




































Asian Finance & Banking Review; Vol. 2, No. 2; 2018 

ISSN 2576-1161   E-ISSN 2576-1188 

Impact Factor: 3.3  

Published by Centre for Research on Islamic Banking & Finance and Business 

 

 

19 
 

The Impact of Exchange Rate on Foreign Private Investment in Nigeria 

 

 

Leonard Nosa Aisien Ph.D 

Senior Lecturer 

Department of Economics, Banking and Finance 

Faculty of Social and Management Sciences 

Benson Idahosa University 

Benin City, Nigeria 

Email: laisien@biu.edu.en 

 
 

 

 

Received: November 6, 2018      Accepted: November 10, 2018           Online Published: November 17, 2018        

 

Abstract  

The study examined the impact of exchange rate on foreign private investment using quarterly time series date from 

Nigeria for the period 2007 to 2017. Foreign private investment in the study was disaggregated into foreign direct 

investment and foreign portfolio investment in order to ascertain their separate reactions to changes in the exchange 

rate of the naira against the US dollars. The empirical analysis was based on the VAR estimation procedure using 

three lagged periods adopted on the basis of various lag order selection criteria. The empirical result revealed that 

devaluation/depreciation of the naira adversely affects foreign direct investment and foreign portfolio investment in 

Nigeria. Increased in the size of the domestic market and development of the financial sector were found to 

stimulate foreign private investment while high inflation rate in the domestic economy discourages foreign private 

investment in Nigeria. The study, therefore, recommended among others that the Central Bank of Nigeria should 

continue to initiate more proactive policy intervention policies to stabilize the exchange rate of the naira in order to 

stimulate more foreign private investment in Nigeria.    

 

Keywords: Foreign Direct Investment, Foreign Portfolio Investment, Foreign Private investment, Exchange rate, 

Financial Development.  

JEL Classification:  F21, F31, P45  

1. Introduction  

The importance of foreign capital inflows in the economic life of the host country cannot be over emphasized. 

Foreign capital plays a pivotal role in the economies of both developed and developing countries. Foreign capital 

inflows played a major role in the development of currently industrialized countries in their course of economic 

advancement. In the developing countries, foreign capital inflow can contribute significantly to the advancement of 

the host country by helping to fill the savings - investment gap.    

In most countries of Sub-Sahara Africa including Nigeria, the domestic savings fall short of the required investment 

level needed to launch the economies to the path of sustainable development. This creates the problem of savings – 

investment gap. Also, there is the problem of foreign exchange shortage resulting from excessive demand for 

foreign goods occasioned by the weak productive base of these Sub-Sahara Africa countries. In order to close this 

dual gap between savings and foreign exchange, foreign capital inflow becomes very crucial. Hence, the 

government of most developing countries in their policy formulation have accorded stimulation of foreign capital 

inflow top priority.  



 
 

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According to Obadan (2004), external capital flows can be categorized into official development finance, export 

credit, and foreign private capital flows. Foreign private investment is a component of the foreign private capital 

flows. It provides a greater proportion of the needed finance to boost the use of existing capacity and stimulate new 

investment in the host countries. The inflow of foreign private investment helps to boost the stamina of the host 

country towards achieving its economic potentials.  

Since the 1980’s, there has been increased private capital flow across international borders globally. However, 

despite this increased global flow of capital, especially into developing economies, Sub-Sahara Africa countries still 

lag behind other regions in attracting foreign private capital (Osinubi & Amaghionyeodiwe, 2009). According to 

Obadan (2004), the distribution of private capital flow to regions and countries has been highly skewed against Sub-

Sahara Africa countries. From the World Bank (1996), East Asia, Latin America, and the Caribbean dominated the 

inflow of the private capital flow in the 1990’s. East Asia and the Pacific accounted for 43.1% of the total private 

capital inflow, Latin America and the Caribbean 35.6%, Europe, and Central Asia 13.2%, while Sub-Sahara Africa 

accounted for just 2.4%. The Middle East and North Africa accounted for 1.9%.  

This disparity in the geographical distribution of foreign private capital inflow has become a source of worry to the 

authorities of Sub-Sahara Africa countries. This is based on the general assumption that foreign private investment is 

very crucial in stimulating growth in developing economies where domestic capital is grossly inadequate. Foreign 

private investment particularly FDI is not just a source of capital formation, it also serves as a source of 

technological development. Technological development result from the transfer of productive technology, 

innovative capacity, skills development and improvement in organizational and managerial capacities.  

Given the importance of foreign private investment in developing countries, several studies have been conducted on 

the key determinants of foreign private capital inflow in developing countries. From both theoretical and empirical 

studies, several determinants of foreign private capital inflow into a host country have been identified. However, one 

of the key determinants that have been a source of prolonging controversy is the exchange rate. From available 

international economics literature, some empirical findings show that exchange rate volatility impacts positively on 

foreign private capital inflow, while others discover a negative impact.  

However, a close study of available literature in this direction shows that majority of the study employed annual 

time series date which may not adequately capture the volatility of the exchange rate compared to high frequency 

data such a quarterly or monthly data. Most importantly, the majority of the studies only focused their attentions on 

Foreign Direct Investment (FDI) which is just a component of foreign private investment. Foreign portfolio 

investment which is an important part of the foreign private investment is often left out in their analysis. The non-

inclusion of portfolio investment in these studies amount to telling only a part of the story. Although, FDI is a key 

source of technological transfer, however, portfolio investment plays a key role in the economy via the capital and 

money market. Moreover, portfolio investment is more volatile than FDI due to easy transfer. The reaction of FDI 

and portfolio investment to exchange rate volatility may differ significantly. Therefore, to assess the effect of 

exchange rate on foreign private capital inflow, an all embracing analysis covering FDI and foreign portfolio 

investment is necessary. This is the dimension this study wish to address. Therefore the objective of this study is to 

examine the impact of exchange rate on foreign private investment (disaggregated into FDI and foreign portfolio 

investment) using quarterly time series data from Nigeria for the period 2007 to 2017.      

This paper is structured into five sections. Apart from section one which is the introduction, section two deals with 

the review of the relevant literature. Section three covers the theoretical framework and model specification, while 

section four contains the empirical analysis. The paper ends in section five with some policy recommendations and 

concluding remarks.  

2. Literature Review  

2.1 Theoretical Review  

Theoretically, there is a divergence of opinions on the effect of exchange rate on foreign private investment inflow. 

The theoretical literature is examined here under the effect of exchange levels and exchange rate volatility.  

2.1.1 Effect of exchange rate level on Foreign Investment  

The effect of the exchange rate level on foreign investment has been examined by the wealth creation theory and the 

Compa’s model.  



