Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
 
Articles by B.A. Oyejola
Total Records ( 2 ) for B.A. Oyejola
  O.O. Alabi , Kayode Ayinde and B.A. Oyejola
  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.
  Kayode Ayinde and B.A. Oyejola
  The estimates of the OLS estimator of the Classical Linear Regression Model are known to be inconsistent when regressors are correlated with the error terms. However, this does not imply that inference is impossible. In this study, we compare the performances of the OLS and some Feasible GLS estimators when stochastic regressors are correlated with the error terms through Monte Carlo studies at both low and high replications. The performances of the estimators are compared using the following small sampling properties of estimators at various levels of correlation: bias, absolute bias, variance and more importantly the mean squared error of the model parameters. Results show that the OLS and GLS estimators considered in the study are equally good in estimating the model parameters when replication is low. However with increased replication, the OLS estimator is most efficient even though the performances of all the estimators exhibit no significant difference when the correlation between regressor and error terms tends to ±1.
 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility