Estimation of the seemingly unrelated regression model when the error covariance matrix is singular

Abstract

A two-step generalized least-squares (GLS) estimator proposed by Zellner for seemingly unrelated regression (SUR) models is implementable when the estimated covariance matrix of the errors in the SUR system is non-singular. Violating the premise of non-singularity is a common problem among many applications in economics, business and management. We present methods of resolving this problem and propose an efficient procedure. The simulation study shows that the estimator of Haff performs better for small-sized observations, whereas the estimator of Ullah and Racine performs better for larger sized observations. Furthermore, the Ullah-Racine estimate is simple to calculate and easy to use. The empirical analysis involves the study of the diffusion processes of videocassette recorders across different geographic regions in the US, which exhibits a singular covariance matrix. The empirical results show that the procedures efficiently deal with the problem and provide plausible estimation results.