-squared 0.9631  Durbin-Watson 1.83  
Durbin-Watson 2.19     

           *, ** and *** implies significance at 1%, 5% and 10% respectively. 
            Source: Computed by author using eviews9. 

 

4.3.10. Non-Oil Import 
The result of the estimated non-oil import reveals that all the explanatory variables, except non-oil output and 

exchange rate, are significant in determining changes in non-oil imports in Nigeria in the short-run. It is apparent 
from the result that non-oil imports in Nigeria follows adaptive expectation in that the previous value of non-oil 
imports predicts its present value as depicted by the coefficient of the non-oil imports and its corresponding 
probability value. The result shows that in the short-run, a one percent increase in the lag of non-oil import, non-



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oil output, exchange rate, interest rate, and the previous value of interest rate will result in approximately 0.36 
percent, 1.88 percent, 0.13 percent, 0.02 percent and 0.04 percent decline in non-oil imports. Also, a one percent 
increase in lag of non-oil output and tariff will lead, on the average, to about 1.91 percent and 0.3 percent increase 
in non-oil imports. In addition, about 99 percent of the variation in oil import is explained by the explanatory 
variables of the model. In the long run, non-oil output, tariff and exchange rate are found to have positive 
relationship with non-oil import while interest rate has an inverse relationship with non-oil imports. Moreover, 
only interest rate significantly influence non-oil imports in Nigeria in the long run. Interestingly, the long run 
impacts of each of the explanatory variables on non-oil imports exceed their short-run impacts. 

 
4.4. Model Forecast Evaluation and Simulation 

The primary purpose of this macroeconometric model is to explain the relationships between major 
macroeconomic variables, forecast and simulate future time paths of selected economic variables. The predictive 
accuracy of the model is crucial because it shows the closeness of the solution values of each equation in the models 
to the time paths of their actual values. The model is evaluated for both within-sample and out-of-sample predictive 
performance and the results are presented below. 
 

4.4.1. Within-Sample Performance 
Time series data running from 1981 to 2014 is used to generate a static solution for the model. The actual 

values are plotted against the static simulation values for the endogenous variables in Figure 1. The figure shows 
that the predicted series are very close to actual series except for gross fixed capital formation (GFCF) which has 
few gaps between actual and predicted series. However, the closeness of the predicted series to the actual series 
indicates a good forecasting power of the model thus, suggesting that the simulation result will be valid for policy 
prescriptions. 
 

5

10

15

20

25

30

1985 1990 1995 2000 2005 2010

Actual INT (Baseline)

INT

4.5

5.0

5.5

6.0

6.5

7.0

1985 1990 1995 2000 2005 2010

Actual LCON (Baseline)

LCON

-2

0

2

4

6

1985 1990 1995 2000 2005 2010

Actual LCPI (Baseline)

LCPI

-2

0

2

4

6

1985 1990 1995 2000 2005 2010

Actual LEXR (Baseline)

LEXR

21.5

22.0

22.5

23.0

23.5

24.0

1985 1990 1995 2000 2005 2010

Actual LGFCF (Baseline)

LGFCF

0

2

4

6

8

10

1985 1990 1995 2000 2005 2010

Actual LNOILM (Baseline)

LNOILM



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-2

0

2

4

6

8

1985 1990 1995 2000 2005 2010

Actual LNOILX (Baseline)

LNOILX

-4

0

4

8

12

1985 1990 1995 2000 2005 2010

Actual LOILM (Baseline)

LOILM

0

2

4

6

8

10

1985 1990 1995 2000 2005 2010

Actual LOILX (Baseline)

LOILX

30.0

30.4

30.8

31.2

31.6

32.0

1985 1990 1995 2000 2005 2010

Actual LRGDP (Baseline)

LRGDP

 
Figure-1. Actual and simulated values of the endogenous variables. 

                         Source: Authors‟ computation from eviews9. 