 
 

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i. The Wealth Creation Theory  

The wealth creation theory was advanced by Froot and Stein (1991). This approach claimed that depreciation 

(devaluation) of a host country’s exchange rate attracts foreign investment. This is based on the fact that a 

depreciation (devaluation) of a host country’s currency relative to the investors home country’s currency increases 

the relative wealth of the foreign investor. This increases the attractiveness of the host country to foreign investment 

as the foreign investors are able to acquire assets relatively cheaper in the host country. On the basis of this theory, a 

fall in the value of a country’s currency relative to the home country’s country currency of the investor, all things 

being equal will increase foreign investment in the host country, while an appreciation of the host country’s 

currency will reduce investment.         

ii. The Compa’s Model  

This model was advanced by Compa (1993). The model states that a firm decision to invest in a foreign country 

depends on the expected future profitability of such a venture. In such a case, the more the exchange rate of the host 

country appreciates, the higher will be the expected future profit from investment in that country. Therefore, the 

model predicts that an appreciation of the host country currency will lead to an increase in the inflow of foreign 

investment. This is contrary to the prediction of the wealth crease theory.  

2.1.2 Effect of exchange rate volatility on foreign investment  

There are two broad views on the link between exchange rate volatility and foreign investment. These are the real 

options approach and the risk aversion approach.  

i. The real option theory  

This approach was popularized by Dixit and Pindyck (1994). It considered the effect of exchange rate uncertainty on 

investment, particularly when such investment is irreversible. This theory states that under exchange rate 

uncertainty, a firm has an option to invest oversea or not. This is based on the fact that changes in the exchange rate 

affect the price of the options. Another definition of the option is where a firm has plants in different countries 

which create the options to shift production among facilities in responds to exchange rate movement. This is called 

production flexibility.  

The theory, therefore, suggests that investment will change in favour of the lowest cost location after an exchange 

rate movement. This means that it is profitable for a multinational enterprise to open plants at home and abroad, 

postponing production decision until after an exchange rate shock. All things being equal, investing in a country 

with a high degree of exchange rate volatility will have a higher risk in terms of a stream of profit. Hence, as long as 

the investment is partially irreversible, there are some benefits of holding back investment to acquire more 

information about the direction of the exchange rate movement. This theory, therefore, provides the argument of a 

negative effect of exchange rate uncertainty on foreign investment.  

ii. Risk Aversion Theory 

This theory was popularized by Goldberg and Kolstad (1995). According to the theory, exchange rate volatility lead 

to a decrease in foreign direct investment. Higher exchange rate volatility reduces the certainty equivalent expected 

exchange rate. Certainty equivalent level is employed in the firm’s expected profit function to determine investment 

decision of today in order to realize a profit in a future period. Since firms are more concerned about their future 

expected profits, they will postpone their investment decision as the exchange rate becomes more volatile.     

2.2 Empirical Review  

There is a robust literature on the relationship between foreign capital inflow and exchange rate movement. 

However, there is no consensus among writers on the effect of exchange rate on foreign capital inflow. Some studies 

found a positive impact, while others discovered a negative impact.  

Eregha (2017) examined the impact of exchange rate polices and inflation expectations on foreign direct investment 

(FDI) flow in the West Africa Monetary Zone (WAMZ) using annual time series data for the period 1980 – 2014. 

The Arellano panel correction for serial correlation and heteroscedasticity option of within estimate for the selected 

WAMZ countries were employed. From the empirical results, exchange rate uncertainty was found to hinder foreign 

direct investment inflow. Furthermore, the fixed exchange rate policy regime was found to adversely affect foreign 

direct investment inflow. On the other hand, the intermediate policy regime was found to have a positive impact on 

FDI inflow during the periods of the current account imbalance with changes in foreign exchange rate reserve as the 

channel. During this period, the study observed that the negative effect of the fixed exchange rate policy on FDI 



 
 

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increased. Hence, the study concluded that fixed exchange rate regime is not a good policy in a period of current 

account imbalance.  

Ali, Mohammed and Zahir (2017) examined the impact of exchange rate on Foreign Direct Investment (FDI) in 

Somalia using annual time series data for the period 1980 – 2010. The Ordinary Least Square (OLS) estimation 

technique was adopted by the study. From the empirical result, the exchange rate has a significant and negative 

impact on foreign direct investment in Somalia.    

Busse, Hefeker, and Nelgen (2010) examined the impact of exchange rate on FDI inflow in developed and 

developing countries for the period 1980 – 2014. The fixed effect model was specified for the study and estimated 

using the ordinary least square technique. The maximum likelihood estimator was also employed to check for the 

robustness of the estimates. From the empirical results, exchange rate levels were found to have a negative effect on 

FDI inflow in developing countries. In the developed countries, the exchange rate has a positive but insignificant 

effect on FDI inflow. The result from the study shows that a fixed exchange rate regimes have a positive and 

significant effect on FDI inflow in developed countries, while in the developing countries the impact was not 

statistically significant.  

Abbott, Cushman and Vita (2012) employed Generalized Method of Moment (GMM) to examine the effect of 

exchange rate policy on FDI inflow in seventy developing countries for the period 1985 – 2004. From the empirical 

results of their study, it was found that fixed and intermediate policy regimes positively influence FDI inflow as 

compared with the floating policy regime.  

Russ (2012) examined the effect of exchange rate volatility on FDI inflow using a panel of twenty eight OECD 

countries for the period 1980 – 2005. A combination of Ordinary Least Square (OLS), Generalized Least Square 

(GLS) and Generalizes Method of Moment (GMM) were employed in the study to estimate the specialized model. 

The result from the study revealed that the fixed exchange rate regime has a positive impact on FDI inflow.  

Bilawal, Ibrahim, Abbas, Shuaid, Ahmed, Hussain and Fatima (2014) studied the impact of exchange rate on FDI in 

Pakistan for the period 1982 – 2013. The study employed the ordinary least square regression method to estimate the 

specified model. From their results, the exchange rate has a direct and significant impact on FDI in Pakistan. This 

implied that depreciation of the domestic currency encourages FDI inflow in Pakistan.   

Jaratin, Mori, Dullah, Lim, and Rozilee (2014) investigated the effect of exchange rate on FDI in selected Asian 

countries for the period 1970 – 2011. The study covers the Philippines, Singapore, Malaysia, and Thailand. A 

combination of Autoregressive Distributed Lagged (ARDL) bounds test and ECM based autoregressive distributed 

lag approach for causality test was employed to ascertain the nature of the relationship between exchange rate and 

FDI. The empirical results from the study show that there exists a significant long term relationship between 

exchange rate and FDI in Malaysia, Singapore and Philippines with a negative coefficient. This implies that 

appreciation of the countries’ currencies will lead to an increase in FDI inflow. The causality test result shows that 

there exists a bidirectional causality between exchange rate and FDI in Philippines and Singapore, while a long run 

unidirectional causality running from exchange rate to FDI exist for Malaysia.  

