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

Authors

  • Oluwayemisi Oyeronke Alaba 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 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.

Dimensions

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Published

2023-06-14

How to Cite

The Efficiency of the K-L Estimator for the Seemingly Unrelated Regression Model: Simulation and Application. (2023). Journal of the Nigerian Society of Physical Sciences, 5(3), 1414. https://doi.org/10.46481/jnsps.2023.1514

Issue

Volume 5, Issue 3, August 2023

Section

Original Research
Copyright & Licensing

Copyright (c) 2023 Oluwayemisi Oyeronke Alaba, B. M. Golam Kibria

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How to Cite

The Efficiency of the K-L Estimator for the Seemingly Unrelated Regression Model: Simulation and Application. (2023). Journal of the Nigerian Society of Physical Sciences, 5(3), 1414. https://doi.org/10.46481/jnsps.2023.1514