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\title{Econometrics Analysis of Financial Development and Economic Growth: Evidence from Nigeria}
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             \author[1]{Godwin Chigozie  Okpara}

             \affil[1]{  Abia State University Uturu}

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\date{\small \em Received: 16 December 2017 Accepted: 2 January 2018 Published: 15 January 2018}

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\begin{abstract}
        


This work explored the relationship between financial development and economic growth in Nigeria. Specifically it investigated the extent to which financial development engenders economic growth. It also verified the existence of supply leading and/or demand following hypotheses in Nigeria. To evaluate these, the researchers firstly determined the stationarity of the variables which informed the use of co integration and then the vector error correction model to finding the long run impact of financial development variables on the growth of the economy. The diagnostic test was employed to determine the authenticity and stability of our model. The researchers also employed the Granger Causality test to investigate the existence of supply leading and/or demand following hypothesis. The results of the analyses show that there is a long run relationship between financial development and economic growth in Nigeria and that besides the metric for banking system financing of the economy variable which is significantly inadequate, all other financial development indicators engender economic growth. Our diagnostic test shows that the model is adequate, plausible, and stable. The short run causality test shows bidirectional causality between capital market liquidity or economic volatility and the growth of the economy while market capitalization ratio, broad money velocity and the banking system rate of financing the economy drive economic growth with no feedback effect. On the basis of the findings, the researchers call on the government to articulate reform packages (such that may involve vigorous financial inclusion) capable of enhancing the banking sectors? involvement in the financing of the economy so as to achieve enormous economic growth. 

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\keywords{financial development, economic growth, supply leading, demand following hypotheses, cointegration, }

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\section[{Econometrics Analysis of Financial Development and Economic Growth:}]{Econometrics Analysis of Financial Development and Economic Growth:}\par
Evidence from Nigeria Godwin Chigozie Okpara ? , A. N. Onoh ? , B. M. Ogbonna ? , Eugene Iheanacho ? \& Iheukwumere Kelechi. J ¥ Abstract-This work explored the relationship between financial development and economic growth in Nigeria. Specifically it investigated the extent to which financial development engenders economic growth. It also verified the existence of supply leading and/or demand following hypotheses in Nigeria. To evaluate these, the researchers firstly determined the stationarity of the variables which informed the use of co integration and then the vector error correction model to finding the long run impact of financial development variables on the growth of the economy. The diagnostic test was employed to determine the authenticity and stability of our model. The researchers also employed the Granger Causality test to investigate the existence of supply leading and/or demand following hypothesis. The results of the analyses show that there is a long run relationship between financial development and economic growth in Nigeria and that besides the metric for banking system financing of the economy variable which is significantly inadequate, all other financial development indicators engender economic growth.\par
Our diagnostic test shows that the model is adequate, plausible, and stable. The short run causality test shows bidirectional causality between capital market liquidity or economic volatility and the growth of the economy while market capitalization ratio, broad money velocity and the banking system rate of financing the economy drive economic growth with no feedback effect. On the basis of the findings, the researchers call on the government to articulate reform packages (such that may involve vigorous financial inclusion) capable of enhancing the banking sectors' involvement in the financing of the economy so as to achieve enormous economic growth. 
\section[{Introduction a) Financial Sector and Economic Growth and}]{Introduction a) Financial Sector and Economic Growth and}\par
Development he financial sector plays important role in economic growth and development through the process of financial intermediation. The sector's role in influencing the savings-investment process for acceleration of the rate of economic growth and poverty reduction cannot be overemphasized. \hyperref[b31]{Robinson (1954)} refers financial sector to as the handmaiden of economic development. \hyperref[b18]{McKinnon (1973)} contended that the financial sector can be more than a handmaiden to the real economy as it is the major driver of economic growth and development especially when it is liberalized. Williams Amaha (1988) added that if financial sector is free, it can provide the necessary fillip for economic growth and development. \hyperref[b16]{Levine et al (2000)} also asserted that the growth of components of financial intermediation engenders positive growth in the real sector of the economy.\par
World \hyperref[b40]{Bank (2009)} asserts that the financial sector is the brain of the economy and that when it functions properly, it allocates resources to the most productive and efficient uses. In the words of \hyperref[b32]{Sanusi (2002)}, well-functioning financial systems are able to mobilize household savings, allocate resources efficiently, diversify risk, enhance the flow of liquidity, reduce information asymmetry and transaction cost and provide an alternative to raising funds through individual savings and retained earnings. The question of whether Nigerian financial system is actually well functioning gives a food for thought. Furthermore, \hyperref[b38]{Soludo (2009)} maintains that in terms of policy thrust, the banking sector reforms are expected to build and foster a competitive and healthy financial system to support development and to avoid systematic distress.\par
