




































American International Journal of Business and Management Studies  

Vol. 1, No. 1; 2019 

Published by American Center of Science and Education, USA 

 

20 

 

Macroeconomic Variables and Private Investment: A Two Dimensional 

Study from Nigeria Economy  

 
 

Mrs. Fortune Bella Charles M Sc 

Department of Banking and Finance,  

Rivers State University, Rivers State, Nigeria 

 

 

Charles Ugochukwu Okoro  M Sc. 

Department of Accountancy 

Ken Saro-Wiwa Polytechnic, Bori 

Rivers State 

 

 

ABSTRACT 
The study investigated the impact of macroeconomic variables on private investment in Nigeria for the period 1990 

to 2016. To achieve these objectives, the study tests for the study modeled private equity and private real investment 

as the function exchange rate, financial sector development, and interest rate, openness of the economy, real gross 

domestic product, inflation rate and broad money supply. Ordinary least square method of data analysis was used.  

From model one, the study found that real gross domestic product have positive but insignificant effect, openness of 

the economy have positive and insignificant effect, interest rate have positive and significant effect, financial 

deepening have positive and insignificant effect while interest rate, inflation rate and exchange rate have negative 

effect on private real investment. The coefficient of determination (R
2
) proved that the independent variables can 

explain 62 percent variation on private real investment; the f- statistics found that the model is significant while the 

Durbin Watson statistics proved the presence of serial autocorrelation.  The effect of macroeconomic variables on 

private equity investment was presented in model two. The study found that openness of the economy; real gross 

domestic products, broad money supply, and interest rate have negative and insignificant effect on private equity 

investment except openness of the economy with significant effect. Inflation rate, financial sector deepening and 

exchange rate have positive and insignificant effect on private equity investment except financial deepening with 

significant effect. The R
2
 proved that the independent variables can predict 66.9 percent variation on private equity 

investment. The f- statistics found that the model is significant while the Durbin Watson statistics proved the 

presence of serial autocorrelation. We conclude that macroeconomic variable have significant effect on private 

investment in Nigeria. We recommend that interest rate must be able to encourage higher private investment by 

increasing the real interstate on private savings or household savings so that larger amount of income would be 

saved to accumulate more capital and hence private investment. Policies should be formulated by investors and 

government to discourage factors that affect negatively private investment. 

 

Keywords: Macroeconomic Variables, Private Investment, Nigeria Economy, Interest Rate, Money Supply. 

 

INTRODUCTION 

The neoliberal view by Galbis (1979) emphasizes the importance of financial deepening and high interest rates in 

stimulating growth through investment. The proponents of this approach, McKinnon (1973) and Shaw (1973) 

offered a theoretical and empirical foundation for the relationship between financial factors and investment in 

developing countries. They argue that developing countries suffer from financial repression and that if these 

countries were liberated from their repressive conditions, savings, investment and growth would be induced to 

increase.  

The underlying assumption of the model is that saving is responsive to interest rates, thus higher saving rates would 

finance a higher level of investment, leading to higher growth Gemech and Struthers, 2003). Financial repressive 

policies such as interest rate ceiling, minimum/maximum lending rates, quantity restrictions on lending, bank 

reserve requirements, capital controls, interalia, cause real interest rates to be negative and unstable especially in the 

presence of high inflation in an economy. According to their argument, a repressed financial sector discourages both 

saving and investment because the rates of return are lower than what could be obtained in a competitive market. As 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

21 

 

a result, financial intermediaries do not function at their full capacity and fail to channel saving into investment 

efficiently, thereby hampering the development of the whole economic system (Reinert et al., 2008).  

McKinnon and Shaw proposed that financial liberalization, which involves the removal or elimination of restrictions 

and controls on financial markets and financial institutions associated with higher real interest rates would stimulate 

saving and investment by reducing the financial constraint of firms and stimulate financial intermediaries to become 

more efficient,  these will help to improve the efficiency of financial intermediation in a country, and contribute 

more to private sector investment thereby resulting in higher economic growth rates (Hermes and Lensink, 2005). 

Thus in the neoliberal view, investment is positively related to the real rate of interest. The reason for this is what 

McKinnon calls the conduit effect where a rise in interest rates increases the volume of financial savings through 

financial intermediaries and thereby raises investible funds.  

 The above theories and analysis on factors that determine investment are appealing but failed to explain the case of 

the developing countries like Nigeria. For instance Nigerian business environment is rated one of the most 

challenging in the world, there are various issues of policy conflict and policy mismatch. Increase in fiscal policy 

when there is contractionary monetary policy can affect macroeconomic variables and domestic real and portfolio 

investment. There are many studies on factors that determine investment, some of the studies focused on one type of 

investment. For instance Lucky and Uzah (2016) examined monetary policy transmission mechanism and domestic 

real investment in Nigeria. This study examined macroeconomic factors that determine real and portfolio investment 

in Nigeria. 

LITERATURE REVIEW 

Theory of Investment  

 

Keynesian Theory of Investment 
In the General Theory, Keynes (1936) emphasized the central role of investment as the driving force of influencing 

aggregate output, employment, and short run fluctuations in economic activity. The theory emphasizes that 

investment is the result of firms harmonizing the expected return on new capital, referred to as the marginal 

efficiency of capital (MEC), and with the cost of capital, which depends primarily on the real interest rate. The 

theory maintains that at lower rates of interest, more capital projects appear financially viable while higher interest 

rates lead to some projects being postponed or cancelled since the cost of borrowing to finance investment become 

higher. To the Keynes since investment is volatile and dependent on firms’ expectations of the profitability of 

investment, so long as the expected yield on their investment exceeds the real interest rate, new investment will take 

place. Keynes rejected the notion that investment was based exclusively on technological conditions of capital 

productivity, but emphasized monetary factors and finance and uncertainty as the basic determinants of investment 

(Fazzari, 1989). 

 

The Rigid Accelerator Theory 
The simplest theory of investment demand is the rigid accelerator model formulated by Clark (1917). In its simplest 

form, the rigid accelerator theory of investment states that investment is proportional to the increase in output which 

is proxy by changes in demand in the coming period. Thus, the accelerator model relates investment to changes in 

demand and proposes that an increase in a firms output will require a proportionate increase in its stock of capital. 

The theory basically assumes that firms‟ desired capital-output ratio is roughly constant and net investment takes 

place when output is expected to increase. In effect, the theory implies that the level of output or the changes in 

aggregate demand determines investment or the change in capital stock. Mathematically, this proposition of the 

theory is expressed as Kt* = σY, where σ is the desired capital-output ratio which is assumed to be constant, Kt* is 

the desired capital stock in period t, and Yt is the level of output in the same period. 

