Robust Standard Errors in Small Samples: Some Practical Advice

Guido Imbens and Michal Kolesár
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Michal Kolesár: Princeton University

The Review of Economics and Statistics, 2016, vol. 98, issue 4, 701-712

Abstract: 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.

Keywords: Behrens-Fisher Problem; Robust Standard Errors; Small Samples; Clustering (search for similar items in EconPapers)
JEL-codes: C14 C21 C52 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (111)

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Working Paper: Robust Standard Errors in Small Samples: Some Practical Advice (2012)
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