Heteroskedastic Stratified Two-way Error Component Models of Single Equations and Seemingly Unrelated Regressions Systems

A relevant issue in panel data estimation is heteroskedasticity, which often occurs when the sample size is large and individual units are of varying size. Furthermore, many of the available panel data sets are unbalanced in nature, because of attrition or accretion, and micro-econometric models applied to panel data are frequently multi-equation models. This paper considers the general least squares estimation of heteroskedastic stratified two-way error component model of both single equations and seemingly unrelated regressions (SUR) systems (with cross-equations restrictions) on unbalanced panel data. The derived heteroskedastic estimators improve the estimation efficiency, with the SUR procedures performing better than the singleequation procedures.