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Financial development and the economies of oil-dependent African economies: the case of Nigeria.

INTRODUCTION

Development financial authorities have since endorsed the assertion that no economy of the world can grow or develop to expected heights without t first witnessing a preceding growth and development in its financial sector. Their contention would underscore a very controversial statistical and empirical conclusion that financial development would be causally prior to economic growth and development. Their concern was not much of the reverse causal relationship flowing from economy indicators to financial development variables. These arguments cannot be divorced from the resurgence of the neo-classical thinking and the takeover of the mainstream economic and financial landscape by the monetarists especially since the wake of the 1950s. Another school of thought would not give the financial sector and its indicators a pride of place as the prime movers of economic causation as the development financial school. To this school, factors other than financial development indicators are causal prior to economic growth and development, such as government policy, interest rates, taxation, fiscal deficits/surplus and foreign direct investments. This again lends more to the earlier Keynesian ideology of indirect influence of money on the economy (Ezirim, 2005).

The question still remains: Does the financial sector in any way determine the fortunes of the economy? Does financial development determine economic growth and or development? In what ways or through what avenues does financial development affect the economy? Finance literature appears to have immediate response. For instance, Goldsmith, 1969; Bencivenga and Smith, 1991; and Fry, 1988 maintained that financial development can affect real growth of output from two important avenues: raising the volume of investment and improving the volume and structure of savings. Greenwood and Jovanovic (1990) and King and Levine (1993b) also submits that financial development can affect growth through the vehicle of improving the efficiency of investment by means of profitable project selection, innovation and entrepreneurship growth. Convincing as these theoretical submissions may be, they can only be substantiated by concerted empirical evidences. Several works have been conducted to analyze the relationship between economic growth and financial development in developed economies. Some have also been conducted using data from developing countries. However, empirical evidence from some important developing countries such as Nigeria can be recognized only in their dearth. Thus a study that utilizes data from such developing countries to provide contributory evidence towards the resolution of outstanding controversy as defined above is a step in the right direction.

In a related study, Ezirim, Noi, Muoghalu, and Amuzie (2010) attempted to proffer empirical answer to the question: Does financial deepening cause economic growth and inflation in developing countries? This study aimed at providing evidence towards the ascertainment of the true relationship between financial deepening and economic growth, on one part, and between financial deepening and inflation, on the other, in atypical developing country such as Nigeria. The study utilizes the cointegration and causality procedure to analyze the data generated. The results indicate that financial deepening causes both economic growth and inflation in developing economies. Dual causality exists between financial deepening and economic growth as well as between financial deepening and inflation in these countries. It is noteworthy that the measures of financial deepening in this study relates to the money supply while that of banking development is the total credit to the economy. The use of money supply variable, while appropriate for an investigation of the effects of financial deepening, will not be sufficient to capture the broad spectrum of financial development which finds fuller expression in the financial markets' activities. Thus a model that captures the relationship between the GDP and the money supply would be considered restrictive to examine the aggregate effects of financial development on the general growth and development of the economy. The financial market window would be more appropriate.

Thus it behooves us in this study to investigate the relationship between financial development (through the financial market window) and economic growth in Nigeria using data from 1970 through 2008. More specifically, this study will attempt to analyze the relationship between economic growth and financial development variables of financial markets' operations and banking development operations. It will also determine the relationship between these financial development variables and inflationary trends in the economy. This second objective is expected to provide added information to the body of existing literature since the studies reviewed failed to address the problem of inflation in relation to financial sector operations. In order to document our study, the resultant paper is divided into five areas. Section two contains the review of related literature. The third section houses the methodology and data descriptions. The fourth section contains the analysis of estimation results and attendant discussion. The final section is the concluding remarks.

REVIEW OF SOME THEORETICAL AND EMPIRICAL ISSUES

Many authors have provided a strong backdrop for further studies on the subject of financial development and the economy. For instance, Goldsmith (1969), Fry (1988), and Bencivenga and Smith (1991), believed that by raising the volume of investment improving the volume and structure of savings, financial development can affect real growth of output. On the other hand, Greenwood and Jovanovic (1990) and King and Levine (1993), maintained that financial development is likely to affect growth by improving the efficiency of investment through project selection, innovation and entrepreneurship growth. For Zhang, Wan, and Jin (2007) the financial intermediation-growth nexus is a widely studied topic in the literature of development economics, where deepening financial intermediation may promote economic growth by mobilizing more investments, and lifting returns to financial resources, which raises productivity. Hannoun (2008) argued that deep and strong financial markets are important because of the need for market-based and diversified channels of intermediation between borrowers and investors. Based on this premise he lamented that while Asian economies have made impressive strides in financial deepening, their patterns of capital flows suggest that much financial intermediation is still being carried out at great cost abroad. Meaningful financial deepening will bring some of that intermediation home.