 

The McKinnon-Shaw Hypothesis 
The neoliberal view by emphasizes the importance of financial deepening and high interest rates in stimulating 

growth through investment. According the work of McKinnon and Shaw (1973), which offered a theoretical and 

empirical foundation for the relationship between financial factors and investment in developing countries, 

developing countries suffer from financial repression and that their liberation from these repressive conditions, 

investment, savings and growth would be induced to increase. The important assumption of the model is that saving 

is responsive to interest rates, thus a higher saving rates would finance a higher level of investment, leading to 

higher growth (Gemech and Struthers, 2003). 

 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

22 

 

According to their argument, a repressed financial sector discourages both saving and investment because the rates 

of return are lower than what could be obtained in competitive market. As a result, financial intermediaries do not 

function at their full capacity and fail to channel saving into investment efficiently, thereby hampering the 

development of the whole economic system (Reinert et al., 2008). 

 

Empirical Review  

 

Yamori (1995) using instrumental variable technique from the period 1975-1988; Delke (1996) reported results 

consistent with the hypothesis of Feldstein-Horioka for Japanese data. Similarly, Palley (1996) tested the causal 

relationship between saving and investment over the sample period 1973-1995 using Granger causality test for 

United State. The results showed that investment has a negative effect on personal saving and independent of 

government saving. Also, personal saving negatively affects government saving, thereby concurring with the 

Keynesian paradox of thrift thereby disputing F-H puzzle. Ozmen and Parmaksiz (2003) used Johansen 

cointegration technique and Engle and Granger two-step residual-based approach to cointegration to test for the 

Feldstein-Horioka puzzle for UK economy in the period 1948-1998. The authors concluded that there exist a long 

run relationship between saving and investment, thereby lending support to the Feldstein-Horioka puzzle.  

 

Payne (2005) employed Engle-Granger and error correction model (ECM) to study the relationship between saving 

and investment in Mexico over the period 1960-2002. The results showed that savings and investment are 

cointegrated, thereby indicating low capital mobility in accordance with F-H hypothesis. However, the coefficient of 

error correction model is positive and statistically significant with a binding intertemporal budget constraint and an 

adjustment parameter of 0.242.  

 

Narayan (2005) studied the relationship between investment and saving for the period 1960-1999 by applying 

Autoregressive Distributed Lag (ARDL) Model and Granger causality test for Japan. The author found long run 

relationship between saving and investment which suggest that there must be granger causality in at least one 

direction. Therefore, the Granger causality test results suggest bidirectional causality relationship between saving 

and investment. Thus, lending support to Feldstein and Horioka (1980) hypothesis.  

 

Singh (2008) examined the long run relationship between saving and investment to determine the degree of capital 

mobility using Two-step Residual-based test, Autoregressive Distributed Lag (ARDL) Model and Granger causality 

test from the period 1950-51 to 2001-02. The results revealed long run relationship between saving and investment 

in India, supporting the Feldstein-Horioka hypothesis.  

 

Mishra et al. (2010) studied the dynamic relationship between savings and investment in India for the period 1950-

51 to 2008-09 by employing Johansen cointegration technique and Granger causality test via Vector Autoregressive 

framework. The authors found the presence of long run equilibrium relationship between saving and investment in 

India. The Granger causality test revealed directional causal relationship between the variables under study.  

 

Seth (2011) applied Engle-Granger and Error Correction Model (ECM) to investigate the long run relationship 

between saving and investment for India from the period 1980-2008. The results showed long run relationship 

between savings and investment. The results also revealed long run equilibrium relationship between corporate 

savings and corporate investment. The former supports low capital mobility into India, whereas the latter revealed 

that corporate sectors dependency on their fund for investment.  

 

Tang and Lean (2008) applied Rolling Windows Bounds test to empirically investigate the relationship between 

savings and investment over the period 1960-2007 for Malaysia. The study showed that savings and investment are 

not cointegrated implying that capital is internationally mobile over the same period.  

 

Shahbaz et al. (2010) analyzed savings and investment correlation through the application of Autoregressive 

Distributed Lag (ARDL) bounds testing for cointegration through Error Correction Model (ECM) for Pakistan from 

period1976-2006. The authors reported long run relationship among savings, domestic investment, inflation, real 

exchange rate, and financial development which invariably indicate inadequate capital mobility in the country.  

 

Adebola and Dahalan (2012) investigated the relationship between savings and investment nexus for Tunisia from 

the period 1970-2009 by employing Autoregressive Distributed Lag (ARDL) Model and Granger causality test. The 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

23 

 

authors found the existence of long run relationship when investment is taken as dependent variable. The results of 

Granger causality test revealed two-way relationship justifying the low capital mobility as suggested by FH 

hypothesis. Empirical studies also emerged from a panel of OECD countries.  Krol (1996) examined the relationship 

between saving and investment using data for 21 OECD countries covering the period 1962-1990 by employing 

fixed effects estimates. The results reject the idea that capital is highly mobile internationally.  

 

Jansen (1996) re-examined the relationship between savings and investment for 23 OECD countries spanning the 

period 1951-1991 using Error Correction Model (ECM). The author revealed evidence of cointegration between 

saving and investment which invariably indicating an in capital mobility within the OECD. Another study by 

Hussein (1998) for 23 OECD countries over the period 1960-1993 to test the Feldstein-Horioka hypothesis by 

applying Dynamic Ordinary Least Square (DOLS). The results revealed that international capital mobility in 18 out 

23 is very low, while the results suggest a moderate change in Canada, Denmark, Finland, Greece and Sweden.  

 

Kasuga (2004) investigated the relationship between savings-investment nexus for 23 OECD and 79 non-OECD 

countries spanning 1980-1995. The author employed Ordinary Least Square (OLS) and instrumental variables. The 

results revealed that if domestic saving increases net worth, it increases domestic investment. Therefore, the study 

suggests that the impact of domestic saving depends on financial system and their development.  Pelgrin and Schich 

(2008) applied a panel Error Correction Model (ECM) to analyze the long run relationship distinctly from the short 

run adjustment via the Autoregressive Distributed Lag (ARDL) Model in addition to Dynamic Fixed-Effects 

Estimator (DFE), Pooled Mean Group (PMG) estimator and Mean Group Estimator (MGE) for 20 OECD countries 

from 1960-1999. The authors found that saving and investment have long run cointegration relationship that is 

consistent with the interpretation that a long run solvency constraint is binding for each country.  

 

Raoet al. (2010) applied the Bludell and Bound systems GMM method and Structural Breaks tests of Mancini-

Griffoli and Pauwels to test the Feldstein-Horioka from the period 1960-2007 for a panel of 13 OECD countries. 

The results evidenced that the Feldstein-Horioka hypothesis is valid in the pre-Bretton Woods period and 

international capital mobility was negligible even though there has been a significant improvement in international 

capital mobility in the OECD countries. Last but not least, another group of studies examine if the puzzle also holds 

in country groups other than the OECD countries.  Mamingi (1997) tested the savings and investment correlation by 

employing Ordinary Least Squares and Fully Modified Least Squares for 58 developing countries over the period 

1970 1990. The author revealed that many developing countries are financially integrated in the long run. The 

results further showed that saving and investment correlation for low-income countries is higher than those for 

middle-income countries, using Japan and 10 other Asian countries data by employing Johansen framework 

covering the period 1950-1999.  