Choi, Chung, and Kim (2013) examined the impact of exchange rate volatility on FDI in Korea using monthly data 

for the period 1990 – 2011. A combination of markov switching model estimation technique and multivariate 

GARCH-in-mean model and the impulse responds function were employed in studying the nature of the relationship 

between exchange rate volatility and capital inflow. From the empirical result, all kinds of capital inflows increase 

under low volatile exchange rate regimes. On the other hand, all capital inflow except FDI decreases under high 

volatile exchange rate regimes. The study thus concluded that medium level exchange rate volatility is most 

favorable for economic stability and growth.  

Rashid and Fazal (2010) examined the nature of the relationship between exchange rate volatility and capital inflow 

in Pakistan using monthly data for the period 1990 – 2007. The linear and non-linear co-integration analysis was 

employed. From the empirical results, the causality runs from capital inflow to exchange rate. According to the 

results, monetary expansion emanating from capital inflow fuel exchange rate volatility.  

Brozozoneski (2003) conducted a study on the impact of exchange rate risk on foreign direct investment using a 

panel of 32 countries. A combination of fixed effect ordinary least square, the Generalized Method of moment 

(GMM) and Arellano-Bond model was employed. The result of the estimation revealed that exchange rate volatility 

negatively affects foreign direct investment.     



 
 

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Studies based on Nigeria include Amasoma, Nwosa and Fasoranti (2015), Nwosa and Amassoma (2014), Osinubi 

and Amaghionyeodiwe (2009) and Tokunbo and Lioyd (2009). All the studies employed annual time series data and 

their results were mixed. Nwosa and Amassoma (2014) found that the exchange rate only affects FDI in the long 

run. Their result suggested that an increase in the exchange rate will lead to a fall in FDI in the long run. This was 

corroborated by the findings of Amasoma, Nwosa, and Fasoranti (2015). On the contrary results from the study of 

Osinubi and Amaghionyeodiwe (2009) revealed that an increase in the exchange rate will lead to an increase in the 

flow of FDI. Tokunbo and Lioyd (2009) concluded based on their results that the impact of exchange rate on FDI 

inflow is not statistically significant.       

A close observation shows that on both theoretical and empirical ground there is no consensus on the possible effect 

of the exchange rate on foreign private investment. While some found a positive relationship between both 

variables, others found a negative relationship, even a third variant concluded that there is no significant relationship 

between the exchange rate and foreign private investment. 

Also, with the exception of Choi, Chung, and Kim (2013) which employed monthly data in their study on Korea and 

Rashid and Fazal (2010) which employed monthly data in their study on Paskistan, other empirical studies employed 

the annual time series data. Specifically, all the study in Nigeria were based on annual time series data. The annual 

time series data is normally the annual average of the data which may smoothen the data thereby omitting some 

actual fluctuations which would have been captured by higher frequency data.  

Finally, the majority of the studies captured foreign private investment using FDI. It is important to note that foreign 

private investment is made up of both FDI and foreign portfolio investment. Each of this component of foreign 

investment may react differently to changes in the exchange rate. The non-inclusion of foreign portfolio investment 

in most of the studies amount to only telling a part of the story.  

3. Theoretical Framework and model specification  

3.1 Theoretical Framework  

The empirical model of this study is based on the portfolio balance framework developed by Fernandez-Arias & 

Montiel ((1995) and popularized by Taylor & Sarno (1997) and Moody, Taylor & Kim (2001). Their framework is 

based on the fact that a foreign investor will exploit all the possibility of arbitrage across his home and host country. 

Factors influencing capital flows can be grouped into domestic or pull factors and global or push factors. The pull 

factors represent a country’s specific investment risk and returns which attract foreign investors to invest in a 

country. On the other hand, the push factors represent external factors which push investment towards the host 

country. The pull or domestic factors can further be categorized into those which operates at the country level and 

those that operate at the project or asset level.  

Assuming capital inflows are represented by transactions in different types of assets in the host country, the 

expected returns on investment can be said to be a function of the domestic business environment (DBE). Therefore, 

for a foreign investor to consider investing in a country, he will consider the domestic business environment of the 

host country (DBE), the credit worthiness of the country (Credit) and the financial and economic opportunities in his 

own (source) country (FEO). From the group of factors, domestic business environment (DBE) and credit 

worthiness of the host country (Credit) represent the pull factors, while the financial and economic opportunities 

(FEO) in the source country represent the push factors. The above can be captured in the following equation: 

FI = FI (DBE, Credit, FEO) - - - - - - - 1 

Where: FI = foreign investment.  

The domestic business environment (DBE) can be influenced according to Fernandze-Arias & Montiel (1996) by 

numerous factors. This include among others exchange rate, exchange rate volatility, interest rate, inflation rate, 

foreign investment policy, cost of doing business, institutional factors, output growth rate and macroeconomic 

policy.   

The link between the exchange rate and foreign capital inflow can be seen from the argument advanced by Froot and 

Stein (1991). Using the imperfect capital market framework, they argued that the exchange rate operates on foreign 

private capital inflow through the wealth effect. As the host country currency depreciates, it automatically increases 



 
 

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the wealth of foreigners, allowing them to make higher bids for domestic assets in the host country. In this case, host 

country currency depreciation stimulates inflow of foreign private capital.  

However, there may be an exchange rate risk associated with the timing between investment and profit. If the 

exchange rate depreciates to a lower level to source country currency at the time of profit repatriation relative time 

of investment, it will lower the return of the foreign investor. This exchange rate risk is a factor in the cost of 

investment. Firms will invest abroad when the expected returns equal the cost of operation and payment for the 

degree of risk introduced by exchange rate volatility. The greater the exchange rate volatility, the higher the 

exchange rate risk. Hence, exchange rate volatility has an inverse relationship with foreign private capital inflow.  

3.2 Model Specification      

On the basis of the theoretical framework above, foreign private capital inflow is a function of the exchange rate, 

exchange rate volatility, and other control variables. This can be specified as  

FPCI = FPCI (EXCH, EXCHV, Z) - - - - - - 2 

Where:  

FPCI = Foreign Private Capital Inflow 

EXCH = Exchange rate  

EXCHV = Exchange rate Volatility 

Z = other control variables affecting Foreign Private Capital Inflow 

This can be specified explicitly in econometric form as  

FPCIt = β0 + β1EXCHt + β2 EXCHVt + η1Zt + Ut - - - - 3 

In order to examine the effect of exchange rate on each component of foreign private capital inflow, the foreign 

private capital inflow is disaggregated into Foreign Direct Investment (FDI) and foreign portfolio investment. 