The stock market on the other hand is vital for the provision of investible funds and requires the participation of the key economic agents whose participation in Nigerian capital market seems passive rather than active. For instance a look at the participants in the stock market reveals that the leading sub-sector on the stock exchange is banking sector which accounts for about 50\% of the total equity market capitalization. Each of the other sectors such as breweries and building materials takes a proportion which is at most 10 percent. Engineering technology has 0\% of the total market capitalization. Heavy industries especially in the area of technology are virtually inactive or non-existent in the stock market \hyperref[b29]{(Onwumere and Modebe, 2007)}. These heavy industries are the core of industrial production and their products add heavily to the gross domestic product  {\ref (NSE Fact Books 2005}. Invariably, the non-participation of 
\section[{Year ( )}]{Year ( )}\par
C these core industries in the stock market may have negative implications on the Nigerian stock market development and the economy at large. \hyperref[b37]{Schumpeter (1911)} posited that financial intermediation through the banking system played a pivotal role in economic development by affecting the allocation of savings thereby improving productivity, technical change and the rate of economic growth. By mobilizing savings from the surplus unit to the deficit unit who are desirous for productive investment, capital inflow are facilitated. The financial markets not only stimulate investments in both physical and human resources but also channel savings to more productive uses by collecting and analyzing information about investment opportunities  {\ref (Jalloh, 2009)}. In the words of \hyperref[b33]{Sanusi (2011)}, Well functioning financial system are able to mobilize household savings, allocate resources efficiently, diversify risk, enhance the flow of liquidity, reduce information asymmetry and transaction costs and provide an alternative to raising funds through individual savings and retained earnings.\par
Finance is a life wire of every productive activity. Public as well as private sector operators need various financial instruments to enable them invest in short as well as long term investment. The financial market is divided into two -the money market and the capital market. The money market is a market where short term securities are traded while long term securities are traded in the capital market. Included in the money market are financial access that are short term, highly marketable and accordingly possess low risk and high degree of liquidity. The markets facilitate trading in short term debt instruments to meet short term needs of large users of funds. Government raises short term funds from the money market to finance its short term investment. The capital market on the other hand is a market which provides industrial and commercial firms with long term finance for their capital developments. Capital market therefore, adds to the stock of capital and generates capital formation for new investments .\par
Government raises long term funds from the capital market to finance its long term investment projects such as social overheads like public hospital, construction of roads, airports, public schools, dam construction etc. Corporations issue corporate bonds to finance long term development projects like construction of new plants, new buildings, new technology and expansion of existing ones while issuing equities to raise additional financial resources for long term investments. Thus wealth creation for economic growth is facilitated by the financial markets. 
\section[{II.}]{II.} 
\section[{Literature Review a) Financial Development and Economic Growth}]{Literature Review a) Financial Development and Economic Growth}\par
Patrick (1966) quoted in Isu and Okpara (2013) identified two possible causal relationships between financial development and economic growth. They include demand following hypothesis which sees the demand for financial services as dependent upon the growth of real output and the commercialization and modernization of agriculture and other subsistence sectors. In other words, it posits a unidirectional causation from economic growth to financial development.\par
In support of supply leading hypothesis, Clarke, (2002) and \hyperref[b13]{Jayaratne and Strahan (1996)} concluded that banking sector development following deregulation has led to state level economic growth in the U.S. \hyperref[b39]{Wadud (2005)} in his study of long-run causal relationship between financial development and economic growth for 3 South Asian countries namely India, Pakistan and Bangladesh disaggregated financial system into "bank-based" and "capital market based" categories and by employing a cointegrated vector autoregressive model found that the results of error correction model indicate causality that runs from financial development to economic growth.\par
Nnanna (2004) using ordinary least square regression technique, concluded that financial sector development does not significantly affect per capital growth of output. Others who used various methods and data to establish a positive and significant relationship and/or causality running from financial development to growth are De Grgor and Guidotti (1995), \hyperref[b9]{Guiso, et al (2002)}, empirical work by \hyperref[b7]{Gelb (1989)}, \hyperref[b8]{Ghani (1992)}, \hyperref[b14]{King and Levine (1993)}, DeGregorio and Giudotti (1995), \hyperref[b15]{Levine and Zervos (1996)} have all lent support to the supply-leading hypothesis in the case of many developing and developed countries (See Nwezeaku and Okpara, 2014).\par
Odiambo (2004) investigated the financegrowth nexus in South Africa using cointegration approach and vector error correction model and found out a demand-following response between financial development and economic growth and therefore discredited the supply-leading hypothesis. \hyperref[b10]{Guryay et. al (2007)} examined the relationship between financial development and economic growth for Northern Cyprus for the period of 1986-2004 and concluded that there was evidence of causality from economic growth to the development of financial intermediaries in Northern Cyprus. \hyperref[b35]{Shan, et al (2001)} in their study reached a conclusion that economic growth causes financial development in China.\par
