RESEARCH METHODOLOGY OF RELATIONSHIP BETWEEN COMMERCIAL BANK CREDIT AND INDUSTRIAL SECTOR GROWTH IN NIGERIA

RESEARCH METHODOLOGY
            In order to properly locate and situate the impact of commercial bank credit and industrial growth in Nigeria, an econometric methodology of research will be used in estimating the model. This is because of its interesting BLUE (Best linear unbiased Estimator), minimum variance, zero mean value of the random terms, and its intrinsic assumptions (Koutsoyiannis 2001, Gujarati 2004, Baltagi, 1999, and Nwobi 2001).
MODEL SPECIFICATION
            The essence of economic modelling is to represent the phenomenon under investigation in such a way as to enable the researcher to attribute numerical values to the concept. To determine the effect of commercial bank credit on industrial sector growth, we will specify the model as:

          MAN     =          f (CRDT,IR)   ………………..              3.1
   Where
         MAN        =        Manufacturing sector output (Dependent variable)
         CRDT  =            Commercial banks total credit to private sector
         (Independent variable)
            IR           =        Real interest Rate (Independent variable)
      
  In a linear function, it is represented as follows,
MAN  =   β0  +   β1 CRDT  +  β2IR +  ut    ---------------------            3.2
Where
β0        =          Constant or intercept
β1        =          Commercial bank credit parameters
β2        =          Real interest rate parameters
ut         =          Error term or Stochastic variable
MODEL EVALUATION
          At this level of research, using a time series data, the researcher estimates the model with ordinary least square method. This method is preferred to others as it is best linear unbiased estimator, minimum variance, zero mean value of the random terms, etc (Koutsoyiannis 2001, Gujarati 2004, Baltagi, 1999, and Nwobi 2001).
           However, due to conventional reasons, the researcher will make use of E-view software statistical package in running the regression. This as believed by the researcher will help in determining the result of the various tests that is to be carried out. These test as defined by statistical theory and will be used to evaluate the reliability of the parameter estimates. According to (Gujarati, 2004), “a test of significance of a procedure by which sample results are used to verify the truth or falsity of a null hypothesis…”
The tests that will be considered in this study include:
v      Coefficient of multiple determination (R2)
v          Standard Error test (S.E)
v          T-test
v      F-test
v      Durbin Watson Statistics
Coefficient of Multiple Determination (R2 ): It is used to measure the proportion of variations in the dependent variable which is explained by the explanatory variables. The higher the (R2) the greater the proportion of the variation in the independent variables.
Standard Error test (S.E): It is used to test for the reliability of the coefficient estimates.

Decision Rule

If S.E < ½ b1, reject the null hypothesis and conclude that the coefficient estimate of parameter is statistically significant. Otherwise accept the null hypothesis.
T-test: It is used to test for the statistical significance of individual estimated parameter. In this research, T-test is chosen because the population variance is unknown and the sample size is less than 30.

     Decision Rule

If T-cal > T-tab, reject the null hypothesis and conclude that the regression coefficient is statistically significant. Otherwise accept the null hypothesis.
F-test: It is used to test for the joint influence of the explanatory variables on the dependent variable.

Decision Rule

If F-cal > F-tab, reject the null hypothesis and conclude that the regression plane is statistically significant. Otherwise accept the null hypothesis.
Durbin Watson (DW): It is used to test for the presence of autocorrelation (serial correlation).

 Decision Rule

If the computed Durbin Watson statistics is less than the tabulated value of the lower limit, there is evidence of positive first order serial correlation. If it is greater than the upper limit there is no evidence of positive first order serial correlation. However, if it lies between the lower and upper limit, there is inconclusive evidence regarding the presence or absence of positive first order serial correlation.      



SOURCES OF DATA
           The data for this research project is obtained from the following sources:
- Central Bank of Nigeria Statistical Bulletin for various years.
- Central Bank of Nigeria Annual Account for various years.
- Central Bank of Nigeria Economic and Financial Review for various years.
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