 

Sinha (2002) revealed long run relationship between savings and investment for Myanmar and Thailand. The study 

also showed that the growth of the saving rates granger causes the growth rate of investment rates for Malaysia, 

Singapore, Sri Lanka and Thailand. However, causality runs from investment rates to saving rate for Hong-Kong, 

Malaysia, Myanmar and Singapore.  

Chakrabarti (2006) re-examined the relationship between saving and investment by employing Multivariate 

Heterogeneous panel cointegration for the panel of 126 countries spanning 1960-2000. The author found a 

significant positive association between the ratio of gross domestic investment to GDP and the ratio of gross 

domestic saving to GDP ranging from 0.58 to 0.81. The evidence of cointegration and a significant positive 

correlation between saving and investment may indicate a low degree of financial integration in the world capital 

markets, which is the basis for the FH hypothesis.  

 

Telataret al., (2007) studied the relationship between savings and investment for 10 European countries over the 

period 1970-2002 by applying a Markov-Switching Model which allowed data to be drawn from two different 

states-high capital mobility and low capital mobility-and extent it to allow variances to change among different 

regimes. The authors found a low correlation between savings and investment for Belgium, Denmark, Finland, 

France, Italy and Sweden. While, no single switching point in the regime of capital mobility measuring the degree of 

correlation between national savings and national investment was reported for the remaining countries.  Kim et al. 

(2007) applied Generalized Least Square (GSL) estimation by iterating the Seemingly Unrelated Regression (SUR) 

system using the newly computed covariance and system equation estimates for Big three (China, Malaysia, and 

Korea), ASEAN countries and Greater China (Hong Kong, Taiwan, and China) covering the period 1980-2002. The 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

24 

 

authors concluded that the saving-investment correlation in East Asia steadily decreases over time but is still higher 

than that of the OECD countries.  

 

Ketenci (2012) used Gregory and Hansen and Johansen approach to cointegration to measure long run relationship 

between savings and investment for 23 EU countries for the period 1995-2009. The author showed that there is 

evidence of cointegration in all cases except for Estonia and Portugal. The low level saving-retention coefficient 

estimated in the presence of structural breaks revealed high capital mobility in most of the countries under study 

disputing the Feldstein-Horioka hypothesis. Dixit and Pindyck (1994) suggested that increased uncertainty caused 

by exchange rate variations reduces investment given the irreversibility of investment projects and, hence, increases 

the value option of delaying expenditures. Jayaraman (1996) in his cross-country study on the macroeconomic 

environment and private investment in six Pacific Island countries observed a statistically significant negative 

relationship between the variability in the real exchange rate and private investment. Thomas (1997) in his study of 

86 developing countries examined data on terms of trade, real exchange rates, and property rights and concluded that 

while factors including credit availability and the quality of physical and human infrastructure are important 

influences, uncertainty in the foreign exchange rate was negatively related to private investment in sub-Saharan 

countries.  

 

Gómez (2000) in a study titled exchange rate volatility effects on domestic investment in Spain argue that there is no 

unique expected exchange rate effect on investment, its sign and importance remaining as a mainly empirical 

question.  Bakare (2011) carried out an empirical analysis of the consequences of the foreign exchange rate reforms 

on the performances of private domestic investment in Nigeria adopting the ordinary least square multiple regression 

analytical method. The multiple regression results showed a significant but negative relationship between floating 

foreign exchange rate and private domestic investment in Nigeria. The findings and conclusion of the study support 

the need for the government to dump the floating exchange regime and adopt purchasing power parity which has 

been considered by researchers to be more appropriate in determining realistic exchange rate for naira and contribute 

positively to macroeconomic performances in Nigeria.  

 

Kanagaraj and Ekta (2011) examined the level of foreign exchange exposure and its determinants in Indian firms 

and it was found that only 16 percent of the firms had exchange rate exposure at 10 percent level of significance. 

About 86 percent of the firms are negatively affected by an appreciation of the rupee which confirms that Indian 

firms are net exporters. On the determinants of exchange rate exposure, the study reveals that export ratio is 

positively and hedging activity is negatively related to the exchange rate exposure of pure exporter firms.  

Nazar and Bashiri (2012) investigates the relationship between real exchange rate uncertainty and private investment 

in Iran for the period of 1988 to 2008 by using quarterly data and applying bivariate generalized autoregressive 

conditional heteroskedasticity (Bivariate GARCH) model in the Iranian economy. The study reveal that real 

exchange rate uncertainty significantly influences private investment and has a negative effect on it and that private 

investment uncertainty affects the level of private investment, negatively.  

 

Lucky and Kingsley (2016) examined factors that determine Nigerian capital formation. The objective was to test 

Jhingans propositions for sources of capital formation in Nigeria. Time series data were sourced from Central Bank 

of Nigeria (CBN) Statistical Bulletin. Nigerian Gross Fixed Capital Formation (GFCG/GDP) was modeled as the 

function of Broad Supply (M2/GDP), Credit to Private Sector (CPS/GDP), Gross National Savings (GNS/GDP), 

Commercial Banks Lending Rate, Exchange Rate (EXR), Inflation Rate (INFR), External Debt (EXTD/GDP), 

Public Expenditure (PEX/GDP), Government Revenue (GR/GDP), Terms of trade (TT/GDP) and Operating Surplus 

(OPS/GDP). Cointegration Test, Augmented Dickey Fuller Unit Root Test, Granger Causality Test and Vector Error 

Correction Model were used to test the dynamic relationship between the variables. Findings proved that M2/GDP, 

GNS/GDP, EXR, EXTD/GDP, TT/GDP have negative and insignificant effect on capital formation while 

CPS/GDP, LR, INFR, PEX/GDP, GR/GDP and OPS/GDP have positive and insignificant effect. The model 

summary revealed 86.0% explained variation and f-statistics 12.38458 probability of 0.000004. The study concludes 

that the variables have significant impact on Nigerian Gross Fixed Capital Formation and confirm the Jhingan’s 

proposition.  

 

Adelowokan Adesoye & Balogun (2015) examines the effect of exchange rate volatility on investment and growth 

in Nigeria over the period of 1986 to 2014. The vector error correction method, impulse responses function, co-

integration and Augmented Dickey Fuller (ADF) test for stationarity were employed to capture the interactions 

between the variables. The results confirm the existence of long run relationship between exchange rate, investment, 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

25 

 

interest rate, inflation and growth. Finally the results show that exchange rate volatility has a negative effect with 

investment and growth while exchange rate volatility has a positive relationship with inflation and interest rate in 

Nigeria.  