Levine (1997) argues that the preponderance of theoretical reasoning and empirical evidence suggests a positive first order relationship between financial development and economic growth. There is evidence that the financial development level is a good predictor of future rates of economic growth, capital accumulation, and technological change. Moreover, cross-country, case-style, industry-level and firm-level analyses document extensive periods when financial development crucially affects the speed and pattern of economic development. The author explains what the financial system does and how it affects, and is affected by, economic growth. Theory suggests that financial instruments, markets and institutions arise to mitigate the effects of information and transaction costs. A growing literature shows that differences in how well financial systems reduce information and transaction costs influence savings rates, investment decisions, technological innovation, and long-run growth rates. A less developed theoretical literature shows how changes in economic activity can influence financial systems. The author advocates a functional approach to understanding the role of financial systems in economic growth. This approach focuses on the ties between growth and the quality of the functions provided by the financial systems. The author discourages a narrow focus on one financial instrument, or a particular institution. Instead, the author addresses the more comprehensive question: What is the relationship between financial structure and the functioning of the financial system?

Wikipedia (2010) affirms that financial development or deepening plays an important role in reducing risk and vulnerability for disadvantaged groups, and increasing the ability of individuals and households to access basic services like health and education, thus having a more direct impact on poverty reduction. Wang (2010) posed a very important question at a blog maintained by the Growth and Crisis (GC) Program of the World Bank Institute relating to why financial deepening is not happening in the poor countries? It was reported that while global financial integration has been progressing well, financial development or deepening is not. Only a handful of emerging market economies are benefiting from large capital inflows in the form of FDI, for instance. In countries like Kenya where the capital account is open and foreign bank entry has long been allowed, capital market remains shallow and real interest rate remains high, hindering the private sector development. Why is there a weak association between financial openness and financial deepening in the poor countries?

Wang (2010: 1) in response related that "in a November conference, Professors Ju and Wei (2006) seemed to have provided an answer". In their paper "A solution to two paradoxes of international capital flows", they Ju and Wei (2006) provided a framework to study the role of financial and property right institutions in determining patterns of capital flows. Their two-sector model features differentiating returns to financial investment and physical investment, as financial investors have to share the return to capital with entrepreneurs. The more developed a financial system is, the greater the slice that goes to the financial investors. As an implication, a poor country with an inefficient financial sector may experience a large outflow of financial capital, but together with inward FDI, resulting in a small net inflow. The model also incorporates property rights protection as another institution. Countries with poor property rights protection may well experience an outflow of financial capital without a compensating inflow of FDI.

As we earlier stated, Kiyotaki and Moore (2005) developed a model of financial deepening, based on the distinction between limited bilateral commitment and limited multilateral commitment. Their model borrowed from the model of money and liquidity they developed in Kiyotaki and Moore (2004) to explore the impact of financial deepening. In that paper they drew a distinction between two aspects of financial contracting: bilateral commitment versus multilateral commitment. On the one hand, there may be a limit on how much a private agent can credibly promise to repay someone who provides finance: that is, the degree of bilateral commitment a borrower can make to an initial lender when selling a paper claim. On the other hand, there may be a limit on the extent to which the initial lender can resell the paper to someone else in a secondary market: in effect, the degree of multilateral commitment the borrower can make to repay any bearer of the claim.

Based on these pedestals, Kiyotaki and Moore (2005), explored the effects of secular changes in financial depth on investment and output; on intermediation and interest rates; on the long-run velocities of circulation of different monetary instruments, and the use of outside money; on the patterns of saving and trade in paper. Three stages of financial development are identified. The international evidence provided by Bordo and Jonung (2003) and others suggests that over the long-run, presumably as a result of financial development: (i) the value of money divided by output--the money/output ratio (the inverse velocity)--is hill-shaped; and (ii) the broader is the monetary instrument, the later the peak of the hill arrives. With [theta] and [phi] taken as indices of financial development, our model makes the same predictions. The parameter [theta] in part reflects the legal structure and contractual redress available to a creditor in the event of default. In this sense, [theta] provides one simple measure of financial depth, capturing the degree of "trust" in the economy. The costs of conversion are indexed by a parameter [phi], which, like [theta], is taken to lie between 0 and 1. The higher is [phi], the less costly is conversion. Taking [phi] to be another index of financial depth, we will be investigating the effects of an exogenous rise in [phi].

