  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.
