Heteroskedasticity-Robust Inference in Finite Samples

10 Pages Posted: 25 Dec 2011 Last revised: 11 Nov 2021

See all articles by Jerry A. Hausman

Jerry A. Hausman

Massachusetts Institute of Technology (MIT) - Department of Economics; National Bureau of Economic Research (NBER)

Christopher Palmer

MIT Sloan; National Bureau of Economic Research (NBER)

Date Written: December 2011

Abstract

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.

Suggested Citation

Hausman, Jerry A. and Palmer, Christopher, Heteroskedasticity-Robust Inference in Finite Samples (December 2011). NBER Working Paper No. w17698, Available at SSRN: https://ssrn.com/abstract=1976514

Jerry A. Hausman (Contact Author)

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

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Christopher Palmer

MIT Sloan ( email )

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