 

4.4.2. Out-of-Sample Performance 
Time series data spanning the period between 1981 and 2014 are estimated to generate static solution of the 

model and one-step ahead out-of-sample predictions were made. The focus of the out-of sample forecast is to 
compare the forecast figure of each of the endogenous variables with their actual figures. This will help ascertain 
the accurate predictive performance of our model. The statistics used to evaluate the predictive performance of a 
model are mean absolute errors (MAE), root mean square errors (RMSE) and Theil inequality coefficient. Table 4 
presents these statistics for all the endogenous variables and it shows that the errors are considerably small 
indicating that the model predicts historical data well.  
 

Table-4. Prediction statistics of the macroeconometric model. 

Endogenous variables Mean absolute error 
(MAE) 

Root mean absolute error 
(RMSE) 

Theil inequality 
coefficient 

Real GDP 0.061 0.071 0.001 
Consumer price index 1.891 2.542 0.094 

Interest rate 0.144 0.173 0.027 
Exchange rate 0.293 0.338 0.046 

Consumption 0.103 0.124 0.011 
Gross fixed capital formation 0.182 0.241 0.005 

Oil export 0.298 0.427 0.031 
Non-oil export 0.369 0.455 0.057 

Oil import 0.482 0.682 0.066 
Non-import 0.300 0.435 0.031 

            Source: Computed from eviews9. 

 

4.5. Analysis of Impact of Shocks on the Endogenous Variables 
The model checked for the impact shocks have on the endogenous variables using stochastic simulation 

exercises. Given the above tests and the level of satisfactory performance observed in many of the variables and 
equations, simulation on possible outcomes of changes in selected variables (government expenditure, money 
supply and oil price) are provided. The process involves introducing shocks to selected policy variables and tracing 
their impacts given the relationships in the model. The aim is to examine what will happen to the endogenous 
variables if a particular policy instrument is altered. This study looks at the impact of three sets of shocks:  

Scenario 1: A 10 percent increase in government expenditure. 
Scenario 2: A 10 percent depreciation of exchange rate. 
Scenario 3: A fall in oil price in the international market by 10 percent. 

 

4.5.1. Simulation Results  
There are three policy variables used for the simulation: government expenditure, exchange rate and crude oil 

price. The actual figures of these policy variables for 2015 and 2016 are inserted into the model and the result of 
the simulation is presented in Table 5. 

 
Scenarios 1: A 10 percent Increase in Government Expenditure 

Scenario 1, which depicts the impact of an increase in government expenditure by 10 per cent on the model of 
the Nigerian economy, shows that real GDP will increase by 3 percent consecutively in 2017 and 2018 



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respectively; this shows that there is a positive relationship between government expenditure and real GDP. The 
results of this scenario also show that the shock will make consumption increase by 4 percent consecutively in 2017 
and 2018 respectively. Furthermore, the result revealed that exchange rate is expected to depreciate by 16 percent 
and 14 percent in the immediate future if the economy is hit by a positive government expenditure shock. Also, 
there will be a surge in the general price level as CPI will increase as well as gross fixed capital formation while 
interest rate will nosedive in 2017 and 2018. Furthermore, as a result of the 10 percent positive shock in 
government expenditure, oil export, non-oil exports, oil imports and non-oil imports is expected to increase in 
2017 and 2018 respectively. This confirms the positive relationship between government expenditure and each of 
the other endogenous variables (real GDP, consumption, exchange rate, CPI, oil exports, non-oil exports, oil 
imports and non-oil imports). In sum, the effects of an increase in government expenditure by 10 percent is that 
real GDP, consumption, price level (CPI), oil exports, non-oil exports, oil imports and non-oil imports will increase 
while exchange rate will depreciate in the following years. 