 

Chowdhry and Wheeler (2008) in an empirical analysis studied the relationship between volatility of exchange rate 

for the four developed countries of Canada, Japan, United State and United Kingdom. Using a number of variables 

this study applied vector auto regressive (VAR) approach and found that shocks to exchange rate volatility have 

positive and significant impact on flow of FDI.  Akeju (2014) examined the impact of real exchange rate on terms of 

trade and economic growth which relies on cointegration techniques and error correction model using annual data 

covering from 1980-2012. It was revealed that a real exchange rate moves along the same direction with terms of 

trade in the long run.  

 

Rasaq (2013) examined the impact of exchange rate volatility on the macro economic variables in Nigeria and 

findings shows that exchange rate volatility has a positive influence on GDP, FDI and trade openness with a 

negative influence on the inflationary rate in the country.  Ndikumana (2014) searched the implications of monetary 

policy for domestic investment through its impacts on bank lending to the private sector and interest rates in sub-

Saharan African countries, the study based on a sample of 37 sub-Saharan African countries over 1980-2012, the 

study found that monetary policy affects domestic investment negatively indirectly through the bank lending or 

quantity channel, as well as directly through the interest rate or cost of capital channel.  

 

Zulkefly Abdul Karim (2010) searched the impacts of monetary policy on institutions‟ investment in Malaysia, the 

study used dynamic neoclassical framework in an autoregressive distributed lagged (ARDL) mode, the study 

showed the impact of monetary policy on institutions investment spending, the study also reveal that the impact of 

monetary policy channels to the institutions investment are heterogeneous, therefore the small institutions that faced 

financial constraint responded more to monetary tightening as compared to the large institution.  Tobias and Mambo 

(2012) searched the impacts of monetary policy on private sector investment in Kenya during (1996-2009) by 

tracing the impacts of monetary policy through the transmission mechanism to explain how investment responded to 

changes in monetary policy, they founded that government domestic debt and Treasury bill rate are inversely related 

to private sector investment, while money supply and domestic savings have positive relationship with private sector 

investment consistent with the ISLAM model. 

 

Lucky and Uzah (2017) examined the effects of monetary policy transmission mechanisms on the domestic real 

investment in Nigeria, time series data were sourced from Central Bank of Nigeria statistical bulletin from 1981 to 

2015. Domestic real investment was modeled as the function of percentage of credit to private sector to gross 

domestic product, naira exchange rate per US dollar, maximum lending rate, monetary policy rate, prime lending 

rate, net domestic credit, savings rate and Treasury bill rate. Granger causality test and Johansen co-integration test 

in the vector error correction model (VECM) setting were employed. Durbin Watson, β Coefficient, R-Square (R2) 

and F-Statistics were used to determine the relationship between the dependent and independent variables as 

formulated in the regression models. The result proved that CPS/GDP, MLR, MPR, NDC and SR have positive 

relationship with Nigeria real domestic investment while EXR, PLR, and TBR have negative relationship with 

domestic real investment. The cointegration test proved the present of long run relationship between monetary 

policy variables and domestic real investment. The ADF test prove that the variables are stationary at first 

difference, the granger causality test proved both bi-directional, uni-directional and independent relationship running 

from the independent variables to the dependent variable and from the dependent variable to the independent 

variables. The error correction model proved that the speed of adjustment is adequate while the parsimonious error 

correction model proved that MPR and SR have positive relationship while EXR and PLR have negative 

relationship. From the regression summary, the study concludes that monetary policy transmission mechanism has 

significant relationship with Nigeria domestic real investment. 

 

RESEARCH METHODOLOGY 

The research objectives were addressed using empirical analysis of macroeconomic variables that determine 

corporate investment in Nigeria. Private real investment and private traded equities on the floor of Nigerian stock 

exchange as dependent variable. The required data was sourced from Central Bank of Nigeria statistical bulletin 

from 1990-2016. 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

26 

 

Model specification  

The model specified in this study is based on the Classical monetary theory of interest rate and investment. 

PEI = f(EXR,FD,INTR,OPE,RGDP,IFR,M2) 

PEI  = 0 XRE1 FD2 NTRI3 OPE4  RGDP5 IFR6 27M   1 

PRI = f(EXR,FD,INTR,OPE,RGDP,IFR,M2) 

PRI  = 0 XRE1 FD2 NTRI3 OPE4  RGDP5 IFR6 27M   2 

Where  

PEI  = Private Equity Investment  

PRI  = Private Real Investment 

EXR  = Exchange Rate 

FD  = Financial Sector Development 

INTR  = Interest Rate 

OPE  = Openness of the Economy 

RGDP  = Real Gross Domestic Products 

IFR  = Inflation Rate 

M2  = Broad Money Supply 


  = Error Term
 

Stationarity (Unit Root) Tests 

The study investigates the stationarity properties of the time series data using the Augmented Dickey Fuller (ADF) 

test. According to Nelson and Plosser (1982), Chowdhury (1994) there exist a unit root in most macroeconomic time 

series. Non stationary time series will have a time varying mean or a time- varying variance or both. If a time series 

is non stationary, we can study its behaviour only for the time period under consideration, and cannot generalize it to 

other time periods, and hence remain of little practical value if we intend to forecast (Gujarati, 2003). It should be 

noted that a time series is a set of observations on the values that a variable takes at different times (daily, weekly, 

monthly, quarterly, annually, etc). Stationary test therefore checks for the stationarity of the variables used in the 

models. If stationary at level, then it is integrated of order zero which is 1(0). Thus, test for stationarity is also called 

test for integration. It is also called unit root test. Stationarity denotes the non existence of unit root. 

Etyiyy t

m

i
tt  


 1

1
121 

                                       3 

Where:  

ty
   = change time t 

1 ty
 = the lagged value of the dependent variables  

t   = White noise error term  

If in the above 


=0, then we conclude that there is a unit root. Otherwise there is no unit root, meaning that it is 

stationary. The choice of lag will be determined by Akaike information criteria. 

Decision Rulet-ADF (absolute value) > t-ADF (critical value) : Reject Ho (otherwise accept H1) 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

27 

 

Note that each variable will have its own ADF test value. If the variables are stationary at level, then they are 

integrated of order zero i.e 1(0). Note that the appropriate degree of freedom is used. If the variables are stationary at 

level, it means that even in the short run they move together. The unit root problem earlier mentioned can be 

explained using the model: 

Y= Yt-1 + I                                                                                                              4 

Where; Yt is the variable in question; i is stochastic error term. Equation (a) is termed first order regression 

because we regress the value Y at time “t” on its value at time (t- 1). If the coefficient of Yt-i is equal to 1, then we 

have a unit root problem (non stationary situation). This means that if the regression. 

Y= Yt-1 + I                                                                                                                                                                                                   

5   

Where Y and I are found to be equal to 1 then the variable Yt has a unit root (random work in time series 

econometrics). If a time series has a unit root, the first difference of such time series are usually stationary. 

Therefore to salve the problem, take the first difference of the time series. The first difference operation is shown in 

the following model: 

Y=(L-1)Yt-1I                                                                                                                                                         

6 

Yt-1 + I                                                7                                                                                

(Note:  =1-1= 0; where L =1; Yt = Yt - Yt-i) 8              

Integrated Of Order 1 or I (I) 

Given that the original (random walk) series is differenced once and the differenced series becomes stationary, then 

the original series is said to be integrated of order I or I (1). 

