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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