Townsend and Ueda (2005) proposed a coherent unified approach to the study of the linkages among economic growth, financial structure, and inequality, in an attempt to bringing together disparate theoretical and empirical literature by showing how to conduct model-based quantitative research on transitional paths. Thus, they proposed a quantitative research methodology consistent with the widely held view that financial deepening and changing inequality, along with economic development, are transitional phenomena. They also showed that, consistent with this view, simple regression studies would not be able to capture the true linkages among growth, financial deepening, and inequality. With analytical and numerical methods, they calibrated and made tractable a prototype canonical model and applied to Thailand 1976-96 annual data; an application to an emerging market economy in a phase of economic expansion with uneven financial deepening and increasing inequality. They looked at the expected paths generated by the model, which were broadly consistent with the 1976-1996 averages in the data, and with the time trends of the data, especially for increasing inequality and financial deepening. By changing parameters as in the paper's robustness checks, the authors matched GDP growth as well, but then actual financial depth and inequality were low compared to the model prediction. They thus concluded that the model is a useful starting point for studying these phenomena.

Early empirical works focusing on economic growth and financial structure including the works of Goldsmith (1969), Shaw (1973), and McKinnon (1973), and more recent King and Levine (1993) and Amusa (2010) established that financial deepening is at least an intrinsic part of the growth process and may be causal--that is, repressed financial systems harm economic growth. By implication, financial development given impetus by financial depth would boost economic growth. In turn, Acemoglu and Zilibotti (1997) showed that capital accumulation is associated with increasing intermediation and that better diversification, which comes with higher levels of wealth, reduces the variability of growth. Likewise well known are landmark contributions on growth and inequality. Kuznets (1955) posited that growth is associated with increasing and eventual decreasing inequality. Interest and controversies, especially with respect to cross-country regressions, have continued ever since. Forbes (2000), confirms previous regression studies that high (initial) inequality is associated with low subsequent long-run growth but finds that the relationship is the opposite for the medium term. Resting separately from this strand of the empirical literature are the deservedly well-known theoretical contributions more motivated by Kuznets's original assertion that growth may bring increasing, and eventually decreasing, inequality--namely, Aghion and Bolton (1997), Piketty (1997), Banerjee and Newman (1993), and Lloyd-Ellis and Bernhardt (2000).

Beck, Levine, Ross, and Loayza (1999) investigated the issue of finance and the sources of growth. The authors evaluate whether the level of development in the banking sector exerts a causal impact on economic growth and its sources-total factor productivity growth, physical capital accumulation, and private saving. They use (1) a pure cross-country instrumental variable estimator to extract the exogenous component of banking development and (2) a new panel technique that controls for country-specific effects and endogeneity. They find that: Banks do exert a large, causal impact on total factor productivity growth, which feeds through to overall GDP (Gross Domestic Product) growth. The long-run links between banking development and both capital growth and private savings are more tenuous.

Darrat, Abosedra, and Aly (2005) assessed the role of financial deepening in business cycles from the experience of the United Arab Emirates. In the paper, the relation between financial market development and the severity of business cycles in the economy of the United Arab Emirates is investigated. No evidence is found of a dampening effect from financial deepening on cyclical fluctuations in the short-run, but strong effects in the long-run. These results extend recent findings on the financial development/economic growth nexus and imply that growth volatility reductions expected from further financial developments are slow to materialize especially in countries with relatively large and well-functioning financial sectors.