Integrated of Order 2 or I (2) 

Given that the original series is differenced twice before it becomes stationary (the first difference of the first 

difference), then the original series is integrated of order 2 or 1(2). 

Therefore, given a time series has to be differenced Q times before becoming stationary it said to be integrated of 

order Q or I (q). Hence, non stationary time series are those that are integrated of order 1 or greater. 

The null hypothesis for the unit root is: Ho: a = 1; 

The alternative hypothesis is Hi: a < 1. 

We shall test the stationarity of our data using the ADF test. 

Co-integration Test (The Johansen Test) 

It has already been warned that the regression of a non stationary time series on another non stationary time series 

may lead to a spurious regression. The important contribution of the concept of unit root and co-integration is to find 

out if the regression residual are stationary. Thus, a test for co-integration enables us to avoid spurious regression 

situation. If the residuals from the regression are 1(1) or 2(2), i.e. stationary, then variables are said to be co-

integrated and hence interrelated with each other in the long run. This approach is based on conducting unit root test 

on residual obtained from the estimated regression equation. If the residual is found to be stationary at level, we 

conclude that the variables are co-integrated and as such as long-run relationship exists among them. 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

28 

 

tijt

j

i

iit

i

i

tOt TATAwTA 1

11

  








                                                                  9 

Granger Causality Test 

One of the objectives of this study is to investigate the causality between the independent and the dependent 

variables. Granger causality test according Granger (1969) is used to examine direction of causality between two 

variables. Causality means the impact of one variable on another, in other-words; causality is when an independent 

variable causes changes in a dependent variable. The rationale for conducting this test is that it enables the 

researcher to know whether the independent variables can actually cause the variations in the dependent variable. 

Thus, Granger causality test helps in adequate specification of model. In Granger causality test, the null hypothesis 

is: no causality between two variables. The null hypotheses is rejected if the probability of F* statistic given in the 

Granger causality result is less than 0.05.  

The pair-wise granger causality test is mathematically expressed as:  

111

1

11

1

uxYxY t

x
n

i

t

y
n

i

ot  







 
                                                            10 

1
V

1y
xxdp1

n

1i

1Yt
y
1

dp
n

1i
o

dp
t

x 










                                                             11 

Where xt and yt are the variables to be tested white ut and vt are the white noise disturbance terms. The null 

hypothesis
011  yy dp

, for all I’s is tested against the alternative hypothesis 
01 x

 and 
.01 ydp
if the co-

efficient of 

x

1 are statistically significant but that of 
ydp1

 are not, then x causes y. If the reverse is true then y 

causes x. however, where both co-efficient of 

x

1 and 

ydp1 are significant then causality is bi – directional. 

Data analysis method  

The method of data analysis to be used in this study is the simple linear regression using ordinary least square 

method. This approach, which is a quantitative technique, includes tables and the test for the hypotheses formulated 

by using ordinary least square with Econometric View regression analysis at 5% level of significance. 

RESULTS AND DISCUSSIONS 

The tables below have details of the dynamic effect of macroeconomic variables and private investment in Nigeria. 

Table i:   Dynamic Effect of Macroeconomic Variables on Private Investment: Private Real Investment 

Variable Coefficient Std. Error t-Statistic Prob.   

RGDP 2.082194 1.228528 1.694870 0.1073 

OPE 0.174101 0.303387 0.573860 0.5732 

M2 -0.662831 0.227875 -2.908749 0.0094 

INTR 1.370468 0.727841 1.882923 0.0260 

IFR -0.310196 0.216799 -1.430804 0.1696 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

29 

 

FD 1.012108 0.651230 1.554149 0.1376 

EXR -0.167041 0.054051 -3.090413 0.0063 

C 66.88343 23.53590 2.841762 0.0108 

R-squared 0.621715     Mean dependent var 90.00923 

Adjusted R-squared 0.474605     S.D. dependent var 21.47513 

S.E. of regression 15.56606     Akaike info criterion 8.575723 

Sum squared resid 4361.442     Schwarz criterion 8.962830 

Log likelihood -103.4844     Hannan-Quinn criter. 8.687196 

F-statistic 4.226175     Durbin-Watson stat 0.995530 

Prob(F-statistic) 0.006391    

Private equity investment 

Variable Coefficient Std. Error t-Statistic Prob.   

OPE -3.152819 1.298010 -2.428964 0.0252 

RGDP -1.539837 5.232390 -0.294289 0.7717 

M2 -0.935558 0.957003 -0.977592 0.3406 

INTR -2.389049 3.109726 -0.768251 0.4518 

IFR 0.804723 0.897337 0.896790 0.3811 

FD 5.962534 2.779894 2.144878 0.0451 

EXR 1.061988 0.222540 4.772128 0.0001 

C 102.1102 95.58864 1.068225 0.2988 

R-squared 0.758691     Mean dependent var 105.0785 

Adjusted R-squared 0.669788     S.D. dependent var 115.9589 

S.E. of regression 66.63471     Akaike info criterion 11.47752 

Sum squared resid 84363.51     Schwarz criterion 11.86147 

Log likelihood -146.9466     Hannan-Quinn criter. 11.59169 

F-statistic 8.533908     Durbin-Watson stat 0.938425 

Prob(F-statistic) 0.000094    

Source: extract from E-View 9.0 

From model one, the study found that real gross domestic product have positive but insignificant effect, openness of 

the economy have positive and insignificant effect, interest rate have positive and significant effect, financial 

deepening have positive and insignificant effect while interest rate, inflation rate and exchange rate have negative 

effect on private real investment. The coefficient of determination (R
2
) proved that the independent variables can 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

30 

 

explain 62 percent variation on private real investment; the f- statistics found that the model is significant while the 

Durbin Watson statistics proved the presence of serial autocorrelation. 

 The effect of macroeconomic variables on private equity investment was presented in model two. The study found 

that openness of the economy; real gross domestic products, broad money supply, and interest rate have negative 

and insignificant effect on private equity investment except openness of the economy with significant effect. 

Inflation rate, financial sector deepening and exchange rate have positive and insignificant effect on private equity 

investment except financial deepening with significant effect. The R
2
 proved that the independent variables can 

predict 66.9 percent variation on private equity investment. The f- statistics found that the model is significant while 

the Durbin Watson statistics proved the presence of serial autocorrelation. The positive effect of the variables 

confirms the a-priori expectation of the study and various reforms in the Nigerian economy to attract foreign real 

and portfolio investment. Empirically it confirm the findings of Adelowokan Adesoye & Balogun (2015) confirm 

the existence of long run relationship between exchange rate, investment, interest rate, inflation and growth and that 

exchange rate volatility has a negative effect with investment and growth while exchange rate volatility has a 

positive relationship with inflation and interest rate in Nigeria.  Rasaq (2013) that exchange rate volatility has a 

positive influence on GDP, FDI and trade openness with a negative influence on the inflationary rate in the country.  

