Department of Statistics, University of Ibadan, Ibadan, Nigeria
Olaomi, J.O., Department of Statistics, University of Ibadan, Ibadan, Nigeria
Assumptions in the classical normal linear regression model include that of lack of autocorrelation of the error terms and the zero covariance between the explanatory variable and the error terms. This paper is channeled towards understanding the performance of estimators of the parameters of the linear regression models when the above two assumptions are violated. The study used the Monte-Carlo method to investigate the performance of five estimators: OLS, CORC, HILU, ML and MLGRID in estimating the parameters of a single linear regression model in which the geometric explanatory variable is also correlated with the autoregressive error terms. The finite sampling properties of Bias, Variance and RMSE were used in evaluating the estimators. The results show that all estimators are adversely affected as autocorrelation coefficient (ρ) is close to unity. The estimators rank as follows in descending order of performance: OLS, MLGRID, ML, CORC and HILU as ρ increases while as significant level (α) decreases the ranking is MLGRID, ML, OLS, CORC and HILU. The estimators conform to the asymptotic properties of estimates considered. This is seen at all levels of autocorrelation and at all significant levels. The estimators' rank in decreasing order in conformity with the observed asymptotic performance as follows: HILU, OLS, ML, MLGRID, and CORC. The results suggest that OLS should be preferred when autocorrelation level is relatively mild (ρ = 0.4) and the geometric regressor is significantly correlated at 5% with the autocorrelated error terms. © EuroJournals Publishing, Inc. 2008.