Selecting the covariance structure for the seemingly unrelated regression models

Objective: This paper is concerned with evaluating suggested approach of selecting the suitable covariance structure for fitting the seemingly unrelated regression equations (SURE) models efficiently. Method: The paper assessed AL-Marshadi (2014) methodology in terms of its percentage of times that it identifies the right covariance structure for mixed model analysis of SURE models using simulated data.Application: The simulated equations of SURE models have identical explanatory variables, the regressors in one block of equations are a subset of those in another, and different regressors in the equations with various settings of covariance structures of P. Moreover, the percentage of times that REML fail to converge under normal situation are reported. The application of the proposed methodology is given using a panel of data.Conclusions: In short, AL-Marshadi (2014) methodology provided an excellent tool for selecting the right covariance structure for SURE models using restricted maximum likelihood (REML) estimation method in order to fit the SURE models more efficiently than the existing method that considering the stander unstructured covariance structure in fitting SURE models.(c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

This paper evaluated a method for selecting suitable covariance structure to efficiently fit SURE models, and validated it using simulated data, showing excellent performance in identifying the correct covariance structure.