Ndikumana (2014) that monetary policy affects domestic investment negatively indirectly through the bank lending 

or quantity channel, as well as directly through the interest rate or cost of capital channel. Zulkefly Abdul Karim 

(2010) that the impact of monetary policy channels to the institutions investment are heterogeneous, therefore the 

small institutions that faced financial constraint responded more to monetary tightening as compared to the large 

institution. Tobias and Mambo (2012) that government domestic debt and Treasury bill rate are inversely related to 

private sector investment, while money supply and domestic savings have positive relationship with private sector 

investment consistent with the ISLAM model. 

 

Table ii: ADF Unit Root Test 

Variable  ADF Stat Mackinnon value 

1%         5%              10% 

Prob. Order of 

integration   

Remark  

PRI -4.729164 -3.724070 -2.986225 -2.632604 0.0009 I(I) Stationary 

PEI -1.725512 -3.752946 -2.998064 -2.638752 0.4059 I(0) Not Stationary 

OPE -8.642427 -3.724070 -2.986225 -2.632604 0.0000 I(I) Stationary 

RGDP -6.857014 -3.724070 -2.986225 -2.632604 0.0000 I(I) Stationary 

M2 -5.359404 -3.724070 -2.986225 -2.632604 0.0002 I(I) Stationary 

INTR -4.153126 -3.711457 -2.981038 -2.629906 0.0035 I(I) Stationary 

IFR -5.804288 -3.724070 -2.986225 -2.632604 0.0001 I(I) Stationary 

FD -4.560431 -3.808546 -3.020686 -2.650413 0.0020 I(I) Stationary 

EXR -6.888586 -3.737853 -2.991878 -2.635542 0.0000 I(I) Stationary 

Source: extract from E-View 9.0 

From the table, all the variables are stationery at first difference and integrated in the order of 1(I), we accept 

alternate hypothesis except private equity investment. 

 

 

Table iii: Cointegration test: Trace test: Private real investment  

Hypothesized  Trace 0.05     

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**    



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

31 

 

None *  0.999924  364.1848  125.6154  0.0000    

At most 1 *  0.955999  155.6037  95.75366  0.0000    

At most 2 *  0.885879  86.88569  69.81889  0.0012    

At most 3  0.611499  39.13480  47.85613  0.2549    

At most 4  0.375376  18.33471  29.79707  0.5415    

At most 5  0.301734  7.981377  15.49471  0.4674    

At most 6  0.003628  0.079953  3.841466  0.7773    

Private equity investment 

Hypothesized  Trace 0.05      

No. of CE(s) Eigenvalue Statistic Critical Value Prob.**     

None *  0.994167  337.3117  159.5297  0.0000     

At most 1 *  0.969372  208.7053  125.6154  0.0000     

At most 2 *  0.847933  121.5593  95.75366  0.0003     

At most 3 *  0.638880  74.47340  69.81889  0.0202     

At most 4 *  0.588725  49.00978  47.85613  0.0388     

At most 5  0.451685  26.79747  29.79707  0.1067     

At most 6  0.274106  11.77485  15.49471  0.1680     

At most 7  0.139845  3.766064  3.841466  0.0523     

Source: extract from E-View 9.0 

The table above shows the co-integration results of the variables; it shows at least two cointegrating equations in 

model one and four cointegrating equation in model two. This indicates the presence of long run relationship 

between the variables in the time series. The null hypotheses of no cointegration are rejected and the alternate 

accepted. 

Table iv: Normalized cointegration test: Private real investment  

PEI RGDP OPE M2 INTR IFR EXR  

 1.000000  8.084933 -0.269303  1.239984 -2.571665  0.811905 -0.123045  

  (0.03993)  (0.00413)  (0.00490)  (0.01587)  (0.00395)  (0.00125)  

Private equity investment  

PRI OPE RGDP M2 INTR IFR FD EXR  

 1.000000  0.134291  67.91182  6.059239  24.50820 -6.391699 -18.22613 -2.232333  

  (0.18116)  (1.84531)  (0.23791)  (0.52915)  (0.19363)  (0.54170)  (0.05376)  

Source: extract from E-View 9.0 

From the table, model one found that openness of the economy,  interest rate and exchange rate have negative long 

run effect on private real investment while real gross domestic products, broad money supply and inflation rate have 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

32 

 

positive long run effect on private real investment. Financial deepening was eliminated in the model due to the 

insignificant effect on private real investment in the previous results. Model two revealed that openness of the 

economy, real gross domestic products, broad money supply and interest rate have positive long run effect on 

private equity investment while inflation rate, financial deepening and exchange rate have negative long run effect 

on private equity investment. 

 

Table v: Granger Causality test:  Private real investment 

 Null Hypothesis: Obs F-Statistic Prob.  

 OPE does not Granger Cause PRI  25  3.91712 0.0367 

 PRI does not Granger Cause OPE  1.99542 0.1621 

 RGDP does not Granger Cause PRI  25  2.23044 0.1335 

 PRI does not Granger Cause RGDP  1.47896 0.2518 

 M2 does not Granger Cause PRI  25  0.24676 0.7837 

 PRI does not Granger Cause M2  0.86216 0.4374 

 INTR does not Granger Cause PRI  25  0.70304 0.5069 

 PRI does not Granger Cause INTR  0.72839 0.4951 

 IFR does not Granger Cause PRI  25  0.31345 0.7344 

 PRI does not Granger Cause IFR  0.24184 0.7874 

 FD does not Granger Cause PRI  25  1.95776 0.1673 

 PRI does not Granger Cause FD  0.12008 0.8875 

 EXR does not Granger Cause PRI  25  2.61825 0.0977 

 PRI does not Granger Cause EXR  1.33744 0.2850 

Private equity investment  

 RGDP does not Granger Cause PEI  25  4.89035 0.0187 

 PEI does not Granger Cause RGDP  1.93215 0.1709 

 OPE does not Granger Cause PEI  25  1.97295 0.1652 

 PEI does not Granger Cause OPE  0.40675 0.6712 

 M2 does not Granger Cause PEI  25  1.16322 0.3327 

 PEI does not Granger Cause M2  3.22333 0.0412 

 INTR does not Granger Cause PEI  25  0.19881 0.8213 

 PEI does not Granger Cause INTR  0.38062 0.6883 

 IFR does not Granger Cause PEI  25  0.32198 0.7284 

 PEI does not Granger Cause IFR  6.04796 0.0088 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

33 

 

 FD does not Granger Cause PEI  25  0.04266 0.9583 

 PEI does not Granger Cause FD  0.54564 0.5879 

 EXR does not Granger Cause PEI  22  2.57553 0.1054 

 PEI does not Granger Cause EXR  4.37035 0.0294 

Source: extract from E-View 9.0 

 

From the table, model one proved uni-directional causality from openness of the economy to private real investment. 

