The Efficiency of the K-L Estimator for the Seemingly Unrelated Regression Model: Simulation and Application

Abstract

This paper considers the Ridge Feasible Generalized Least Squares Estimator (RFGLSE), Ridge Seemingly Unrelated Regression RSUR and proposes the Kibria-Lukman KLSUR estimator for the parameters of the Seemingly Unrelated Regression (SUR) model when the regressors of the models are collinear. A simulation study was conducted to compare the performance of the three different types of estimators for the SUR model. Different correlation levels (0.0, 0.1, 0.2, ..., 0.9) among the independent variables, sample sizes replicated 10000 times and contemporaneous error correlation (0.0, 0.1, 0.2, ..., 0.9) among the equations were assumed for the simulation study. The efficiency of the three (RFGLSE, RSUR, and KLSUR estimators for SUR, when the predictors are correlated, was investigated using the Trace Mean Square Error (TMSE). The results showed that the KLSUR estimator outperformed the other estimators except for a few cases when the sample size is small.