 

Scenarios 2: A 10 percent Fall in Oil Price in the International Market 
Table 5 shows that a 10 percent decrease in the price of crude-oil in the international market will result in an 

increase in real GDP by 3 percent in 2017 and 2 percent in 2018. The oil price shock is expected to lead to a 
depreciation of exchange rate by 3 percent and 23 percent in 2017 and 2018 respectively. Also, interest rate is 
expected to rise by 7 percent and 36 percent and gross fixed capital formation will fall by 1 percent consecutively in 
2017 and 2018. Total export (oil and non-oil) and total imports (oil and non-oil) are expected to increase in 2017 
and 2018 if oil price falls by 10 percent. This result is plausible because the recent incessant fall in oil price propels 
Nigeria to export more crude-oil to increase her revenue since the proceeds from this product is the major source of 
revenue to the government. Furthermore, the oil price shock will lead to an increase in consumer price index 11 
percent and 8 percent in 2017 and 2018 respectively. Summarily, the impact of a 10 percent fall in oil price in the 
international market is that whereas real GDP, interest rate, oil and non-oil exports and imports will increase, 
exchange rate will depreciate and gross fixed capital formation will fall in subsequent years.  
 

Scenario 3: A 10 percent Depreciation of Exchange Rate 
The result of introducing a shock of a depreciation of exchange rate by 10 percent would lead to a rise in real 

GDP by 4 percent in 2017 and 3 percent in 2018; an increase in consumer price index (CPI) by 6 percent in 2015 
and 11 percent 2018. The depreciation of exchange rate will bring about an increase in interest rate and also an 
increase in gross fixed capital formation in 2017 and 2018. The shock to exchange rate will lead to increases in the 
values of oil export, non-export, oil import and non-oil imports in the years under review. Furthermore, the 
exchange rate shock will lead to an increase in consumption by 4 percent in 2017 and 3 percent in 2018. 
Summarily, the impact of a10 percent exchange rate depreciation on the Nigerian economy is that real GDP, price 
level (CPI), interest rate, gross fixed capital formation, consumption, oil export, non-export, oil import and non-oil 
imports will all increase although with different magnitude. This implies that exchange rate depreciation will have 
diverse effects on the Nigerian economy. However, depending on the policy objective of the government and 
monetary authorities, this policy stance can be used albeit it has its inherent trade-offs.  
 

Table-5. Policy scenarios. 

Endogenous 
variables 

Year Scenario 
1 

Scenario 
2 

Scenario 
3 

Endogenous 
variables 

Year Scenario 
1 

Scenario 
2 

Scenario 
3 

 
Real  GDP 

2016 31.94 31.94 31.94 Gross fixed 
capital 

formation 

2016 23.84 23.84 23.84 

2017 31.97 31.97 31.98 2017 23.86 23.83 23.88 

2018 32.01 32.00 32.01 2018 23.89 23.83 23.91 
 

Consumer 
price index 

2016 4.85 4.85 4.85  
Oil export 

2016 10.22 10.22 10.22 

2017 5.00 4.96 4.91 2017 10.38 10.43 10.33 
2018 5.10 5.07 4.99 2018 10.53 10.60 10.48 

 
Interest rate 

2016 13.45 13.45 13.45  
Non-oil 
export 

2016 8.58 8.58 8.58 
2017 13.43 14.15 13.94 2017 8.99 9.08 8.95 

2018 13.37 14.51 14.42 2018 9.42 9.56 9.37 
 

Exchange 
rate 

2016 6.13 6.13 6.13  
Non-oil 
export 

2016 9.36 9.36 9.36 

2017 6.29 6.43 6.28 2017 9.54 9.75 9.42 
2018 6.43 6.66 6.43 2018 9.73 10.06 9.57 

 
Consumption 

2016 6.87 6.87 6.87  
Non-oil 
import 

2016 10.38 10.38 10.38 
2017 6.91 6.89 6.91 2017 10.71 10.80 10.64 

2018 6.95 6.92 6.96 2018 11.03 11.17 10.92 

         Source: Author‟s computation using eviews 9. 