Therefore, the disaggregate model is as follows: 

FDIt = α0 + α1EXCHt + α2EXCHVt + η1Zt + Ut - - - - 4   

Where:  

          FDI = Foreign Direct Investment  

FPI =  Ф0 + Ф1EXCHt + Ф2EXCHVt + η1Zt + Ut - - - - 5 

Where: 

         FPI = Foreign Portfolio Investment  

In the above models, the Z is a vector of control variables that influence the dependent variable. The selected 

variables here include interest rate (INT), Real Gross Domestic Product (RGDP), Inflation rate (INF), Financial 

Development (FD) and degree of openness (OPEN). These variables were selected based on their popular usage in 

economic literature. (for example, Eregha, 2017; Alobari, Paago, Igbara, Felix & Emmah, 2016; Bilawal, Ibrahim, 

Abbas, Shuaib, Ahmed, Hussain & Fatima, 2014; Osinubi & Amaghionyeodiwe, 2009).  

The above model is converted into a VAR model as follows 

FPCIt = β0 + β1 ∑ FPCI t − nk
n=1  + β2 ∑ EXCH t − nk

n=1   + β3 ∑ EXCHV t − nk
n=1  + β4 ∑ INTt − nk

n=1  

  β5∑ RGDPt − nk
n=1   +β6 ∑ INFt − nk

n=1  + β7∑ OPENt − nk
n=1   + β8∑ FDt − nk

n=1  + Ut        (6) 

 

FDIt = β0 +β1 ∑ FDI t − nk
n=1  +β2∑ FPIt − nk

n=1  + β3 ∑ EXCH t − nk
n=1   + β4 ∑ EXCHV t − nk

n=1   

           + β5 ∑ INTt − nk
n=1  + β6∑ RGDPt − nk

n=1   +β7 ∑ INFt − nk
n=1  + β8∑ OPENt − nk

n=1   +  

          Β9∑ FDt − nk
n=1  + Ut         (7)  

                         



 
 

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 FPIt = β0 +β1 ∑ FPI t − nk
n=1  +β2∑ FDIt − nk

n=1  + β3 ∑ EXCH t − nk
n=1   + β4 ∑ EXCHV t − nk

n=1   

           + β5 ∑ INTt − nk
n=1  + β6∑ RGDPt − nk

n=1   +β7 ∑ INFt − nk
n=1  + β8∑ OPENt − nk

n=1   +  

          Β9∑ FDt − nk
n=1  + Ut         (8)  

          

The description of variable and their sign expectations are contained in the table 1 below:  

            Table 1: Description of Selected Variables  

Variables  Description  Measurement  Sign 

expectation  

FPCI Foreign private capital inflow    

FDI Foreign Direct Investment    

FPI Foreign Portfolio Investment    

EXCH Exchange rate  Naira to one US dollar( 

N / US$1) 

+ 

EXCHV Exchange rate volatility  Mean deviation of the 

exchange rate  

- 

INT Interest rate   Prime rate  + 

RGDP Real GDP  GDP at constant market 

price 

+ 

INF Inflation rate  12 months moving 

average inflation. (To 

control to the level of 

economic stability of the 

economy) 

- 

FD Financial Development  The ratio of money 

supply to GDP 

(M2/GDP)( To control 

for financial sector 

development)  

+ 

OPEN Degree of openness  The ratio of total trade 

to GDP 

(
IMPORT+EXPORT

GDP
)  (a 

proxy for trade policy) 

+ 

 

The causal relationship between the dependent variables and the explanatory variables will be examined using the 

Vector Autoregressive modeling technique. This will allow the study to be able to ascertain the direction of causality 

between all the variables in the model.  

4. Empirical Analysis  

The econometric analysis begins with the test of the time series properties of the variables. This involves the unit 

root test and the co-integration test. This is aimed at establishing whether or not the time series is stationary. This is 

followed by the estimation of the specified model and then the test of the hypothesis.  

4.1 Unit Root Test  

The unit root test is based on the Augmented Dickey Fuller (ADF) statistics. The result is presented in the table 

below:  

Table 2: Unit root test of variables  

Unit root test for variables in levels Unit root test for variables in first order difference  

Variables  Computed 

ADF  

Critical 

ADF at 

5% 

Remark  Variables  Computed 

ADF  

Critical 

ADF at 

5% 

Remark  

FPCI -2.1419 -2.9369 Non-stationary  D(FPCI) -11.4492 -2.9369  Stationary  



 
 

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FDI 

FPI 

EXCH 

EXCHV 

INT 

RGDP 

FD 

INF 

OPEN 

-4.2671 

-1.8564 

 1.5986 

1.9035 

-1.2657 

-1.8348 

-2.3420 

-3.0382 

-2.4246 

-2.9350 

-2.9369 

-2.9458 

-2.9369 

-2.9369 

-2.9434 

-2.9540 

-2.9458 

-2.9350 

Stationary  

Non-stationary  

Non-stationary  

Non-stationary  

Non-stationary  

Non-stationary  

Non-stationary  

Stationary  

Non-stationary 

- 

D(FPI) 

D(EXCH) 

D(EXCHV) 

D(INT) 

D(RGDP) 

D(FD) 

- 

D(OPEN) 

- 

-7.6640 

-3.7869 

-5.9144 

-5.3269 

-3.0045 

-2.9625 

- 

-7.0696 

- 

2.9389 

-2.9458 

-2.9369 

-2.9369 

-2.9434 

-2.9540 

- 

-2.9389 

- 

Stationary 

Stationary 

Stationary 

Stationary 

Stationary 

Stationary 

- 

Stationary  

                          

From the table 5 above, only FDI and inflation rate were stationary in levels. Hence, these two variables can be said 

to be integrated of order one. The other variables were stationary in their first order difference, hence they are said to 

be integrated of order one. 

4.2 Co-integration Test  

The unit root test shows that most of the variable are non-stationary. Regressing non-stationary variables on each 

other may lead to spurious regression. Therefore, it is important to ascertain if there exists a long run or equilibrium 

relationship between the variables. If there is a long run or equilibrium relationship between the variables then it 

means that although the variables are non-stationary, their linear combination is stationary, hence they drift together 

over time. This can be established by conducting a co-integration test.  

