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Robust Standard Errors in Small Samples: Some Practical Advice

Guido W. Imbens1,2; Michal Kolesár3

1 National Bureau of Economic Research · 2 Stanford University · 3 Princeton University

The Review of Economics and Statistics 2016

We study the properties of heteroskedasticity-robust confidence intervals for regression parameters. We show that confidence intervals based on a degrees-of-freedom correction suggested by Bell and McCaffrey (2002) are a natural extension of a principled approach to the Behrens-Fisher problem. We suggest a further improvement for the case with clustering. We show that these standard errors can lead to substantial improvements in coverage rates even for samples with fifty or more clusters.We recommend that researchers routinely calculate the Bell-McCaffrey degrees-of-freedom adjustment to assess potential problems with conventional robust standard errors.

DOI
10.1162/rest_a_00552
Volume
98 (4)
Pages
701-712
Language
en
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