Arestis and Demetriades (1997), using time series analysis, concluded that evidence favors a bidirectional growth. Also Murinende and Eng (1994) found evidence of such bi-directionality in the case of Singapore, while Demetriades and Hussein (1996) also found a feedback effect for 16 developing countries. \hyperref[b6]{Demetriades and Hussein (1996)}, using time series analysis for a study of developing economies also found causality running both ways.\par
A study of China by \hyperref[b36]{Shan et al (2006)} not only found a bidirectional causality between financial development and economic growth but also revealed that the Granger causality from economic growth to financial development is stronger than that from finance to growth. Likewise, Luintel and Khan (1999), investigated the finance-growth nexus in a multivariate VAR model and found bidirectional causality between financial development and economic growth in all their sample country. \hyperref[b0]{Adelakun (2010)} in his study of relationship between financial development and economic growth found that there is a substantial positive effect of financial development on economic growth in Nigeria. Using Granger causality test, he also found that financial development promotes economic growth. Akinlo, Enisan, Egbetunde and Tajudeen (2010), in examining the long run relationship between financial development and economic growth for ten countries in sub-Saharan Africa found that financial development is cointegrated with economic growth. They also added that financial development Granger causes economic growth in Central African Republic, Congo Republic, Gabon and Nigeria while economic growth Granger causes financial development in Gambia. However in Kenya, Chad, South Africa, Sierra Leone and Switzerland bidirectional relationship was established between financial development and economic growth. In their study, Odenira and Udeaja (2010) examined the relationship between financial sector development in Nigeria using Granger Causality and found a bidirectional causality between financial development and economic growth. Bi-directional causality hypothesis has been advocated by Altay and Atgur (2010). 
\section[{III.}]{III.} 
\section[{Materials and Methods}]{Materials and Methods}\par
To assess whether financial development impacts and/or drives economic growth (Supply leading) or whether it is economic growth that drives the financial sector (demand following) and leads to aggressive expansion of the financial sector or whether there exists a feedback effect?, the financial market indicators such as capital market liquidity proxied by value of share traded divided by GDP (VST/GDP), market capitalization ratio denoted by market capitalization divided by GDP (MKTCAP/GDP), broad money velocity denoted by broad money supply divided by GDP (M 2 /GDP) economic volatility represented by credit to private sector over GDP (CPS/GDP), intermediation efficiency indicated by currency outside banks over broad money supply (COB/M 2 ) and the metric for banking system financing of the economy which is proxied by demand deposit over narrow money supply (DD/M 1 ) are presented in table 4.1.\par
The researchers therefore posit the following hypotheses stated in null form as follows:\par
Ho 1 : Financial development does not exert positive and significant impact on economic growth. 
\section[{Ho 2 : Financial development does not drive economic growth}]{Ho 2 : Financial development does not drive economic growth}\par
To assess these hypotheses, the researchers employed cointegration and error correction model as there are seen appropriate for impact determination. Also the Granger causality test was used for causality test.\par
It must have to be noted that one thing drives or predicts another must not be seen as leading the later to a fortune. Something can be led to a misfortune, in such a situation, the leader or driver has driven it/him to a wrong direction. It is on this note that Granger causality has failed to indicate the desired direction of a particular economic variable. To augment or authenticate Granger causality, the direction of any variable on another must have to be determined through a well built and tested model to avoid recommending a causal relationship that will be detrimental to economic policy. This argument can be justified by the fact that a variable say X can be found to be negatively related to the dependent variable Y yet such a variable X will be found to granger cause Y with no feedback effect. The question of the direction the variable X is driving Y must be of concern. Thus, the researchers diligently tested the parameters of the variables in order to determine the significance of the magnitude of each of the variables as well as exploring their direction.\par
IV. 
\section[{Estimation and Analysis of Data}]{Estimation and Analysis of Data}\par
The relevant data presented in table 4.1 are estimated and analyzed in lieu of the stated objectives and hypothesis. From the above table, the ADF statistics of all the series are more negative than their 1 percent critical values and far more than that of 5 percent at first difference. This implies that the series are differenced once for them to be stationary. They are therefore said to be integrated of order one. This being the case, the researchers resorted to testing for cointegration between the variables and found that there are three cointegrating equations in the series which invariably suggest the existence of long run relationship between the GDP series and the financial market indicators in Nigeria. The result of the cointegration test is presented in table 4.3 below. 
\section[{Table 4.3: Cointegration Test for GDP-Financial Development Data}]{Table 4.3: Cointegration Test for GDP-Financial Development Data}\par
The existence of cointegrating equations formed the basis for the researchers' use of the error correction model. The parsimonious result of the error correction model is shown in table 4.4 as follows.  