The Johansen co-integration test is employed to ascertain the existence of a long run or equilibrium relationship 

between the variables. The Johansen co-integration test is based on trace statistics and maximum Eigenvalue 

statistics at a 5% significance level. The results are presented in the tables below  

Table 3: Johansen co-integration rank test based on trace statistics  

Hypothesized No. of 

Co-integrated 

equations   

Eigenvalue  Trace 

statistics  

0.05 critical 

value  

Probability  

None * 

At most 1* 

At most 2 

At most 3 

At most 4 

At most 5 

At most 6 

At most 7  

0.8693 

0.6494 

0.4878 

0.4159 

0.2785 

0.2359 

0.2191 

0.0296 

206.5283 

125.1318 

83.2009 

56.4338 

34.9261 

21.8639 

11.0992 

1.2049 

159.5297 

125.0154 

95.7536 

69.8188 

47.8561 

29.7970 

15.4947 

3.8414 

0.0000 

0.0434 

0.2658 

0.3607 

0.4519 

0.3061 

0.2055 

0.2723 

     Trace test indicates 2 co-integrating equations at 0.05 level 

 

Table 4: Johansen co-integration rank test based on maximum eigenvalue statistics 

Hypothesized No. of 

Co-integrated 

equations   

Eigenvalue  Max-Eigen 

statistics  

0.05 critical 

value  

Probability  

None * 

At most 1* 

At most 2 

At most 3 

At most 4 

At most 5 

At most 6 

At most 7  

0.8693 

0.6494 

0.4878 

0.4159 

0.2785 

0.2359 

0.2191 

0.0296 

81.3964 

41.9308 

26.7670 

21,5076 

13.0622 

10.7646 

9.8942 

1.2049 

52.3626 

40.2314 

40.0775 

33.8768 

27.5843 

21.1316 

14.2646 

3.8414 

0.0000 

0.0346 

0.6494 

0.6456 

0.8815 

0.6708 

0.2190 

0.2723 

Maximum Eigenvalue test indicates 2 co-integrating equations at 0.05 level 

 

From the results in tables 7 and table 8 above, both the trace test and the maximum eigenvalue test indicates the 

existence of two co-integrating equations at a 5% significance level. This is a clear indication that the variables are 



 
 

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27 
 

co-integrated. Hence there exist a long run or equilibrium relationship between the variables. On this basis, it can be 

concluded that although the variables are stationary on an individual basis, a linear combination of the variables is 

stationary. Therefore regressing the variables on each other will not produce a spurious regression.   

4.3 Selection of lag length  

In order to estimate the VAR model, there is the need to ascertain the VAR lag length. In this study, the lag length 

selection is based on five criteria. These include Sequentially modified LR test statistics, Final prediction error 

(FPE), Akaike information criterion (AIC), Schwarz information criterion (SC) and Hannan-Quinn information 

criterion (HQ). The result of the various test is presented in the table below: 

Table 5: VAR Lag Order Selection Criteria  

Lag  Logl  LR FPE AIC SC HQ 

0 

1 

2 

3 

-1771.771 

-1541.135 

-1430.173 

-1312.304 

NA 

354.8256 

125.1881 

84.6234* 

6.00e+29 

1.25e+26 

1.73e+25 

4.44e.24* 

91.2703 

82.7248 

80.3165 

77.5540* 

91.6115 

85.7960* 

86.1176 

86.0851 

91.39.27 

83.8267 

82.3979 

80.6149* 

*indicates lag order selected by the criterion  

 

From table 9 above, four out of the five criteria selected lag order of three while one criterion selected one lag 

length. Specifically, Sequentially modified LR test statistics, Final prediction error (FPE), Akaike information 

criterion (AIC) and Hannan-Quinn information criterion (HQ) selected lag order of three, while Schwarz 

information criterion (SC) selected one lag order. Therefore, this study in the estimation of the VAR model adopted 

the lag order of three.  

4.4 Vector Autoregressive Estimates  

The estimation of the vector autoregressive model was conducted in three stages. The first stage is the estimation of 

the aggregate foreign private investment inflow model. This is followed by the disaggregate model comprising of 

foreign direct investment and foreign portfolio investment.  

i. Estimates of the aggregate foreign private investment model 

The result of the estimation of the vector autoregressive model for the foreign private investment is presented in the 

table below:  

Table 6: Estimates of aggregate foreign private investment model   

Regressors  FPCI EXCH EXCHV INT RGDP INF OPEN FD 

FPCI(-1) 

 

FPCI(-2) 

 

FPCI(-3) 

 

EXCH(-1) 

 

EXCH(-2) 

 

EXCH(-3) 

 

EXCHV(-

1) 

 

EXCHV(-

2) 

 

EXCHV(-

0.136* 

(6.831) 

0.066** 

(1.857) 

0.417** 

(1.963) 

-0.255** 

(-2.382) 

-0.184** 

(-2.022) 

-0.351 

(-0.227) 

-1.712** 

(-2.381) 

-1.905* 

(-6.638) 

-2.347* 

(-8.594) 

1.203 

1.168** 

(1.894) 

7.359 

(0.898) 

-4.426 

(-0.057) 

1.032** 

(2.191) 

0.996* 

(15.575) 

0.058* 

(4.017) 

-0.005*** 

(-1.460) 

0.006 

(0.997) 

0.004 

(0.652) 

3.852 

1.090 

(1.029) 

7.030 

(0.501) 

2.660 

(0.202) 

9.136** 

(2.247) 

-3.043* 

(-2.779) 

3.968 

(0.659) 

0.629* 

(3.322) 

1.109* 

(3.489) 

1.168 

(1.098) 

6.014 

8.323** 

(1.730) 

-5.925 

(-0.092) 

-6.720 

(-1.119) 

0.046 

(1.269) 

-0.048 

(-0.963) 

0.027 

(0.991) 

-0.0004 

(-1.585) 

0.0008*** 

(1.655) 

3.400 

(0.070) 

0.891* 

0.090* 

(8.657) 

9.960* 

(4.978) 

0.500* 

(4.976) 

-1.608 

(-0.517) 

7.639 

(0.181) 

1.113 

(0.480) 

-2.716** 

(-2.122) 

-5.701 

(-0.132) 

.7.717** 

(-1.882) 

1.626 

-5.350 

(-0.506) 

-4.650 

(-0.331) 

3.310 

(0.251) 

-0.023*** 

(-1.395) 

-0.008 

(-0.385) 

0.028** 

(2.341) 

1.700 

(0.123) 

0.0002 

(0.915) 

-

0.0003*** 

(-1.382) 

-5.350 

(0.506) 

-4.650 

(-0.331) 

3.310 

(0.251) 

-0.0004 

(-0.568) 

7.370 

(0.0006) 

7.810 

(0.129) 

7.170 

(0.106) 

6.390 

(0.571) 

-5.790 

(-0.544) 

0.008**

 6.950* 

(2.752) 

 7.340** 

(2.190) 

6.210 

(0.197) 

-0.004** 

(-2.224) 

-7.550 

(-0.028) 

0.0009 

(0.668) 

2.150*** 

(1.342) 

-1.130 

(-0.424) 

-1.640 

(-0.645) 

0.014 



 
 

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28 
 

3) 

 

INT(-1) 

 

INT(-2) 

 

INT(-3) 

 

