Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression

Key Points

• To examine the asymptotic consistency of conventional heteroskedasticity-robust variance estimators in fixed effects panel data models and develop a robust alternative.
• Evaluated the mathematical properties of conventional cross-sectional heteroskedasticity-robust variance matrix estimators applied to panel data when the number of time periods (T > 2) is fixed while the number of entities (n) increases.
• Formulated a bias-adjusted heteroskedasticity-robust estimator and extended its framework to accommodate fixed-order serial correlation.
• Demonstrated that traditional heteroskedasticity-robust variance matrix estimators, with or without degrees-of-freedom adjustments, are inconsistent when T is fixed and n approaches infinity.
• Established that the proposed bias-adjusted estimator is root-nT-consistent across any asymptotic sequences where n, T, or both grow infinitely.