Other variables in the model have no causality. Model two have uni-directional causality from real gross domestic 

product to private equity investment, from private equity investment to broad money supply, from private equity 

investment to inflation rate and from private equity investment to exchange rate. 

 

Table vi: Estimated Error Correction Model 

Variable Coefficient Std. Error t-Statistic Prob.   

C 61.67099 6.786586 9.087189 0.0698 

D(PRI(-2)) 1.110710 0.127279 8.726601 0.0726 

D(PRI(-1)) 1.465196 0.206280 7.102945 0.0890 

D(PRI(-3)) -0.531717 0.195600 -2.718394 0.2244 

D(M2(-1)) -2.172671 0.384825 -5.645867 0.1116 

D(M2(-2)) 3.109482 0.332386 9.355034 0.0678 

D(M2(-3)) 10.09627 1.240691 8.137617 0.0778 

D(IFR(-1)) -6.617139 0.795251 -8.320817 0.0761 

D(IFR(-2)) -1.800661 0.243833 -7.384804 0.0857 

D(IFR(-3)) -1.350795 0.279543 -4.832146 0.1299 

D(FD(-1)) -13.76491 1.760872 -7.817095 0.0810 

D(FD(-2)) -33.26903 4.380346 -7.595068 0.0833 

D(FD(-3)) 4.912709 0.355352 13.82492 0.0460 

D(EXR(-1)) -0.238797 0.063297 -3.772648 0.1650 

D(EXR(-2)) -4.052002 0.522179 -7.759791 0.0816 

D(EXR(-3)) -4.437919 0.512569 -8.658191 0.0732 

D(RGDP(-1)) 11.97662 2.858124 4.190378 0.1491 

D(INTR(-1)) 12.43416 1.378582 9.019535 0.0703 

D(INTR(-2)) 6.939735 0.872340 7.955309 0.0796 

D(OPE(-1)) 1.122627 0.189212 5.933158 0.1063 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

34 

 

D(OPE(-3)) -5.109912 0.601508 -8.495164 0.0746 

ECM(-1) -1.540626 0.172322 -8.940399 0.0709 

R-squared 0.999449     Mean dependent var 12.77913 

Adjusted R-squared 0.987874     S.D. dependent var 25.11979 

S.E. of regression 2.766156     Akaike info criterion 3.650343 

Sum squared resid 7.651617     Schwarz criterion 4.736468 

Log likelihood -19.97895     Hannan-Quinn criter. 3.923501 

F-statistic 86.34610     Durbin-Watson stat 1.969954 

Prob(F-statistic) 0.084678    

Private equity investment  

C 13.48995 38.10975 0.353976 0.7412 

D(PEI(-1)) 1.233222 0.778601 1.583894 0.1884 

D(RGDP(-2)) 0.164366 2.357362 0.069725 0.9478 

D(RGDP(-3)) -0.277705 1.797497 -0.154496 0.8847 

D(OPE(-1)) 0.182172 0.456118 0.399395 0.7100 

D(OPE(-2)) 0.072823 0.574272 0.126809 0.9052 

D(OPE(-3)) -0.097954 0.593885 -0.164938 0.8770 

D(M2(-1)) -0.177295 0.609113 -0.291071 0.7855 

D(M2(-2)) -0.279461 0.299462 -0.933210 0.4035 

D(M2(-3)) -0.054750 0.428185 -0.127865 0.9044 

D(INTR(-1)) -0.384271 1.036109 -0.370879 0.7295 

D(INTR(-2)) -0.169910 0.868934 -0.195538 0.8545 

D(INTR(-3)) -0.343503 0.889287 -0.386267 0.7190 

IFR -0.444788 0.516950 -0.860409 0.4381 

FD 0.510249 0.893614 0.570995 0.5986 

EXR -0.148439 0.130436 -1.138019 0.3186 

ECM(-1) -0.039495 0.954548 -0.041376 0.9690 

R-squared 0.909374     Mean dependent var -3.619524 

Adjusted R-squared 0.546868     S.D. dependent var 16.61566 

S.E. of regression 11.18484     Akaike info criterion 7.627815 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

35 

 

Sum squared resid 500.4024     Schwarz criterion 8.473381 

Log likelihood -63.09206     Hannan-Quinn criter. 7.811324 

F-statistic 2.508581     Durbin-Watson stat 2.348027 

Prob(F-statistic) 0.193265    

Source: extract from E-View 9.0 

The Parsimonious error correction model shows that the macroeconomic  variable can explain 99 and 90 % variation 

on the private real and equity investment the model summary shows that the model is significant. However, the 

Durbin Watson statistics justifies that there is no autocorrelation problem among the variables in the time series. The 

macroeconomic variables   shows that narrow money supply is negatively related to the private real and equity 

investment  at lag 1 but positive at lag 2 and lag 3, interest  rate  is negatively related at lag 2 while broad money 

supply is negatively related at lag 1 and positive at lag 2.  Model one found speed of adjustment of  154 percent 

while model two found speed of adjustment of  of3 percent annually. 

CONCLUSION AND RECOMMENDATIONS 

This study investigated the impact of macroeconomic variables   on private investment in Nigeria for the period 

1990 to 2016. As such the study sought to investigate other determinants of private investment in Nigeria.    From 

model one, the study found that real gross domestic product have positive but insignificant effect, openness of the 

economy have positive and insignificant effect, interest rate have positive and significant effect, financial deepening 

have positive and insignificant effect while interest rate, inflation rate and exchange rate have negative effect on 

private real investment. The coefficient of determination (R
2
) proved that the independent variables can explain 62 

percent variation on private real investment; the f- statistics found that the model is significant while the Durbin 

Watson statistics proved the presence of serial autocorrelation. 

 The effect of macroeconomic variables on private equity investment was presented in model two. The study found 

that openness of the economy; real gross domestic products, broad money supply, and interest rate have negative 

and insignificant effect on private equity investment except openness of the economy with significant effect. 

Inflation rate, financial sector deepening and exchange rate have positive and insignificant effect on private equity 

investment except financial deepening with significant effect. The R
2
 proved that the independent variables can 

predict 66.9 percent variation on private equity investment. The f- statistics found that the model is significant while 

the Durbin Watson statistics proved the presence of serial autocorrelation. 

We conclude that macroeconomic variables have significant effect on  private real and equity  investment in Nigeria. 

Recommendations 
There is need to increasing the real interest rate on private savings or household savings so that larger amount of 

income would be saved to accumulate more capital and hence private investment. By this, the higher real interest 

rate would increase private savings which would also increase capital accumulation and hence private investment.  

 

Investment related activities should be encouraged in order to promote private investment. Ensuring macroeconomic 

growth in the economy will undoubtedly enhance investment by the private sector.  Economic policies to reduce 

inflation need to be practiced. In other words Nigerian inflation rate should be kept at a manageable level since 

uncertainty arising from persistent levels of inflation impedes the rate of private investment in the country. This can 

be done by reducing money supply.  