Alberola and Salvado (2006) developed a model for banks, remittances, and financial deepening in receiving countries against the back-drop that a remarkable fact of the mushrooming remittances market is the absence of commercial banks as relevant players. Furthermore, remittances have been identified as a potential catalyst for the financial deepening of receiving countries through higher access to banking services by migrants' families. Building upon these features, this paper sets up a two-period financial model of remittances without uncertainty. The formulation acknowledges, on the one hand, the altruism component of remittances sent by migrants to their families and, on the other hand, the dominant position of Money Transfer Operators (MTO's) due to migrants' mistrust to banks, which hinders the access of banks to the market. Altruism compounded with a non-competitive market allows MTO's to set excessively high remittance fees and to attain monopolistic rents. The model shows that banks can challenge this position thanks to their role as providers of remunerated saving and credit, which enables them to overcome the competitive disadvantage derived by migrants' mistrust. Notwithstanding this, the main positive impact of banks' entry is attained through higher competition, not through the provision of financial services. All in all, the entry of banks reduces the fees and increases the level of remittances, allows an optimal consumption smoothing and improves the welfare of migrants and their families, although it also increases the volatility of remittances.

METHODOLOGY

The Models

Ezirim, Noi, Muoghalu and Amuzie (2010) in an earlier paper on the effect of financial deepening hypothesized that economic growth (EC) is a positive function of financial deepening (FD) and banking development (BD), ceteris paribus. Functionally, the modeled that

ECt = f(FB, BD); f1 > 0; f2 > 0 (1)

In the analysis using the above model, the money supply was employed as proxy for financial deepening, while the total credit of banks to the economy represented the banking development variable. Following such theorizing, it behooves us also to propose that economic development (ED) or growth (EC) is a positive function of financial development (FD) and banking development. Thus

ECt = f(FD, BD); f1 > 0; f2 > 0 (2)

Financial development (FD) can be defined in terms of the contributions of the financial markets in terms of the operations in the economy, ie, the financial market angle. The main facets of the financial markets are the money market (MMKT), capital market (CMKT), mortgage market (MGMK), and foreign exchange market (FOREX). Thus we can re-write expression 2 to become

FDt = f(MMKT, CMKT, FOREX, MGMK, BD); fi > 0 (3)

FD or EC can be proxied by the gross domestic product (GDP). The nominal GDP is hereby utilized since like the other variables it has not been corrected for the effects of inflation. The mortgage market (MGMK) activities can be seen as major activities of banks in their bid to assist in developing the economy through the extension of credits to the housing subsector. Thus, as a major argument in the banking development (BD) variable, we assume that much, for the purpose of this paper. To further clarify, the paper utilizes the total credit granted (TCRE) to the economy to represent the banking development variable which as explained above incorporates the mortgage market activities. This will reduce expression (3) above to

GDPNt = f(MMKT, CMKT, FOREX, TCRE); fi > 0 (3a)

Expression reads: the total output of the country will be a positive function of financial development (represented by the activities in the money market, capital market, foreign exchange market), and banking development (represented by the total credit of banks to the economy). This argument supports previous theories and empirical studies reviewed earlier in this paper.

Furthermore, as in Ezirim, 'Noi, Amuzie and Muoghalu (2010), another important indicator of the economy that poses serious worry to economic and financial managers is the spate of inflationary pressures. The role of money and monetary variables such as we are investigating, in part, is always associated with inflationary conditions to the point that the monetarist would postulate that inflation is and always a monetary phenomenon. This being the case, we can hypothesize that inflation represented by the consumer price index (CPI) would be a positive function of the financial development and banking development variables. Thus we can write that

CPI = f(MMKT, CMKT, FOREX, TCRE); fi > 0 (4)

With expressions (3a) and (4) applied to relevant data, we can attempt to investigate the relationship between financial development, economic development or growth, and inflation.

Estimation Procedure

This study employs the Augmented Dickey-Fuller (ADF) and Phillip-Perron unit root test techniques to determine the stationarity status of the time series macroeconomic variables. The ADF is based on the hypothesis of [[delta].sub.o] = 0 against the alternative of [[delta].sub.o] < 1 and is given as:

[DELTA][X.sub.t] = [[beta].sub.0] + [[beta].sub.1]t + [[delta].sub.o] [X.sub.t-1] + [p.summation.over (t=1)][y.sub.t] [DELTA][X.sub.t-1] [[epsilon].sub.t]; t = 1, 2.... T (5)

where [DELTA][X.sub.t] is the first difference operator, [[beta].sub.o] denotes the inclusion of an intercept, [X.sub.t-1] is the lag of the dependent variable while [DELTA][X.sub.t-1] is the difference of the lagged dependent variable, [Y.sub.t] is a vector of the independent variable and t a time trend, is [[epsilon].sub.t] the error term and p is the optimal lag lenght. The ADF incorporates lagged values of the dependent variable in the regression model to ensure that the error term ([u.sub.t)] is not autocorrelated. This also ensures that [u.sub.t] is a white noise process. The Akaike Information Criteria is used to select the maximum lag length.

