METHOD OF CALCULATING AND ANALYSING TAXATION ON FOREIGN DIRECT INVESTMENT



RESEARCH METHODOLOGY
            The methodology to be adopted in this research work is ordinary least square (OLS) techniques. Ordinary least square techniques is adopted because of its simplicity and estimates obtained from this procedure have optimal properties including linearity, unbiassedness and minimum variance (Koutsoyianis 2001:p48)


MODEL SPECIFICATION      
            To analyze the impact of taxation on foreign direct investment (FDI), Foreign Direct Investment will be used as the dependent variable while taxation (CIT) will be used as independent variable.
            The functional relationship between the two variables can be stated thus:
FDI = (CIT)
Where:
FDI = Foreign Direct Investment
F = functional notation.
CIT = Company Income Tax.
The OLS linear regression equation based on the above functional relations is
FDI = b0 + b1 CIT + U 
Where FDI and CIT were previously defined
            b0 = constant term of the regression equation  
            b1 = Regression coefficient of company income tax
Ui = random term representing omitted variables and errors in the dependent variables.
The signs of the parameters based on prior economic theory, is b0>0. This implies that company income tax impact significantly on foreign direct investment.

METHOD OF EVALUATION   
            Adeleye (2002:p19) observed that the essence of evaluating result is to determine the hypothesis that is to be rejected or accepted. To evaluate data, the computed coefficient of determination, R2 will be used to test the goodness off it or the explanatory power of the dependent variable. The value of R2 lies between 0 and 1 i.e. 1k R2 <1. The closer it is to the better the goodness of fit and the farther it is from one, the worse the goodness of it.
            The students t-test is used to test for the statistical significance of the regression coefficient. The observed t-ratio (t*) will be compared with the critical t-ratio. For a two tail test at 5% level of significance with n-k degree of freedom where “n” is the number of observation, and “k” the number of parameters to be estimated.
            If the observed t-ratio (t*) is greater than the critical t-ratio at 5% level of significance, e reject the null hypothesis and accept the alternative hypothesis, vice versa.
            The F-ratio will be used it test for the significance of the entire regression plane and the stability of regression coefficient.
            The observed f-ratio (f*) will be compared with the theoretical f-ratio at 5% level of significance and with v1 = k-1 and v2=N-k.
            If F* > f0.05. We reject the null hypothesis and accept the alternative hypothesis, that the regression is significant and vice versa.

DATA REQUIRED AND SOURCES                
            The data used in the regression work were secondary data. The were time series data on foreign direct investment and taxation (CIT) they were collated from the following under listed sources:
1.                  CBN Statistical Bulletin
2.                  Federal Office of Statistics
3.                  CBN economic and financial review, 2005.

REFERENCE
Koutsoyianis, A. (1997) theory of Econometric, (London: Macmillan Press)
Osuala, E. C. (1993), Introduction to Research Methodology 3rd edittion Onitsha: African Fed. Publisher Ltd)  
Pindyze R. S. (1985), Econometric model and economic forecast Singapore: MCgraw Hill Book)
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