This chapter focuses on the research method that will be adopted. Regression analysis based on the classical linear regression model, otherwise known as ordinary least square (OLS) technique is chosen by the researcher. The researcher’s choice of technique is base not only by its computational simplicity but also as a result of its optimal properties such as linearity, unbiassedness, minimum variance, zero mean value of the random terms, etc (Koutsoyiannis 2001, Gugarati 2004).
Model Specification
In this study, hypothesis has been stated with the view of ascertaining impact of the monetary policy on economic growth in Nigeria. In capturing study, these variables were used as proxy. Thus, the model is represented in a functional form. It is shown as below:
GDP = F (Ms, INF, EXR) ……. 3.1
Where
GDP = Gross Domestic Product (Dependent variable)
Ms = money supply (independent variable)
INT = increase rate (independent variable)
EXT = exchange rate (independent variable)
In a linear function, it is represented as follows:-
GDP = b0 +b1 MS + b2 INT + b3 EXR + Ut ………3.2
Where
B0 = constant term
B1 = regression coefficient of MS
B2 = regression coefficient of INT
B3 = regression coefficient of EXR
Ut = Error term.
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 best liner unbiased estimator, minimum variance, zero mean value of the random terms, etc (Gujarati 2004).
However, due to conventional reasons, the researcher will make use of Pc-give 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. The tests that will be considered in this study includes:
– Coefficient of multiple determination (R2)
– Standard error test (S.E)
– T test
– F-test
– Durbin Watson statistics
Coefficient of Multiple Determination (R2): It is used to measure the proportion of variation in the dependent variable which is explained by the explanatory variables. The higher the (R2) the greater the proportion f the variation in the independent variables.
Decision Rule
If S. E <½ (b1), reject the null hypothesis and concluded that the coefficient estimate of parameter is statically 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 variable is unknown and the sample size is less than 30.
Decision Rule
If t-cal >t-tab, reject the null hypothesis and concluded 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 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 no evidence of positive first order serial and 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 in various years.
· Central Bank of Nigeria annual account for various years
· Central Bank of Nigeria economic and financial review for various years.
· Other CBN periodicals-Bullion of various years.
· National bureau of statistics publication-annual reports of various years.
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