\section[{C}]{C}\par
The results of the analysis show that past GDP at lags 3 and 4 reinforces the present GDP. In other words, increase in past GDP leads to increase in the present value of GDP. The intermediation efficiency indicator (COB/M2) is negative and significant at its immediate past level indicating that the rate at which the financial sector intermediates in the economy seems to initially impose untold hardship. However as time passes by, the intermediation efficiency increases and exerts positive impact on the economic growth. The broad money velocity (M2/GDP) is positive and significant while the metrics for banking system financing of the economy (DD/M1) is negative and significant implying that the banking sector financing of the economy is significantly inadequate to engender growth of the economy. The Stock market liquidity (VST/GDP) for the first lag is negative and significant suggesting that significant illiquidity which hampers the growth of the economy exists in the capital market at lag 1. However, this anomaly corrects as time passed by as the market liquidity exerts positive impact on the growth of the economy at lag 4. Economic volatility (CPS/GDP) and market capitalization ratio (MKT/GDP) are positive and significant. From these findings, \textbackslash the researchers adduced that the financial market activities generally impact economic growth. To accept this model, we embarked on second order econometric tests namely serial correlation test, multicollinearity test using the condition index criteria, heteroskedasticity test, normality test and Model adequacy test. The serial correlation LM test is presented in table 4.5 as follows: The LM test accepts the hypothesis of no serial correlation up to order 2. Implying that the residuals are not serial correlated and the equation therefore should not be respecified before using it for hypothesis testing.\par
The researchers also moved on to testing for multicollinearity using the condition index criteria. The test of multicollinearity is presented in Table \hyperref[tab_5]{4}.6 as follows: The F statistic and the observed R 2 test show that the series are not hetroskedastic but homoskedastic. This is of course expected since the series has already been shown to be stationary. We     The table shows that the probability of the calculated F statistic (0.0977) is greater than 5 percent. That is 0.0977 > 0.05 implying that the model is adequately specified and that that no variable is omitted. We therefore conclude that the model is adequate and plausible.\par
We also know from various Econometric literature that if two or more time series are cointegrated, then there must be Granger causality between them either on one way or in both directions. Thus, employing Granger causality test, we show the causal relationship between financial development indicators and the growth of the economy in table 4.9. Table \hyperref[tab_5]{4}.9: Granger Causality Test Table \hyperref[tab_5]{4}.9 shows that there is short run causality between capital market liquidity (VST/GDP) and the growth of the economy with a feedback effect. Economic volatility (CPS/GDP) also has a feedback effect with the growth of the economy. While Market Capitalization ratio (MKTCAP/GDP), Broad money velocity (M2/GDP) and the banking system rate of financing the economy (DD/M1) are one directionally driving economic growth. In other words, in the short run, financial development indicators drives economic growth.\par
V. 
\section[{Conclusion}]{Conclusion}\par
The intermediation efficiency of the financial sector as well as capital market liquidity do not instantly impact positively on economic growth rather it takes a little time lag for them to manifest positive and significant impact on the economy. The broad money velocity, economic volatility, and market capitalization ratio engender economic growth in the country while the banking sector financing of the economy is significantly inadequate and consequently poses a negative impact on the growth of the economy.\par
There is bidirectional causality between capital market liquidity or economic volatility and the growth of the Nigerian economy. While market capitalization ratio, broad money velocity and the banking system rate of financing the economy drive the growth of economy with no feed-back effect. For the Nigerian economy to achieve an increased economic growth, government should embark on well articulated reform packages capable of increasing the banking sector involvement in the financing of the economy.  