RGDP(-1) 

 

RGDP(-2) 

 

RGDP(-3) 

 

INF(-1) 

 

INF(-2) 

 

INF(-3) 

 

OPEN(-1) 

 

OPEN(-2) 

 

OPEN(-3) 

 

FD(-1) 

 

FD(-2) 

 

FD(-3) 

 

C 

 

(0.835) 

-0.269 

(-0.121) 

0.179 

(0.297) 

0.164** 

(1.945) 

0.833* 

(4.654) 

0.298** 

(1.854) 

-0.528 

(-0.171) 

-3.708** 

(2.342) 

-2.129 

(-0.748) 

6.924* 

(2.568) 

2.038** 

(1.849) 

5.177 

(0.767) 

7.818* 

(3.055) 

1.812 

(0.587) 

6.032** 

(1.764) 

5.373 

(0.261) 

(1.175) 

-3.609 

(-0.715) 

-1.291 

(-0.352) 

-2.762** 

(-2.041) 

-1.123** 

(-2.377) 

2.552 

(0.069) 

9.755 

(1.392)**

* 

-12.533 

(-1.201) 

5.239 

(0.809) 

-184.090 

(-1.051) 

74.990 

(0.502) 

-154.359 

(-1.005) 

-27.103* 

(-3.296) 

-56.827* 

(-2.809) 

21.204 

(0.272) 

-1.096 

(-0.301) 

(1.072) 

-6.917* 

(3.428) 

-17.132 

(-0.027) 

0.0001 

(0.168) 

-0.0001 

(-0.226) 

-0.001 

(-0.260) 

1.962** 

(1.925) 

-2.021 

(-1.132) 

9.540 

(0.860) 

-3.054* 

(-2.550) 

-2.513* 

(-9.853) 

-

3.697*** 

(-1.407) 

-3.036 

(-0.304) 

-1.189 

(-0.989) 

-3.442* 

(-2.521) 

-2.760 

(-0.345) 

(3.489) 

-0.109 

(-0.278) 

-0.067 

(-0.236) 

5.670 

(0.205) 

1.220 

(0.038) 

1.420 

(0.496) 

-0.125** 

(-2.230) 

-0.219 

(-0.270) 

0.326 

(0.646) 

-

18.563*** 

(-1.359) 

27.648** 

(2.374) 

0.654 

(0.054) 

3.374 

(0.743) 

-6.089 

(-1.112) 

-2.471 

(-0.407) 

-1.096 

(-0.301) 

(0.075) 

-5.111 

(-0.153) 

16.235 

(0.671) 

0.967* 

(4.153) 

0.384** 

(2.278) 

0.639* 

(2.647) 

-3.783** 

(-2.306) 

-2.158** 

(-2.001) 

-1.444* 

(-5.352) 

3.374* 

(2.615) 

7.382** 

(1.918) 

7.973* 

(7.321) 

1.169* 

(2.667) 

3.379 

(0.729) 

1.132** 

(2.208) 

-2.638 

(-0.085) 

0.326* 

(2.831) 

-0.223 

(-1.263) 

0.139 

(1.086) 

-1.260 

(-0.101) 

7.400 

(0.051) 

-1.560 

(-1.207) 

1.360* 

(5.526) 

-0.383 

(-1.045) 

-0.198 

(-0.872) 

9.249*** 

(1.502) 

2.437 

(0.464) 

-9.543*** 

(-1.769) 

3.217*** 

(1.572) 

2.313 

(0.937) 

2.768 

(1.012) 

-2.959** 

(-1.803) 

* 

(1.594) 

-0.006 

(-0.756) 

-0.003 

(-0.587) 

7.170 

(1.184) 

-4.290 

(-0.614) 

-3.730 

(-0.594) 

-0.014 

(-1.202) 

0.025**

* 

(1.452) 

-0.006 

(-0.608) 

0.305* 

(2.774) 

0.181* 

(3.250) 

0.042 

(0.159) 

0.135**

* 

(1.361) 

-0.067 

(-0.560) 

-0.066 

(-0.502) 

0.120**

* 

(1.505) 

(1.066) 

-0.019 

(-0.923) 

0.020 

(1.399) 

4.120* 

(2.851) 

1.530 

(0.917) 

-1.880 

(-1.252) 

-0.032 

(-1.118) 

0.087** 

(2.054) 

-0.054** 

(-2.068) 

0.910 

(1.270) 

-0.112 

(-0.184) 

-1.139** 

(-1.815) 

0.605* 

(2.542) 

0.039 

(0.137) 

-0.085 

(-0.268) 

0.324** 

(1.699) 

R-Squared 

F-Statistics 

0.753 

5.780 

0.979 

27.382 

0.926 

7.301 

0.964 

15.724 

0.969 

18.433 

0.994 

99.585 

0.876 

4.133 

0.848 

3.278 

                  *significant at 1% ** significant at 5% *** significant at 10% 

Colum two of the above table shows the estimates of the foreign private capital inflow equation. From the result, all 

sign expectations were met and the test statistics show good performance. From the result, the lagged values of the 

foreign private capital inflow have a significant impact on its current period value. With the positive sign of the 

lagged period values, an increase in the previous level of foreign private capital inflow will lead to an increase in its 

current period value. This was statistically significant at 1% judging by the values of the t-ratios.  

The exchange rate has a negative sign and the impact on foreign private capital inflow was statistically significant in 

the first and second lagged periods. However, the impact was not significant in the third lagged period. With the 

negative sign, an increase in the exchange rate (devaluation/ depreciation of the naira) will lead to a fall in foreign 

private capital inflow.  

Exchange rate volatility variable was statistically significant in the first, second and third lagged periods and the 

coefficients have negative signs. Hence, the increase in exchange rate volatility leads to a fall in foreign private 

capital inflow.  

ii.   Estimates of the disaggregate foreign private investment model 

The result of the estimation of the vector autoregressive model for the disaggregated foreign private investment is 

presented in the table below: 



 
 

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29 
 

Table 7: Estimates of disaggregate foreign private investment model  

Variables  FDI FPI EXCH EXCHV INT RGDP INF OPEN FD 

FDI(-1) 

 

FDI(-2) 

 

FDI(-3) 

 

FPI(-1) 

 

FPI(-2) 

 

FPI(-3) 

 

EXCH(-1) 

 

EXCH(-2) 

 

EXCH(-3) 

 

EXCHV(-

1) 

 

EXCHV(-

2) 

 

EXCHV(-

3) 

 

INT(-1) 

 

INT(-2) 

 

INT(-3) 

 

RGDP(-1) 

 

RGDP(-2) 

 

RGDP(-3) 

 

INF(-1) 

 

INF(-2) 

 

INF(-3) 

 

OPEN(-1) 

 

OPEN(-2) 

