Tests based on heteroskedasticity robust standard errors are an important technique in econometric practice. Choosing the right critical value, however, is not simple at all: conventional critical values based on asymptotics often lead to severe size distortions; and so do existing adjustments including the bootstrap. To avoid these issues, we suggest to use smallest size-controlling critical values, the generic existence of which we prove in this article for the commonly used test statistics. Furthermore, sufficient and often also necessary conditions for their existence are given that are easy to check. Granted their existence, these critical values are the canonical choice: larger critical values result in unnecessary power loss, whereas smaller critical values lead to over-rejections under the null hypothesis, make spurious discoveries more likely, and thus are invalid. We suggest algorithms to numerically determine the proposed critical values and provide implementations in accompanying software. Finally, we numerically study the behavior of the proposed testing procedures, including their power properties.

翻译：基于异方差稳健标准误的检验是计量经济学实践中的一项重要技术。然而，选择正确的临界值绝非易事：传统的基于渐近性的临界值常导致严重的尺度扭曲；现有调整方法（包括自助法）亦存在此问题。为避免这些缺陷，我们建议使用最小控制尺度的临界值，并在本文中证明这类临界值在常用检验统计量中具有普遍存在性。此外，我们给出了易于检验的充分条件（通常也是必要条件）。若临界值存在，它们将是规范选择：更大的临界值会导致不必要的功效损失，而更小的临界值则会在原假设下造成过度拒绝，增加虚假发现的概率，因而是无效的。我们提出数值算法以确定建议的临界值，并提供了配套软件实现。最后，我们通过数值方法研究了所提检验程序的行为，包括其功效特性。