\section[{Pairwise Granger Causality}]{Pairwise Granger Causality}\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
   \begin{figure}[htbp]
\noindent\textbf{41} \par 
\begin{longtable}{P{0.2175018982536067\textwidth}P{0.15231586940015185\textwidth}P{0.09035687167805619\textwidth}P{0.029688686408504177\textwidth}P{0.23557327258921792\textwidth}P{0.07873955960516324\textwidth}P{0.043242217160212607\textwidth}P{0.0025816249050873196\textwidth}}
Year\tabcellsep GDP\tabcellsep \multicolumn{5}{l}{VSTGDP Mcapgdp M2GDP CPSGDP COBM2}\tabcellsep DDM1\\
1981\tabcellsep 94.33\tabcellsep 323\tabcellsep 5\tabcellsep 15.3\tabcellsep 9.1\tabcellsep 27\\
1982\tabcellsep 101.01\tabcellsep 213\tabcellsep 5\tabcellsep 15.6\tabcellsep 10.6\tabcellsep 27\\
1983\tabcellsep 110.06\tabcellsep 362\tabcellsep 5\tabcellsep 16.1\tabcellsep 10.6\tabcellsep 27\\
1984\tabcellsep 116.27\tabcellsep 221\tabcellsep 5\tabcellsep 17.3\tabcellsep 10.7\tabcellsep 24\\
1985\tabcellsep 134.59\tabcellsep 235\tabcellsep 5\tabcellsep 16.6\tabcellsep 9.7\tabcellsep 22\\
1986\tabcellsep 134.6\tabcellsep 370\tabcellsep 4\tabcellsep 17.7\tabcellsep 11.3\tabcellsep 22\\
1987\tabcellsep 193.13\tabcellsep 198\tabcellsep 3\tabcellsep 14.3\tabcellsep 10.9\tabcellsep 22\\
1988\tabcellsep 263.29\tabcellsep 323\tabcellsep 3\tabcellsep 14.6\tabcellsep 10.4\tabcellsep 25\\
1989\tabcellsep 382.26\tabcellsep 160\tabcellsep 3\tabcellsep 12\tabcellsep 8\tabcellsep 21\\
1990\tabcellsep 472.65\tabcellsep 48\tabcellsep 3\tabcellsep 11.2\tabcellsep 7.1\tabcellsep 28\\
1991\tabcellsep 545.67\tabcellsep 44\tabcellsep 3\tabcellsep 13.8\tabcellsep 7.6\tabcellsep 31\\
1992\tabcellsep 875.34\tabcellsep 56\tabcellsep 3\tabcellsep 12.7\tabcellsep 6.6\tabcellsep 33\\
1993\tabcellsep 1,089.68\tabcellsep 74\tabcellsep 3\tabcellsep 15.2\tabcellsep 11.7\tabcellsep 35\\
1994\tabcellsep 1,399.70\tabcellsep 70\tabcellsep 2\tabcellsep 16.5\tabcellsep 10.2\tabcellsep 39\\
1995\tabcellsep 2,907\tabcellsep 63\tabcellsep 4\tabcellsep 9.9\tabcellsep 6.2\tabcellsep 37\\
1996\tabcellsep 4032.3\tabcellsep 172\tabcellsep 7\tabcellsep 8.6\tabcellsep 5.9\tabcellsep 34\\
1997\tabcellsep 4189.25\tabcellsep 25\tabcellsep 7\tabcellsep 9.9\tabcellsep 7.5\tabcellsep 32\\
1998\tabcellsep 3989.45\tabcellsep 34\tabcellsep 7\tabcellsep 12.2\tabcellsep 8.8\tabcellsep 32\\
1999\tabcellsep 4679.21\tabcellsep 301\tabcellsep 5\tabcellsep 13.4\tabcellsep 9.2\tabcellsep 30\\
2000\tabcellsep 6713.57\tabcellsep 419\tabcellsep 7\tabcellsep 13.1\tabcellsep 7.9\tabcellsep 31\\
2001\tabcellsep 6895.2\tabcellsep 837\tabcellsep 8\tabcellsep 18.4\tabcellsep 11.1\tabcellsep 27\\
2002\tabcellsep 7795.76\tabcellsep 762\tabcellsep 8\tabcellsep 19.3\tabcellsep 11.9\tabcellsep 26\\
2003\tabcellsep 9913.52\tabcellsep 1215\tabcellsep 14\tabcellsep 19.7\tabcellsep 11.1\tabcellsep 21\\
2004\tabcellsep 11411.07\tabcellsep 1979\tabcellsep 19\tabcellsep 18.7\tabcellsep 12.5\tabcellsep 22\\
2005\tabcellsep 14610.88\tabcellsep 1800\tabcellsep 20\tabcellsep 18.1\tabcellsep 12.6\tabcellsep 21\\
2006\tabcellsep 18,564.59\tabcellsep 2,533\tabcellsep 28\tabcellsep 20.5\tabcellsep 12.3\tabcellsep 17\\
2007\tabcellsep 20657.32\tabcellsep 5209\tabcellsep 64\tabcellsep 24.8\tabcellsep 17.8\tabcellsep 14\\
2008\tabcellsep 24296.33\tabcellsep 6911\tabcellsep 39\tabcellsep 33\tabcellsep 28.6\tabcellsep 11\\
2009\tabcellsep 24794.24\tabcellsep 2766\tabcellsep 28\tabcellsep 38\tabcellsep 36.9\tabcellsep 10\\
2010\tabcellsep 54612.26\tabcellsep 3465\tabcellsep 18\tabcellsep 20.2\tabcellsep 18.6\tabcellsep 10\\
2011\tabcellsep 62980.4\tabcellsep 1014\tabcellsep 16\tabcellsep 19.3\tabcellsep 16.9\tabcellsep 10\\
