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 based not only by its computational simplicity but also as a
result of its optimal properties such as linearity, unbiasedness, minimum variance,
zero mean value of the random terms, etc (koutsoyiannis 2001, Gujarati 2004).
MODEL
SPECIFICATION
In this study, hypothesis has been
stated with the view of evaluating the effect of government expenditure on
Education in Nigeria. In capturing the study, these variables were used as
proxy. Thus, the model is represented in a functional form. It is shown as
below:
GDP = F (RGEE, CGEE)…………. 3.1
Where
GDP = Gross Domestic
Product (Dependent variable)
RGEE = Recurrent Government Expenditure on Education
(Independent
variable)
CGEE = Capital Government Expenditure on Education
(Independent
variable)
In a linear function, it is
represented as follows,
GDP = b0 + b1RGEE + b2CGEE
+ Ut ……………3.2
Where
b0
= Constant term
b1 = Regression coefficient of RGEE
b2
= Regression coefficient of CGEE
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 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 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 include:
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 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 < 1/2b1, 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.