Heteroskedasticity-Robust Inference in Finite Samples
Since the advent of heteroskedasticity-robust standard errors, several papers have proposed adjustments to the original White formulation. We replicate earlier findings that each of these adjusted estimators performs quite poorly in finite samples. We propose a class of alternative heteroskedasticity-robust tests of linear hypotheses based on an Edgeworth expansions of the test statistic distribution. Our preferred test outperforms existing methods in both size and power for low, moderate, and severe levels of heteroskedasticity.
The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. Palmer acknowledges support from the National Science Foundation Graduate Research Fellowship under Grant No. 0645960.