2012\tabcellsep 71713.94\tabcellsep 1124\tabcellsep 21\tabcellsep 19.4\tabcellsep 20.4\tabcellsep 10\\
2013\tabcellsep 80092.56\tabcellsep 2935\tabcellsep 24\tabcellsep 18.9\tabcellsep 19.7\tabcellsep 10\\
2014\tabcellsep 89043.62\tabcellsep 1499\tabcellsep 19\tabcellsep 19.9\tabcellsep 19.2\tabcellsep 8\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{4}{l}{Source: Compiled from CBN Statistical Bulletin}\\
\multicolumn{4}{l}{The GDP and financial development indicators}\tabcellsep \multicolumn{4}{l}{shows that all the variables are stationary at first}\\
\multicolumn{4}{l}{in the table above are tasted for stationarity to avoid}\tabcellsep \multicolumn{4}{l}{difference. In other words all the variables are integrated}\\
\multicolumn{4}{l}{spurious results which could have arisen if non}\tabcellsep \multicolumn{4}{l}{of order one, I(1). The summary of these results is}\\
\multicolumn{4}{l}{stationary data are used for regression. The result}\tabcellsep \multicolumn{3}{l}{shown in table 4.2 as follows:}\end{longtable} \par
 
\caption{\label{tab_0}Table 4 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{42} \par 
\begin{longtable}{P{0.08315217391304348\textwidth}P{0.030027173913043475\textwidth}P{0.21942934782608695\textwidth}P{0.18709239130434782\textwidth}P{0.15244565217391304\textwidth}P{0.1778532608695652\textwidth}}
\tabcellsep Lag\tabcellsep ADF Test Statistic\tabcellsep \multicolumn{2}{l}{Critical Values}\tabcellsep \\
\tabcellsep SCI\tabcellsep 1st difference\tabcellsep 1\%\tabcellsep 5\%\tabcellsep Remarks\\
GDP\tabcellsep 4\tabcellsep -3.863830\tabcellsep -3.653730\tabcellsep -2.957110\tabcellsep Stationary\\
VSTGDP\tabcellsep 8\tabcellsep -6.552533\tabcellsep -3.653730\tabcellsep -2.957110\tabcellsep Stationary\\
MCAPGDP\tabcellsep 8\tabcellsep -5.879994\tabcellsep -3.653730\tabcellsep -2.957110\tabcellsep Stationary\\
M2GDP\tabcellsep 8\tabcellsep -5.378874\tabcellsep -3.653730\tabcellsep -2.957110\tabcellsep Stationary\\
CPSGDP\tabcellsep 8\tabcellsep -5.726112\tabcellsep -3.661661\tabcellsep -2.960411\tabcellsep Stationary\\
COBM2\tabcellsep 8\tabcellsep -4.870376\tabcellsep -3.653730\tabcellsep -2.957110\tabcellsep Stationary\\
DDM1\tabcellsep 8\tabcellsep -5.232303\tabcellsep -3.653730\tabcellsep -2.957110\tabcellsep Stationary\end{longtable} \par
 
\caption{\label{tab_1}Table 4 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{44} \par 
\begin{longtable}{P{0.31622691292875993\textwidth}P{0.14428320140721196\textwidth}P{0.1809146877748461\textwidth}P{0.10167106420404573\textwidth}P{0.10690413368513632\textwidth}}
\multicolumn{2}{l}{Dependent Variable: D (GDP)}\tabcellsep \tabcellsep \tabcellsep \\
Method: Least Squares\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Date: 03/04/17 Time: 17:16}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Sample (adjusted): 1986 2014}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{Included observations: 29 after adjustments}\tabcellsep \tabcellsep \\
Variable\tabcellsep Coefficient\tabcellsep Std. Error\tabcellsep t-Statistic\tabcellsep Prob.\\
D(GDP(-3))\tabcellsep 1.131716\tabcellsep 0.092826\tabcellsep 12.19176\tabcellsep 0.0000\\
D(GDP(-4))\tabcellsep 0.128731\tabcellsep 0.045405\tabcellsep 2.835145\tabcellsep 0.0132\\
D(COBM2(-1))\tabcellsep -285.0871\tabcellsep 112.1978\tabcellsep -2.540933\tabcellsep 0.0235\\
D(COBM2(-3))\tabcellsep 311.5502\tabcellsep 113.4656\tabcellsep 2.745768\tabcellsep 0.0158\\
D(CPSGDP(-1))\tabcellsep 316.4391\tabcellsep 136.0973\tabcellsep 2.325094\tabcellsep 0.0356\\
D(CPSGDP(-2))\tabcellsep 654.3071\tabcellsep 67.66742\tabcellsep 9.669456\tabcellsep 0.0000\\
