Empirical Investigation of Effect of Multicollinearity on Type 1 Error Rates of the Ordinary Least Squares Estimators

The effect of multicollinearity on the parameters of regression model using the Ordinary Least Squares (OLS) estimator is not only on estimation but also on inference. Large standard errors of the regression coefficients result in very low values of the t-statistic. Consequently, this study attempts to investigate empirically the effect of multicollinearity on the type 1 error rates of the OLS estimator. A regression model with constant term ( 0) and two independent variables (with 1 and 2 as their respective regression coefficients) that exhibit multicollinearity was considered. A Monte Carlo study of 1000 trials was conducted at 8 levels of multicollinearity (0, 0.25, 0.5, 0.7, 0.75, 0.8, 0.9 and 0.99) and sample sizes (10, 20, 40, 80, 100, 150, 250 and 500). At each specification, the true regression coefficients were set at unity. Results show that multicollinearity effect on the OLS estimator is not serious in that the type 1 error rates of 0 is not significantly different from the preselected level of significance (0.05), in all the levels of multicollinearity and samples sizes and that that of 1 and 2 only exhibits significant difference from 0.05 in very few levels of multicollinearity and sample sizes. Even at these levels the significant level different from 0.06.