A Monte Carlo study of 608 real regressions finds that HC1 and HC2 with default degrees of freedom over-reject, and customized degrees-of-freedom methods, including a new partial-leverage approach, perform best.
The factor vb is an n × 1 vector of random weights independently drawn from a Rademacher distribution vb i = ( −1 with probability 0.5 1 with probability 0.5 (24)
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From Replications to Revelations: Heteroskedasticity-Robust Inference
A Monte Carlo study of 608 real regressions finds that HC1 and HC2 with default degrees of freedom over-reject, and customized degrees-of-freedom methods, including a new partial-leverage approach, perform best.