The relationship between economic growth and financial development variables on one part, and between inflation and the financial development variables on the other, as in expressions (3) and (4) above can be re-specified as a vector autoregressive (VAR) model as follows:

[EG.sub.t] = [mu] + [n.summation.over (K=1)] [X.sub.k] [Y.sub.t-k] + [[mu].sub.t] (6)

Where [mu] is a constant, Y is a vector of the financial development predictors, [X.sub.k] are coefficients to be estimated and [u.sub.t] is a white noise disturbance term with E([u.sub.it)] = 0, i = 1,2, E([u.sub.1t] [u.sub.2t]) = 0. Similarly, with inflation as the explained variable, the VAR equation above changes to

[CPI.sub.t] = [mu] + [n.summation.over (K=1)])] [X.sub.k] [Y.sub.t-k] + [[mu].sub.t] (7)

The terms are as earlier defined. However, the VAR model is deficient in estimating long run relations between the economy indicators (economic growth and inflation) and the financial development indicators. To determine the long run relationship, the paper utilizes the Johansen and Juselius (1990) and Johansen (1991) cointegration procedure. The cointegration test is based on the following vector error correction model

[DELTA][Y.sup.t] = [mu] + [[GAMMA].sub.1] [DELTA][Y.sub.t-1] + [[GAMMA].sub.2] [DELTA][Y.sub.t-2] + [[GAMMA].sub.3] [DELTA][Y.sub.t-3] + [[GAMMA].sub.4] [DELTA][Y.sub.t-4]....[[GAMMA].sub.k-1] [DELTA][Y.sub.t-k+1] + [[PI]y.sub.t-k] + [u.sub.t] (8)

Where; [[DELTA]Y.sup.t]is the first difference of the dependent variable while (GAMMA) and (PI) are 4x4 matrices and k is the lag length. By interpretation, this VAR contains g variables in first differenced form on the LHS, and k-1 lags of the dependent variable (differences) on thr RHS, each with a [GAMMA] coefficient matrix attached to it. The Johansen cointegration centers on the examination of the [PI] matrices, which are the long-run coefficient matrices since in equilibrium all the [[GAMMA].sub.1] [[DELTA]Y.sub.t-1] will be zero, and setting the error term, [u.sub.t] to their expected values of zero will leave [[PI]y.sub.t-k] = 0 (Brook, 2008). In the present analysis, the tests used here involve cointegration with linear deterministic trend in the vector autoregression (VAR). The tests were conducted at the 1% and 5% significance levels. To determine the causal implications, the study utilized the causality test developed by Granger.

The Data

For the purposes of this study, use is made of annual Nigerian time-series data from 1970 through 2008. The data are obtained from the Statistical Bulletin of the Central Bank of Nigeria and the Nigeria Stock Exchange Fact Books for various years. Descriptive statistical representation of the nominal data is summarized on Table 1. The nominal (as opposed to real) GDP (NGDP), recorded a skewness of 5.159 and kurtosis of 29.76 showing a Jarque-Bera statistic of 1268.09 which was significant at 1% level. The money market data recorded skewness of 1.83, kurtosis of 5.22 and a Jarque-Bera statistic of 28.15 which was significant at 1% level.

The capital market variable showed a 0.51 skewness, 2.596 kurtosis and Jarque-Bera statistic of 1.86, which was not significant at the conventional levels. The foreign exchange market recorded a skewness of 2.64, kurtosis of 9.76 and Jarque-Bera of 113.23 that was significant at 1% level. The total credit variable displayed a skewness of 2.39, kurtosis of 7.99, and Jarque-Bera of 73.63 which was significant at 1% level. The graphical representation of the variables is done in Figure 1. As shown, the capital market variable fluctuated more violently that the other variables followed by the GDP variable. The money market, foreign exchange market, and the total credit variables maintained somewhat consistently increasing distributions during the period under consideration.

[FIGURE 1 OMITTED]

ESTIMATION RESULTS AND ANALYSIS

Table 2 depicts the results of the unit root tests conducted using the augmented Dickey-Fuller and Phillip-Perron tests with the assumption of intercept without trends at 1%, 5%, and 10% significance levels. As indicated, only the capital market variable was not stationary at level data but attained stationarity after first differencing in both tests. On the other hand, all the other variables attained stationarity at the level data. When all the variables were first-differenced, they became stationary at I(1). As a rule, in situations where all the variables are not I(0) but I(1), cointegration tests can be applied, otherwise VAR can be used. In the present case, the variables all 'converged' at so the Johansen and Jusellius Cointegration procedure is applied to check whether the variables are cointegrated.

