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 impact of foreign aid on poverty reduction 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:
P = f(ODA, GR,PGR,CAP,GRGDP)……….…………. 3.1
Where
P = Population in Poverty (dependent variable)
ODA = Net official development assistance
GR = Grants received, excluding technical co-operation
PGR = Population growth rate
CAP = Capital expenditure
GRGDP= Growth rate of GDP
In a linear function, it is represented as follows,
P = b0 + b1ODA + b2GR + b3PGR + b4CAP + b5GRGDP+Ut ……3.2
Where
b0 = Constant term
b1 = Regression coefficient of ODA
b2 = Regression coefficient of GR
b3 = Regression coefficient of PGR
b4 = Regression coefficient of CAP
b5 = Regression coefficient of GRGDP
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 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. 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 < 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 lo
wer 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
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