 
5. Conclusion and Policy Recommendations 

This study presents a small macroeconometric model of Nigeria. The model is estimated and simulated to 
describe time paths of the endogenous variables of the system of equations specified in the study. The results shows 
that a positive shock in government expenditure raises aggregate national output, total exports (oil and non-oil), 
total import (oil and non-oil), gross fixed capital formation, exchange rate, consumption, and inflation rate while 
interest rate falls; a negative shock (depreciation) to exchange rate has a negative effect on gross fixed capital 
formation and a positive effect on aggregate national output, consumer price level, interest rate, consumption, total 
export and total imports; and a negative shock in oil prices results in an increase in total imports, total exports, 
consumption, exchange rate, gross fixed capital formation and aggregate national output. 

The study reveals that exchange rate plays a vital role in determining the behaviour of almost all the 
endogenous variables in the model thus recommends that the monetary authorities should ensure stability in 
exchange rate by employing a managed-floating exchange rate regime as a fixed exchange rate regime will make 
her lose control of her monetary policy and floating exchange rate regime subject the value the naira to market 
forces which is not in Nigeria‟s favour as a result of the import-dependent of the country. Also, the government 



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should also provide enabling and conducive environment where investment can thrive and gear efforts towards 
formulating and implementing policies to diversify the economy to prevent it from the shocks that result from oil 
price volatility in the international market. 
 

References 
Ajao, M.G. and O.E. Igbekoyi, 2013. The determinants of real exchange rate volatility in Nigeria. Academic Journal of Interdisciplinary 

Studies, 2(1): 459-471. 
Akanbi, O.A. and C.B. Du Toit, 2011. Macro-econometric modelling for the Nigerian economy: A growth–poverty gap analysis. Economic 

Modelling, 28(1-2): 335-350.Available at: https://doi.org/10.1016/j.econmod.2010.08.015. 
Anthony, E.A. and O.O. Babatunde, 2012. The determinants of interest rate spreads in Nigeria: An empirical investigation. Modern 

Economy, 3(7): 837-845.Available at: https://doi.org/10.4236/me.2012.37107. 
Asteriou, D., D.A. Lalountas and C. Siriopoulos, 2011. A small macro-econometric model for Greece: Implications about the sustainability of 

the Greek external debt. Available from https://ssrn.com/abstract=1884905 or http://dx.doi.org/10.2139/ssrn.1884905. 
Ben, U.O., 2011. The price of oil and exchange rate determination in Nigeria. International Journal of Humanities and Social Science, 1(21): 

232-239. 
CBN, 2014. Statistical bulletin. Available from https://www.cbn.gov.ng/documents/Statbulletin.asp. 
Central Bank of Nigeria (CBN), 2010. Macroeconometric model of the Nigerian economy. Available from 

https://www.cbn.gov.ng/out/2015/rsd/macroeconometric%20model%20of%20the%20nigerian%20economy.pdf. 
Duruechi, A.H. and J.N. Ojiegbe, 2015. Determinants of investments in the Nigerian economy: An empirical approach. International Journal 

of Financial Research, 6(4): 217-227. 
Egwaikhide, I.C., E. Anthony and S. Zakaree, 2012. Counter-factual analysis of the Nigerian economy: A test of the relative potency of 

monetary and fiscal policies. International Journal of Business, Humanities and Technology, 2(4): 111-124. 
Fatukasi, B., 2005. Determinants of inflation in Nigeria: An empirical analysis. International Journal of Humanities and Social Science, 1(18): 

262-271. 
Ferdinand, N., S.S. Kuyeli and K. Rangaza, 2015. The determinants of interest rate spreads in South Africa: A cointegration approach. 

Journal of Economics and Behavioral Studies, 7(2): 101-108. 
Folawewo, A.O. and D. Tennant, 2008. Determinants of interest rate spreads in Sub-Saharan African Countries: A Dynamic Panel Analysis. 

