  Tests based on heteroskedasticity robust standard errors are an important
technique in econometric practice. Choosing the right critical value, however,
is not all that simple: 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.
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.