 

OPEN(-3) 

 

FD(-1) 

 

0.938* 

(2.877) 

0.230** 

(1.932) 

0.076** 

(1.964) 

0.133** 

(1.672) 

0.015*** 

(0.136) 

0.055 

(0.604) 

-1.339** 

(-1.899) 

-1.585** 

(-2.252) 

-2.721 

(-0.618) 

-2.315** 

(-2.274) 

-1.256** 

(-2.022) 

-3.315** 

(-2.147) 

-1.008** 

(2.044) 

-

1.210*** 

(-1.548) 

-2.319 

(0.410) 

1.065** 

(2.125) 

1.263** 

(2.341) 

16.488 

(0.337) 

-1.601** 

(-1.845) 

-3.201** 

(-2.378) 

-2.560* 

(-2.785) 

6.350* 

(2.600) 

4.130** 

(2.173) 

6.160* 

(3.080) 

8.010** 

(1.971) 

1.110 

(1.105) 

0.501 

(0.465) 

1.605** 

(1.678) 

0.800 

(0.867) 

0.410*** 

(1.554) 

0.350** 

(1.930) 

0.707** 

(2.314) 

-2.121** 

(-2.098) 

-2.761* 

(-2.512) 

-6.645* 

(-4.430) 

-1.336* 

(-13.204) 

-3.217* 

(-8.543) 

-1.178* 

(-39.863) 

-7.002 

(-0.431) 

3.080 

(1.185) 

-8.950 

(-0.478) 

6.180* 

(3.730) 

8.120* 

(4.552) 

1.897** 

(1.869) 

-1.070* 

(-11.888) 

-4.100* 

(-2.926) 

-2.150** 

(-2.085) 

7.001** 

(2.000) 

5.790* 

(2.412) 

1.001*** 

(1.546) 

3.061** 

(1.800) 

3.600* 

(2.769) 

8.111** 

-1.380* 

(-4.649) 

-3.480* 

(-2.608) 

-5.951* 

(-2.501) 

-4.031* 

(-7.309) 

-5.770 

(-0.054) 

-.5.059* 

(-8.409) 

1.231** 

(2.291) 

-0.683 

(-0.822) 

0.251 

(0.625) 

-0.007*** 

(-1.646) 

0.013*** 

(1.467) 

-0.001 

(-0.235) 

7.878** 

(1.755) 

 

11.121*** 

(1.551) 

3.338 

(0.646) 

9.318* 

(4.606) 

8.167* 

(4.906) 

3.780* 

(4.506) 

9.421* 

(7.907) 

11.844* 

(12.285) 

4.902* 

(8.392) 

4.080** 

(2.372) 

5.360 

(0.030) 

2.655*** 

(1.487) 

-3.738 

(-0.049) 

-1.064* 

(-9.133) 

-3.680 

(-0.681) 

2.730 

(0.005) 

-8.510 

(-0.184) 

1.680 

(1.268) 

7.080 

(0.370) 

9.730 

(0.063) 

1.242** 

(1.974) 

 

6.847*** 

(1.454) 

4.001 

(0.548) 

-0.970 

(-1.130) 

1.845 

(1.090) 

1.203 

(0.812) 

1.102*** 

(1.355) 

-1.380 

(-1.061) 

2.466 

(0.263) 

0.003 

(0.417) 

-0.005 

(-0.655) 

-1.610 

(-0.019) 

1.173 

(0.818) 

-1.171 

(-0.525) 

1.896 

(0.124) 

2.466 

(0.573) 

3.108 

(0.970) 

-

5.014*** 

(-1.548) 

8.400 

(0.061) 

-

-6.990 

(-0.331) 

2.714*** 

(1.446) 

-1.400 

(-0.776) 

1.270** 

(2.454) 

-7.650 

(-1.024) 

-1.150** 

(-1.916) 

0.068** 

(1.810) 

-0.110** 

(-1.880) 

0.047*** 

(1.661) 

-0.007** 

(-2.139) 

0.001* 

(2.456) 

-0.005 

(-1.000) 

1.319* 

(4.152) 

-0.901** 

(-1.777) 

0.422 

(1.155) 

1.730 

(0.053) 

-1.950 

(-0.558) 

5.080*** 

(1.609) 

-0.186 

(-0.333) 

-0.103 

(-0.118) 

0.259 

(0.436) 

-3.122 

(-0.185) 

2.094*** 

(1.674) 

-1.096 

(-0.867) 

0.381 

(0.071) 

-1.141** 

(-1.764) 

7.215 

 0.009* 

(2.534) 

 0.001** 

(1.850) 

 0.003** 

(2.223) 

5.570** 

(2.131) 

0.001 

(0.179) 

 3.510** 

(1.971) 

1.006 

(0.321) 

-2.275 

(-0.472) 

1.181 

(0.507) 

-6.541** 

(-2.023) 

5.866 

(1.085) 

-5.839** 

(-1.934) 

8.210 

(0.315) 

-1.166 

(-0.281) 

2.795 

(0.934) 

0.087 

(0.331) 

-0.387 

(-1.359) 

0.602* 

(2.330) 

-8.455** 

(-1.846) 

9.772*** 

(1.373) 

-

6.424*** 

(-1.321) 

8.093 

(0.589) 

2.632 

(0.257) 

1.021** 

(1.987) 

2.497 

(0.572) 

2.316 

(0.437) 

-9.980 

(-0.913) 

-5.280 

(-0.544) 

9.830 

(1.051) 

-

4.380*** 

(-1.631) 

-6.600** 

(-1.704) 

5.891 

(0.190) 

 

0.027*** 

(1.390) 

4.140** 

(2.001) 

0.026** 

(1.816) 

6.450 

(0.371) 

0.001 

(0.305) 

-0.002 

(-0.996) 

0.363** 

(2.207) 

-0.263 

(-1.002) 

0.106 

(0.559) 

6.750 

(0.401) 

-5.750 

(-0.317) 

-1.490 

(-0.909) 

1.447* 

(4.987) 

-0.479 

(-1.062) 

-0.178 

(-0.579) 

1.174*** 

(1.349) 

0.923 

(0.142) 

-1.182** 

(-1.804) 

3.057 

(1.107) 

1.209 

-4.871 

(-1.150) 

-

4.911*** 

(-1.307) 

9.361* 

(2.585) 

-1.341 

(-0.129) 

1.721 

(1.148) 

8.401 

(0.699) 

-

0.001*** 

(-1.450) 

0.001*** 

(1.589) 

-0.005 

(-0.955) 

7.760 

(1.154) 

-1.500 

(-1.129) 

7.830 

(0.674) 

0.006 

(1.063) 

0.001 

(0.186) 

-0.015 

(-2.108) 

1.720* 

(2.640) 