A Paper Prepared for the 13th Annual African Econometrics Society Conference, 9-11 July, 2008, Pretoria, Republic of South 
Africa. 

Friend, I. and P. Taubman, 1964. A short run forecasting model. Review of Economics and Statistics, 46(3): 229-236. 
Gurara, D.Z., 2013. A macroeconometric model for rwanda. African Development Bank Working Paper No. 177. 
Hanif, M.N., Z. Hyder, M.A.K. Lodhi, M.H. Khan and I. Batool, 2011. A small-size macroeconometric model for Pakistan economy. 

Technology and Investment, 2(2): 65-80.Available at: https://doi.org/10.4236/ti.2011.22008. 
Harris, R. and R. Sollis, 2003. Applied time series modelling and forecasting. West Sussex: Wiley. 
John, D. and P. Chris, 2000. A cross-country empirical investigation of the aggregate production function specification. Journal of Economic 

Growth, 5(1): 87-120. 
Khan, B., 2014. A macroeconometric model of food for the Indian economy. Journal of Social Sciences and Public Policy, 6(2): 86-90. 
Nkoro, E. and A.K. Uko, 2018. A small-size macroeconometric model for Nigerian economy. Journal of Statistical and Econometric Methods, 

7(2): 71-90. 
Nwaobi, G., 2011. Agent-based computational economics and african modeling: Perspectives and challenges. Nigeria: Quantitative Economic 

Research Bureau. 
Nworuh, G.E. and C.C. Nwachukwu, 2010. Modeling a dynamic simultaneous macroeconomic system for a developing economy (A Case of 

Nigerian Economy). American Journal of Social and Management Sciences, 1(2): 131-140.Available at: 
https://doi.org/10.5251/ajsms.2010.1.2.131.140. 

Olatunji, G.B., O.A. Omotesho, O.E. Ayinde and K. Ayinde, 2010. Determinants of inflation in Nigeria: A co-integration approach. A Paper 
Presented at the Joint 3rd African Association of Agricultural Economists (AAAE) and 48th Agricultural Economists Association 
of South Africa (AEASA) Conference, Cape Town, South Africa, September 19-23, 2010. 

Olayide, S.O., S.O. Olofin, J.O. Iyaniwura and J.O. Adeniyi, 1981. UIFP econometric model of the Nigerian economy: Some initial result. 
University of Ibadan Forecasting Programme (UIFP-81/001). 

Olofin, S., O. Olubusoye, C. Mordi, A. Salisu, A. Adeleke, S. Orekoya, A. Olowookere and M. Adebiyi, 2014. A small macroeconometric model 
of the Nigerian economy. Economic Modelling, 39: 305-313.Available at: https://doi.org/10.1016/j.econmod.2014.03.003. 

Pesaran, M. and Y. Shin, 1999. An autoregressive distributed lag modeling approach to cointegration analysis, In S. Strom, (Ed), 
Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium, Cambridge University Press, 
Cambridge. 

Pesaran, M.H., Y. Shin and R.J. Smith, 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied 
Econometrics, 16(3): 289-326.Available at: https://doi.org/10.1002/jae.616. 

Saravanan, V., 2015. The determinant of consumer price index in Malaysia. Journal of Economics, Business and Management, 3(12): 1115-
1119. 

Spanos, A. and N. Papadopoulou, 2013. A small macroeconometric model for the cyprus economy. Central Bank of Cyprus Working Paper 
Series, No 02. 

Udah, E.B., 2009. A dynamic macroeconomic model of the Nigerian economy with emphasis on the monetary sector. South African Journal of 
Economic and Management Sciences, 12(1): 28-47.Available at: https://doi.org/10.4102/sajems.v12i1.259. 

Udousung, I., D. John and I. Umoh, 2012. Real exchange rate determinants in Nigeria (1971-2000). Global Journal of Management and 
Business Research, 12(20): 20-26. 

   
 
 
 
 
 
 
 
 
 
 
 
 
 
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