The results of the Johansen and Jusellius test for cointegration between economic development and financial development (and, of course, banking development) are summarized on Table 3. The critical assumption was that of linear deterministic trend in the data Series, namely DGDPN DMMKT DCMKT DTCRE DFOREX. From the Table 3, it can be seen that economic development variable, GDPN is cointegrated with the financial development (DMMKT DCMKT DTCRE DEXRT) at 1% significance level. This indicates that a long-run equilibrium relationship exists between economic development and financial development in Nigeria.

Causality Implications

The Granger causality test was employed and results shown on Table 4. For the Table, it can be seen that there a dual causality between the economic development variable (DGDPN) and money market operational performance variable (DMMKT). It can then be inferred that money market operations in Nigeria cause economic development and vice versa. Table 4 also reveals that there is bi-causation thesis existing between the economic development variable (DGDPN) and the banking development variable (DTCRE). This implies that banking development would give rise to economic development and vice versa.

CONCLUDING REMARKS

This critical research questions which this study seeks to answer relates to: Does financial development and Banking Development cause economic growth and Development in developing countries? Does financial development and Banking Development cause inflation in developing countries? Based on these questions, it was the crux of the study to x-ray and provide empirical evidence about the true nature and magnitude of relationships between financial development and economic growth and development, on one part, and between financial deepening and inflation, on the other, in a typical developing country such as Nigeria. The second question was not addressed in this paper, while the present study covered the first research question. Since banks occupy a very important position in the financial horizon of developing countries, a measure of banking development was added to the model to capture the length and breadth of financial development. Again, the operation of the mortgage market industry in Nigeria is essentially bank-based. So the operational performance was seen as subsumed in the bank credit market.

The study constructed simple econometric models and utilized the cointegration and causality procedure to analyze the secondary data generated from the publications of the Central Bank of Nigeria. The findings show that not all variable were stationary at level data but all attained stationarity at first-differenced data. The variables were cointegrated at 1% level. Bi--causation exists between economic development and money market operational performance. Bi-causation exists between economic development and banking development. No significant causality between economic development and capital market and foreign exchange market. The money market, and the banking development and by default, the mortgage market affects economic development significantly. There existed long-run equilibrium relationship between economic development and financial development; and between banking development and economic development. This contradicts the findings of an earlier study by the authors, where banking development was not causally prior to economic growth. The monetary authorities should be proactive in policy formulation and engage in such policies that will encourage both the financial market

and banking credit market in their intermediation roles. Such policies that encourage these variables will surely go a long way to encouraging economic development in Nigeria as well as other developing countries which possess similar characteristics as Nigeria, such as over-dependence on oil.

LIMITATIONS OF STUDY

The study used nominal data only that have not been purged of the effects of inflation. Real data can be useful especially when investigating the relationship between GDP and the financial and banking development variables. This may not be readily imperative when investigating their effects on inflation. It is the need to keep all variables at the same pedestal for ease of analysis that we have utilized the nominal values of the variables in all cases. Again we have assumed that banking credits to the economy would adequately cover the effects of the mortgage markets and banking developments. This is partly because of the dearth of adequate data on the mortgage market activities in Nigeria. It is the thinking of the authors that the analysis could be more robust where these data can be obtained.

SUGGESTIONS FOR FURTHER RESEARCH

It is clear that the present paper has been limited to analysis of the Nigerian data as a proxy for all dependent economies. It is recommended that further analyses be done using data from more dependent economies of Africa and Asia. In doing so the findings would be more robust to make more realistic conclusions about the relationship between financial development and economic development. Further the need to compare the findings from the developing economies and those of the developed economies is rife and is hereby suggested as a problem for further inquiry.

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Chinedu B. Ezirim

Ben 'Noi

University of Port Harcourt, Nigeria

Edith Azuka Amuzie

Ministry of Finance, Imo State, Owerri

Michael Muoghalu

Pittsburg State University Kansas

Chinedu B. Ezirim is a Professor of Finance, University of Port Harcourt, Nigeria. He is Member of the Academic Board of the European Business Competence* License that sits in Germany and Austria. He is a Distinguished Fellow of the International Academy of Business and Behavioral sciences, USA.

