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.

翻译：在违反同方差性假设的情况下，方差的最小二乘估计量变得低效，且基于无效标准误进行的统计推断会导致误导性的拒绝率。尽管截面数据文献已广泛探讨稳健标准误的向下偏误问题，但在面板数据框架下这一问题尚未得到充分研究。我们考察了小样本规模、异方差性与协变量中存在极端值的数据点（即“优质杠杆点”）同时出现对统计推断的影响。聚焦于单向线性面板数据模型，我们通过蒙特卡洛模拟研究了一组异方差一致性估计量的渐近与有限样本性质，并提出了一种方差-协方差矩阵的混合估计量。结果表明，传统标准误始终被更为保守的方差估计量所超越，尤其是在小样本中。此外，在同方差性下，各类HC标准误在检验规模与检验功效方面均表现优异。