Heteroscedastic stratified two-way EC models of single equations and SUR systems

TL;DR

This paper develops heteroscedastic stratified two-way error component models for single equations and SUR systems in unbalanced panel data, improving estimation efficiency in the presence of heteroscedasticity and unbalanced samples.

Contribution

It introduces generalized least squares estimators for heteroscedastic stratified two-way EC models applicable to unbalanced panel data, enhancing estimation accuracy.

Findings

Heteroscedastic estimators improve efficiency over traditional methods.

Models effectively handle unbalanced panel data with heteroscedasticity.

Applicable to both single equations and SUR systems.

Abstract

A relevant issue in panel data estimation is heteroscedasticity, which often occurs when the sample 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 the heteroscedastic stratified two-way error component (EC) models of both single equations and seemingly unrelated regressions (SUR) systems (with cross-equations restrictions) on unbalanced panel data. The derived heteroscedastic estimators of both single equations and SUR systems improve the estimation efficiency.