Robust Estimation Techniques in Panel Data Models in the Presence of Multicollinearity, Heteroscedasticity, and Autocorrelation Violations

This study proposed and evaluated three novel robust estimators—Robust Shrinkage Generalized Method of Moments (RSGMM), Panel Adaptive Ridge GMM (PARGMM), and Heteroscedasticity-Autocorrelation-Robust Shrinkage GMM (HARSGMM)—for panel data models where classical assumptions are frequently violated. The 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 were assessed across three scenarios of increasing violation severity and varying sample sizes. 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. The study concluded with recommendations for broader application and integration of these robust techniques into econometric software and policy-oriented research.