  With the violation of the assumption of homoskedasticity, least squares
estimators of the variance become inefficient and statistical inference
conducted with invalid standard errors leads to misleading rejection rates.
Despite a vast cross-sectional literature on the downward bias of robust
standard errors, the problem is not extensively covered in the panel data
framework. We investigate the consequences of the simultaneous presence of
small sample size, heteroskedasticity and data points that exhibit extreme
values in the covariates ('good leverage points') on the statistical inference.
Focusing on one-way linear panel data models, we examine asymptotic and finite
sample properties of a battery of heteroskedasticity-consistent estimators
using Monte Carlo simulations. We also propose a hybrid estimator of the
variance-covariance matrix. Results show that conventional standard errors are
always dominated by more conservative estimators of the variance, especially in
small samples. In addition, all types of HC standard errors have excellent
performances in terms of size and power tests under homoskedasticity.