-8.890 

(-1.270) 

-

9.900*** 

(-1.561) 

-0.007 

(-0.690) 

0.020 

(1.167) 

-0.008 

(-0.732) 

0.400 

(1.187) 

-0.138 

(-0.553) 

-0.013 

(-0.054) 

0.211** 

(1.976) 

3.701 

(0.336) 

-3.931 

(-0.402) 

1.941** 

(2.060) 

-7.801* 

(-2.899) 

-

5.611*** 

(-1.442) 

9.411 

(0.302) 

-0.006* 

(-3.190) 

0.003 

(1.032) 

0.004 

(0.299) 

4.300* 

(2.466) 

-6.060** 

(-1.761) 

-1.210 

(-0.400) 

-0.008 

(-0.052) 

0.005 

(0.201) 

0.002 

(0.116) 

4.580* 

(2.712) 

2.160 

(1.190) 

-

2.590*** 

(-1.573) 

-0.003 

(-0.137) 

0.041 

(0.911) 

-0.024 

(-0.776) 

0.208 

(0.237) 

-0.511 

(-0.784) 

-

0.946*** 

(-1.437) 

0.467** 

(1.684) 



 
 

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30 
 

FD(-2) 

 

FD(-3) 

 

C 

 

1.500** 

(2.139) 

9.950** 

(1.824) 

(2.211) 

-0.998 

(-0.554) 

1.126 

(1.111) 

-7.464 

(-0.149)_ 

2.172*** 

(-1.311) 

4.172 

(0.226) 

-2.050 

(-0.227) 

(1.005) 

-0.968 

(-0.274) 

1.318** 

(2.244) 

-1.571 

(-0.545) 

(0.360) 

2.739 

(0.736) 

-

2.390*** 

(-1.308) 

-0.124 

(-0.962) 

-

0.223*** 

(-1.551) 

0.175* 

(2.485) 

0.251 

(0.747) 

-0.437 

(-1.170) 

0.390** 

(2.126) 

R-Squared 

F-

Statistics 

0.865 

2.615 

0.855 

2.417 

0.982 

23.497 

0.932 

5.595 

0.975 

16.499 

0.980 

20.667 

0.994 

78.151 

0.930 

5.454 

0.899 

3.654 

 

From the results in table 11 above, column 2 shows estimates of FDI equation. Form the result, the sign expectations 

were met for all the variables except for interest rate and exchange rate. Also, all the test statistics show good 

performance. The coefficient of the determination was 0.86. This means that about 86% of the systematic variation 

in FDI was explained by the model. The F-Statistics has a coefficient of 2.615. This was significant at the 5% level. 

This shows that the group of the selected explanatory variable are significant determinants of FDI inflow.  

The significance of individual variables was tested using the t-statistics. From the estimates, previous levels of FDI 

has a positive and significant impact on the current level of FDI inflow. The impact was significant at the 1% level 

in the first lagged period, while the second and third lagged period were significant at the 5% level. This shows that 

the higher the previous value of FDI inflow, the more the FDI inflow in the current period.  

The exchange rate has a negative sign. The impact of exchange rate on FDI was significant for all the lagged periods 

at 5% level. This shows that an increase in the exchange rate (devaluation/depreciation of the naira) will lead to a 

fall in FDI inflow. Similarly, exchange rate volatility also has a negative sign in the three lagged periods. The impact 

was also significant at 5% in all the lagged periods. This shows that high volatility of the exchange rate leads to a 

fall in FDI inflow.  

Also, in the foreign portfolio investment equation, the exchange rate has negative signs in all the lagged periods. 

Also, the impact of exchange rate on FPI was significant 5% in lagged period one but in lagged period two and three 

the impact was significant at 1% level. This shows that an increase in the value of the exchange rate (devaluation of 

the naira) will lead to a fall in FPI inflow. Closely related is the change rate volatility. Exchange rate volatility has a 

negative sign in as all the lagged periods and its impact on FPI inflow was highly significant even at 1% level. This 

shows that volatility in exchange leads to a fall in FPI inflow into Nigeria. 

On the whole, findings from the estimation of the specified models can be summarized as follows:  

i. Devaluation of the naira has a negative impact on foreign private capital (both foreign direct 

investment and foreign Portfolio investment) inflow in Nigeria.  

ii. The volatility of the exchange rate of the naira reduces foreign private capital inflow in Nigeria. Hence, 

a stable exchange rate promotes foreign private capital inflow.  

iii. Increase in the size of the domestic market promotes foreign private capital inflow in Nigeria. The 

bigger the size of the economy the more attractive it is to foreign investors.   

iv. Increase in the rate of inflation discourages foreign private capital inflow in Nigeria. 

v. Development of the financial sector is a significant factor in promoting foreign private capital inflow 

in Nigeria.  

vi. Increase in the size of the domestic market stimulates the appreciation of the domestic currency of a 

country. This means the sustained growth of the Nigerian economy can lead to an appreciation of the 

naira.  

vii. Increase in the level of financial development will lead to a fall in the exchange rate. This means that 

an improvement in the operations of the financial sector of the economy will lead to an appreciation of 

the naira.  

5. Policy Recommendations 

On the basis of the above findings, the followings are possible recommendations.  



 
 

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i. The government through the central bank should put in place policies to stabilize the exchange rate of the 

naira. This can be done through regular Central Bank intervention foreign exchange market. A more stable exchange 

rate will promote foreign capital inflow into the economy.  

ii. An appropriate macroeconomic policy framework should be put in place to boost the size of the domestic 

market. An increase in the real Gross Domestic Product (GDP) will stimulate foreign capital inflow into the 

economy. Increase in the size of the domestic economy will also empower the naira to appreciate.  

iii. The government through the central bank should employ appropriate macroeconomic policies to control the 

inflationary pressure in the economy. The current 15% inflation rate is on the high side. As the empirical result from 

this study revealed, increasing rate of inflation discourage foreign capital inflow and also adversely affect the 

exchange rate of the naira.  

iv. A sound financial sector is a basic pre-requisite for assessing the absorptive capacity of the domestic 

economy to the inflow of foreign capital. Therefore, the Nigerian government through the various financial sector 

regulatory agencies should step up their supervisory role in the sector in order to boost the soundness of the financial 

sector of the economy.  

6. Conclusion 

Foreign private capital inflow is a significant determinant of economic growth in Nigeria. In attempting to stimulate 

economic growth which is a key macroeconomic goal of the Nigerian state, the economy must be made attractive for 

foreign capital inflow. From the empirical results, the stable exchange rate is a key determinant of foreign private 

capital inflow in Nigeria. Therefore, an appropriate exchange rate policy aimed at stabilizing the exchange rate of 

the naira is needed. In order to achieve this, the recommendations above would be helpful.     

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