 

Availability of funds ensures an adequate and efficient financial system easing funds from savers to investors that 

can expand the frontier of finance in private investments. There is the need to adopt policies that can encourage 

appreciation of the local currency as depreciation inhibits private investment.  

 

REFERENCES 

Adelowokan O.A, Adesoye A. B., & Balogun O. D (2015). Exchange rate volatility on investment and growth in 

Nigeria, an empirical analysis. Global Journal of Management and Business Research, 5(10),1-11.  

 

Akeju , K., ( 2014). Real Exchange Rates, Terms of Trade and Economic Growth in Nigeria (1980-2012). Journal of 

Economics Theory 8(2), 19-23. 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

36 

 

  

Bakare A.S (2011). The consequences of foreign exchange rate reforms on the performances of private domestic 

investment in Nigeria. International Journal of Economics and Management Sciences,1(1), 25-31.  

 

Chakrabarti, A. (2006). The saving-investment relationship revisited: new evidence from multivariate heterogeneous 

panel cointegration analyses. Journal of Comparative  Economics 34, 402-429.  

 

Clak, J. M., (1917). Business accelerator and the law of demand: A Technical factor in economics cycles. Journal of 

political economy, 25(1), 217-235.  

 

Delke, R. (1996). Savings-investment associations and capital mobility on the evidence from Japanese Regional 

Data. Journal of International Economics, 41, .53-72.  

 

Dixit, A. and  Pindyck, R. (1994), Investment under Uncertainty.Prnceton University Press, New Jersey. Economic 

Research Consortium (A.E.R.C), Research Paper 100.African Economic Research Consortium, Nairobi-

Kenya. 

 

Dixit, A. K. & Pindyck, R. S. (1994). Investment under Uncertainty. Princeton: Princeton University Press.   

 

Feldstein, M. & Horioka, C. (1980): “Domestic saving and international capital flow. Economic Journal,  9(3), 314-

329  

 

Frimpong, Magnus J., Marbuah, George (2010), The Determinants of Private Sector Investment in Ghana: An ARDL 

Approach. European Journal of Social Sciences,Volume 15, Number 2 

Frimpong, Siaw and Adam, A. M. (2010), Does Financial Sector Development CauseInvestment and Growth? 

Empirical Analysis of the Case of Ghana. The Journalof Business and Enterprise Development. 

 

Gomez, M.A (2000).Exchange rate volatility effects on domestic investment in Spain (1980-1998). Anales de 

Economia Aplicada. 

 

Hussein, K.A. (1998). International capital mobility in OECD Countries: The Feldstein-Horioka Puzzle Revisited. 

Economic Letters,5(9), 237-242.  

 

Jansen, W.J. (1996). Estimating saving-investment correlation: evidence for OECD countries based on an error 

correction model. Journal of International Money and Finance,.15(3),749-781.  

 

Jayaraman, T.K. (1996). Private investment and macroeconomic environment in the South pacific island countries. 

A Cross-Country Analysis, Occasional Paper No.14, Asian Development Bank, Manila  

 

Kanagaraj, A., & Ekta, S. (2011). A firm level analysis of the exchange rate exposure of Indian firms. Journal of 

Applied Finance & Banking, 1(4), 163-184.  

 

Kasuga, H.(2004). Savings-investment correlations in developing Countries. Economic Letters,.83,.371-376.  

 

Ketenci, N. (2012). The Feldstein-Horioka Puzzle and structural breaks: evidence from EU Members. Economic 

Modeling. 29 (4), 262-270.  

 

Keynes, J. (1936), The General Theory of Employment; Interest and Money.Cambridge: Cambridge University 

Press, 1972. 

 

Lucky, A. L., & Uzah, C. K., (2016). Determinants of Capital Formation in Nigeria: A Test of Jhingan’s Preposition 

1981 – 2014. IIARD International Journal of Banking and Finance Research, 2 (1), 1 – 19. 

 

Lucky, A. L., & Uzah, C. K., (2017). Monetary Policy Transmission Mechanisms and Domestic Real Investment in 

Nigeria: A Time Series Study 1981-2015. IIARD International Journal of Economic and Financial 

Management, 2 (2), 29 – 59. 



www.acseusa.org/journal/index.php/aijbms     American International Journal of Business and Management Studies   Vol. 1, No. 1; 2019 

37 

 

 

McKinnon, R. I. (1973). Money and capital in economic development. Washington D.C. Brookings Institution.  

 

Narayan, P.K. (2005). The relationship between saving and investment for Japan. Japan and World Economy, .17, 

239-309.  

 

Nazar, D., Bashiri, S (2012). Investigation of the relationship between real exchange rate uncertainty and private 

investment in iran: an application of bivariate generalized autoregressive conditional heteroskedasticity 

(GARCH)-M Model with BEKK approach. African Journal of Business Management, 6(25), 7489-7497. 

  

Ndikumana L. (2014). implications of monetary policy for credit and investment in sub-saharan african countries, 

department of economics and political economy research institute. University of Massachusetts at Amherst.  

 

Ndikumana, Leonce (2000), Financial Determinants of Domestic Investment in Sub-Saharan Africa: Evidence from 

Panel Data. World Development Vol. 28, No. 2,pp. 381-400, Elsevier Science Ltd. 

 

Oshikoya, T. W. (1994). Macroeconomic Determinants of Domestic Private Investment in Africa: An Empirical 

Analysis. Chicago Journals, 42(3), 385-402. 

 

Ozman, E. and Parmaksiz, K. (2003). Policy regime change and the feldstein-horioka puzzle. the UK evidence. 

Journal of Policy Modeling, 25, 137-149.  

 

Payne, J.E. (2005). Savings-investment dynamics in Mexico. Journal of Policy Modeling, .27,525-534.  

 

Pelgrin, F. &  Schich, S. (2008). International capital mobility: what do national saving  investment dynamics tell 

us?. Journal of International Money and Finance, 27,331  

 

Seth, B. (2011). Long run and short run saving-investment relationship in India. RBI Working Paper Series, 

Department of Economics and Policy Research No.13.  

 

 

Singh, T. (2008). Testing the saving-investment correlations in india.  An Evidence from Single-Equation and 

System Estimators. Economic Modeling, 25,1064-1094.  

 

Singha, D. (2002). Saving-investment relationships Japan and other Asian Countries. Japan and World Economy, 

14,1-23.  

 

Tang, C.F. and Lean, H.H. (2008). The savings and investment nexus: evidence from rolling windows bounds test. 

Asian Business and Economics Research Unit, Discussion Paper 66.  

Tobias, O., and Manbo, C., (2012). The Effect of Monetary Policy on Private Sector Investment in Kenya. Journal 

of Applied Finance & Banking, 2(2), 239-287.  

 

 

Copyrights  

Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an 

open-access article distributed under the terms and conditions of the Creative Commons Attribution license 

(http://creativecommons.org/licenses/by/4.0/). 

 

 

 

 

 

 


