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Robust Standard Errors in Small Samples: Some Practical Advice

Guido W. Imbens and Michal Kolesár

Posted Online September 28, 2016
https://doi.org/10.1162/REST_a_00552

Review of Economics and Statistics

Volume 98 | Issue 4 | October 2016
p.701-712
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Robust Standard Errors in Small Samples: Some Practical Advice

Guido W. Imbens and Michal Kolesár
https://doi.org/10.1162/REST_a_00552
Received: November 22, 2013
Accepted: May 07, 2015
Published Online: September 28, 2016
Abstract

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.

Guido W. Imbens
Stanford University and NBER
Michal Kolesár
Princeton University
https://doi.org/10.1162/REST_a_00552
Abstract

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.

Guido W. Imbens
Stanford University and NBER
Michal Kolesár
Princeton University
Download Options

Robust Standard Errors in Small Samples: Some Practical Advice

Guido W. Imbens and Michal Kolesár
https://doi.org/10.1162/REST_a_00552
Received: November 22, 2013
Accepted: May 07, 2015
Published Online: September 28, 2016
Abstract

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.

Guido W. Imbens
Stanford University and NBER
Michal Kolesár
Princeton University
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