Ben Noi is in the Department of Finance and Banking, University of Port Harcourt, Nigeria. He is a Service Fellow of the International Academy of Business and Behavioral sciences, USA.

Edith A. Amuzie is a Director in the Office of Accountant General, Ministry of Finance, Owerri, Imo State Nigeria. She is a doctoral candidate at the Department of Finance, University of Port Harcourt.

Michael Muoghalu is a Professor of Finance and MBA Program Director, Pittsburg State University, Kansas.
Table 1
Descriptive Statistics of Data for Estimation

STATISTIC      GDPN       MMKT       CMKT       FOREX      TCRE

Mean           3749487.   216835.8   10978.34   198371.6   299062.2
Median         275198.2   33751.70   10129.80   23249.00   20044.90
Maximum        65850229   1310480.   25316.00   1739637.   2524298.
Minimum        5203.700   892.9000   934.6000   341.6000   391.4000
Std. Dev.      10931031   348947.6   6510.774   384168.1   599089.8
Skewness       5.158787   1.825833   0.510055   2.637023   2.392409
Kurtosis       29.75988   5.218851   2.596320   9.755110   7.986678

Jarque-Bera    1268.087   28.14771   1.855525   113.2309   73.63221
Probability    0.000000   0.000001   0.395438   0.000000   0.000000

Observations   37         37         37         37         37

Table 2
Augmented Dickey--Fuller and Phillip-Perron Unit Tests Result
(Intercept without trends)

Series      ADF statistic     1%    5%    10%

GDPN        -3.25           -3.62 -2.95 -2.61
D1(GDPN)    MMKT
-6.98
D(MMKT)     FOREX
4.17
D(FOREX)    CMKT
-1.9
D(CMKT)     -4.88           -4.24 -3.54 -3.20
TCRE        5.69
D(TCRE)
Series      P-P statistic     1%     5%   10%

GDPN        -5.06           -3.62 -2.95 -2.61
D1(GDPN)    MMKT
-13.17
D(MMKT)     FOREX
14.76
D(FOREX)    CMKT
-1.79
D(CMKT)     -6.09           -4.24   -3.54 -3.20
TCRE        32.21
D(TCRE)

Table 3 Cointegration Results under Linear Deterministic Trend in
The Data Series: DGDPN, DMMKT, DCMKT, DTCRE, and DFOREX. Lags
interval: 1 to 1

                 Likelihood  5 Percent  1 Percent  Hypothesized

Eigenvalue       Ratio       Critical   Critical   No. of CE(s)
                              Value      Value
0.977479         227.4938    68.52      76.07      None **
0.783162         98.52142    47.21      54.46      At most 1 **
0.507486         46.54887    29.68      35.65      At most 2 **
0.362108         22.46894    15.41      20.04      At most 3 **
0.190440         7.182999    3.76       6.65       At most 4 **
DGDPN            DMMKT       DCMKT      DTCRE        DFOREX     C
1.000000         445.6895    185.1141   640.8366   -1554.954 8151718.
                 (123.996)   (262.084)  (139.983)  (373.218)

Log likelihood   -2054.090

Table 4
Pairwise Granger Causality Tests
DGDPN, DMMKT, DCMKT, DTCRE, and DFOREX.

Lags: 2

Null Hypothesis:                     Obs   F-Statistic   Probability
DMMKT does not Granger Cause DGDPN   34      5.10331       0.01262
DGDPN does not Granger Cause DMMKT           10.1088       0.00047
DCMKT does not Granger Cause DGDPN   34      0.04454       0.95650
DGDPN does not Granger Cause DCMKT           0.31197       0.73443
DTCRE does not Granger Cause DGDPN   34      3.88610       0.03197
DGDPN does not Granger Cause DTCRE           36.0829       1.4E-08
DFOREX does not Granger Cause DGDPN  34      2.04218       0.14799
DGDPN does not Granger Cause DEXRT           0.27616       0.76066
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Author:Ezirim, Chinedu B.; 'Noi, Ben; Amuzie, Edith Azuka; Muoghalu, Michael
Publication:International Journal of Business and Economics Perspectives (IJBEP)
Article Type:Report
Geographic Code:6NIGR
Date:Dec 22, 2011
Words:6376
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