CORRECTING JOINT HETEROSCEDASTICITY AND AUTOCORRELATION IN PANEL DATA: A ROBUST GMM APPROACH

Abstract

This study evaluated Robust Shrinkage Generalized Method of Moments (RSGMM), Panel Adaptive Ridge GMM (PARGMM) and Heteroscedasticity-Autocorrelation-Robust Shrinkage GMM (HARSGMM) for panel data models where autocorrection and heteroscedasticity coexist. These estimators were designed to simultaneously address multicollinearity, heteroscedasticity and autocorrelation, which commonly undermine the reliability of conventional estimators such as Ordinary Least Squares (OLS), Feasible Generalized Least Squares (FGLS), First Difference (FD), and Between Estimators (BTW). Using Monte Carlo simulations, the performance of all estimators was assessed across three scenarios of increasing violation severity and sample sizes (20, 30, 50, 100 and 500). Performance metrics include bias, variance, mean squared error (MSE), and efficiency. Results revealed that HARSGMM and RSGMM consistently outperformed traditional estimators in terms of lower bias and MSE, particularly in settings with high assumption violations and larger samples. Even under baseline conditions with minimal violations, the proposed estimators maintained superior efficiency. These findings support the adoption of HARSGMM and RSGMM as more reliable alternatives for empirical researchers dealing with complex panel datasets where autocorrelation and heteroscedasticity are present. The study concluded with recommendations for broader application and integration of these robust techniques into econometric software and policy-oriented research.