Research Journal of Applied Sciences
ISSN: Online 1993-6079ISSN: Print 1815-932x
Abstract
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.
How to cite this article:
Kayode Ayinde and B.A. Oyejola . A Comparative Study of the Performances of the OLS and Some GLS Estimators When Stochastic Regressors are Correlated with the Error Terms.
DOI: https://doi.org/10.36478/rjasci.2007.215.220
URL: https://www.makhillpublications.co/view-article/1815-932x/rjasci.2007.215.220