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

Oluwayemisi Oyeronke Alaba; B. M. Golam Kibria

doi:10.46481/jnsps.2023.1514

Authors

Oluwayemisi Oyeronke Alaba
[email protected]

Department of Statistics, University of Ibadan, Nigeria | Department of Mathematics and Statistics, Florida International University, Miami, FL, USA
B. M. Golam Kibria
Department of Mathematics and Statistics, Florida International University, Miami, FL, USA

Keywords:

Multicollinearity, Ridge feasible generalized least squares estimator, Seemingly Unrelated Regression, Trace Mean Square Error, K-LSUR

Abstract

This paper considers the Ridge Feasible Generalized Least Squares Estimator (RFGLSE), Ridge Seemingly Unrelated Regression R_SUR and proposes the Kibria-Lukman KL_SUR 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, R_SUR, and KL_SUR estimators for SUR, when the predictors are correlated, was investigated using the Trace Mean Square Error (TMSE). The results showed that the KL_SUR estimator outperformed the other estimators except for a few cases when the sample size is small.

Dimensions

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