D(CPSGDP(-3))\tabcellsep 783.8138\tabcellsep 137.5658\tabcellsep 5.697737\tabcellsep 0.0001\\
D(DDM1(-2))\tabcellsep -194.4585\tabcellsep 60.83273\tabcellsep -3.196609\tabcellsep 0.0065\\
D(DDM1(-3))\tabcellsep -410.7492\tabcellsep 109.3208\tabcellsep -3.757283\tabcellsep 0.0021\\
D(M2GDP(-1))\tabcellsep 417.7338\tabcellsep 189.1424\tabcellsep 2.208568\tabcellsep 0.0444\\
D(MCAPGDP(-1))\tabcellsep 109.6002\tabcellsep 28.91204\tabcellsep 3.790815\tabcellsep 0.0020\\
D(VSTGDP(-1))\tabcellsep -3.163521\tabcellsep 0.218548\tabcellsep -14.47518\tabcellsep 0.0000\\
D(VSTGDP(-4))\tabcellsep 2.407486\tabcellsep 0.539429\tabcellsep 4.463028\tabcellsep 0.0005\\
ECT(-1)\tabcellsep -0.071766\tabcellsep 0.028613\tabcellsep -2.508156\tabcellsep 0.0251\\
C\tabcellsep 320.4475\tabcellsep 222.6680\tabcellsep 1.439127\tabcellsep 0.1721\\
R-squared\tabcellsep 0.986988\tabcellsep \multicolumn{2}{l}{Mean dependent var}\tabcellsep 3065.829\\
Adjusted R-squared\tabcellsep 0.973976\tabcellsep \multicolumn{2}{l}{S.D. dependent var}\tabcellsep 5894.833\\
S. E. of regression\tabcellsep 950.9460\tabcellsep \multicolumn{2}{l}{Akaike info criterion}\tabcellsep 16.85904\\
Sum squared resid\tabcellsep 12660176\tabcellsep \multicolumn{2}{l}{Schwarz criterion}\tabcellsep 17.56626\\
Log likelihood\tabcellsep -229.4560\tabcellsep \multicolumn{2}{l}{Hannan-Quinn criter.}\tabcellsep 17.08053\\
F-statistic\tabcellsep 75.85309\tabcellsep \multicolumn{2}{l}{Durbin-Watson stat}\tabcellsep 2.476241\\
Prob(F-statistic)\tabcellsep 0.000000\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 4 . 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{45} \par 
\begin{longtable}{P{0.4414728682170543\textwidth}P{0.10542635658914729\textwidth}P{0.224031007751938\textwidth}P{0.07906976744186046\textwidth}}
\multicolumn{3}{l}{Breusch-Godfrey Serial Correlation LM Test:}\tabcellsep \\
F-statistic\tabcellsep 1.074760\tabcellsep Prob. F (1,13)\tabcellsep 0.3188\\
Obs*R-squared\tabcellsep 2.214464\tabcellsep Prob. Chi-Square (1)\tabcellsep 0.1367\end{longtable} \par
 
\caption{\label{tab_3}Table 4 . 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{46} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_4}Table 4 . 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.09501822600243012\textwidth}P{0.4554678007290401\textwidth}P{0.15388821385176185\textwidth}P{0.14562575941676792\textwidth}}
Year\tabcellsep \tabcellsep \tabcellsep \\
6\tabcellsep \tabcellsep \tabcellsep \\
Volume XVIII Issue II Version I\tabcellsep Variance Inflation Factors\tabcellsep \tabcellsep \\
Global Journal of Management and Business Research ( ) C\tabcellsep Date: 03/04/17 Time: 17:26 Sample: 1981 2014 Coefficient Variable Variance D(GDP(-3)) 0.008617 D(GDP(-4)) 0.002062 D(COBM2(-1)) 12588.35 D(COBM2(-3)) 12874.44 D(CPSGDP(-1)) 18522.49 D(CPSGDP(-2)) 4578.880 D(CPSGDP(-3)) 18924.35 D(DDM1(-2)) 3700.621 D(DDM1(-3)) 11951.03 D(M2GDP(-1)) 35774.85 D(MCAPGDP(-1)) 835.9058 D(VSTGDP(-1)) 0.047763 D(VSTGDP(-4)) 0.290983 ECT(-1) 0.000819 C 49581.02 Included observations: 29\tabcellsep Uncentered VIF 9.709253 2.163407 2.951161 3.146364 13.15236 3.248920 13.17132 2.561753 7.942701 21.96579 2.154706 2.048665 9.506051 2.387260 1.590017\tabcellsep Centered VIF 8.410134 1.929747 2.857077 3.004485 13.09515 3.232151 13.14267 2.516033 7.701627 21.96230 2.143199 2.035250 9.396512 2.386915 NA\end{longtable} \par
 
\caption{\label{tab_5}Table 4 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{47} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_6}Table 4 . 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{48} \par 
