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With the violation of the assumption of homoskedasticity, least squaresestimators of the variance become inefficient and statistical inferenceconducted with invalid standard errors leads to misleading rejection rates.Despite a vast cross-sectional literature on the downward bias of robuststandard errors, the problem is not extensively covered in the panel dataframework. We investigate the consequences of the simultaneous presence ofsmall sample size, heteroskedasticity and data points that exhibit extremevalues in the covariates ('good leverage points') on the statistical inference.Focusing on one-way linear panel data models, we examine asymptotic and finitesample properties of a battery of heteroskedasticity-consistent estimatorsusing Monte Carlo simulations. We also propose a hybrid estimator of thevariance-covariance matrix. Results show that conventional standard errors arealways dominated by more conservative estimators of the variance, especially insmall samples. In addition, all types of HC standard errors have excellentperformances in terms of size and power tests under homoskedasticity.

Robust Inference in Panel Data Models: Some Effects of Heteroskedasticity and Leveraged Data in Small Samples