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Robust standard errors in small samples : some practical advice

Author: Guido Imbens; Michal Kolesár; National Bureau of Economic Research.
Publisher: Cambridge, Mass. : National Bureau of Economic Research, ©2012.
Series: Working paper series (National Bureau of Economic Research), no. 18478.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
In this paper we discuss the properties of confidence intervals for regression parameters based on robust standard errors. We discuss the motivation for a modification suggested by Bell and McCaffrey (2002) to improve the finite sample properties of the confidence intervals based on the conventional robust standard errors. We show that the Bell-McCaffrey modification is the natural extension of a principled approach  Read more...
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Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Guido Imbens; Michal Kolesár; National Bureau of Economic Research.
OCLC Number: 814230426
Notes: Title from http://www.nber.org/papers/18478 viewed October 24, 2012.
"October 2012."
Description: 1 online resource (28 pages).
Series Title: Working paper series (National Bureau of Economic Research), no. 18478.
Responsibility: Guido W. Imbens, Michal Kolesar.

Abstract:

In this paper we discuss the properties of confidence intervals for regression parameters based on robust standard errors. We discuss the motivation for a modification suggested by Bell and McCaffrey (2002) to improve the finite sample properties of the confidence intervals based on the conventional robust standard errors. We show that the Bell-McCaffrey modification is the natural extension of a principled approach to the Behrens-Fisher problem, and 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 sample sizes of fifty and more. We recommend researchers calculate the Bell-McCaffrey degrees-of-freedom adjustment to assess potential problems with conventional robust standard errors and use the modification as a matter of routine.

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