\begin{longtable}{P{0.2714121037463977\textwidth}P{0.11659942363112391\textwidth}P{0.19596541786743515\textwidth}P{0.13178674351585015\textwidth}P{0.08426512968299713\textwidth}P{0.0063688760806916425\textwidth}P{0.04360230547550432\textwidth}}
\multicolumn{3}{l}{Heteros kedas ticity Tes t: Breus ch-Pagan-Godfrey}\tabcellsep \tabcellsep \tabcellsep \\
F-s tatis tic\tabcellsep 0.366528\tabcellsep Prob. F(14,14)\tabcellsep \tabcellsep \multicolumn{2}{l}{0.9647}\\
Obs *R-s quared\tabcellsep 7.778335\tabcellsep \multicolumn{2}{l}{Prob. Chi-Square(14)}\tabcellsep \multicolumn{2}{l}{0.9006}\\
Scaled explained SS\tabcellsep 2.868501\tabcellsep \multicolumn{2}{l}{Prob. Chi-Square(14)}\tabcellsep \multicolumn{2}{l}{0.9993}\\
Tes t Equation:\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Dependent Variable: RESID\textasciicircum 2}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Method: Leas t Squares\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Date: 03/04/17 Tim e: 18:50}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Sam ple: 1986 2014\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Included obs ervations : 29\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Variable\tabcellsep Coefficient\tabcellsep Std. Error\tabcellsep t-Statis tic\tabcellsep \tabcellsep Prob.\\
C D(GDP(-3)) D(GDP(-4))\tabcellsep 568553.5 -72.87875 -33.52181\tabcellsep 223891.9 93.33653 45.65501\tabcellsep 2.539411 -0.780817 -0.734242\tabcellsep \multicolumn{2}{l}{0.0236 0.4479 0.4749}\tabcellsep Year\\
D(COBM2(-1))\tabcellsep -11069.69\tabcellsep 112814.5\tabcellsep -0.098123\tabcellsep \multicolumn{2}{l}{0.9232}\\
D(COBM2(-3))\tabcellsep 37468.49\tabcellsep 114089.3\tabcellsep 0.328414\tabcellsep \multicolumn{2}{l}{0.7475}\\
D(CPSGDP(-1))\tabcellsep 126687.9\tabcellsep 136845.5\tabcellsep 0.925774\tabcellsep \multicolumn{2}{l}{0.3702}\\
D(CPSGDP(-2)) D(CPSGDP(-3)) D(DDM1(-2)) D(DDM1(-3)) D(M2GDP(-1)) D(MCAPGDP(-1)) D(VSTGDP(-1)) D(VSTGDP(-4)) ECT(-1) R-s quared Adjus ted R-s quared S.E. of regres s ion Sum s quared res id Log likelihood F-s tatis tic Prob(F-s tatis tic)\tabcellsep -7574.406 -184679.0 33204.94 48026.08 -168869.5 -1271.794 -115.3502 617.9393 37.68962 0.268218 -0.463563 956173.2 1.28E+13 -429.9399 0.366528 0.964737\tabcellsep \multicolumn{2}{l}{68039.38 138322.0 61167.12 109921.7 190182.1 29070.96 219.7492 542.3940 28.77029 Mean dependent var -0.111324 -1.335138 0.542856 0.436912 -0.887936 -0.043748 -0.524917 1.139281 1.310019 S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Wats on s tat}\tabcellsep \multicolumn{2}{l}{0.9129 0.2031 0.5958 0.6688 0.3896 0.9657 0.6079 0.2737 0.2113 436557.8 790370.7 30.68551 31.39273 30.90700 1.646029}\tabcellsep Volume XVIII Issue II Version I ( )\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{Series: Residuals Sample 1986 2014 Observations 29 Mean -2.50e-13 Median -118.5291 Maximum 1402.878 Minimum -1880.137 Std. Dev. 672.4204 Skewness 0.002604 Kurtosis 4.164741 Jarque-Bera 1.639285}\tabcellsep Global Journal of Management and Business Research\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Probability}\tabcellsep 0.440589\end{longtable} \par
 
\caption{\label{tab_7}Table 4 . 8 :}\end{figure}
 			\footnote{© 2018 Global Journals} 			\footnote{Econometrics Analysis of Financial Development and Economic Growth: Evidence from Nigeria} 			\footnote{© 2018 Global Journals 1} 		 		\backmatter   			 
\subsection[{Acknowledgement}]{Acknowledgement}\par
This paper is extracted from the project work sponsored by TET Fund of Nigeria and written by the authors. 			  			  				\begin{bibitemlist}{1}
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\end{bibitemlist}
 			 		 	 
\end{document}
