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Cited by 922

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Bootstrap-Based Improvements for Inference with Clustered Errors

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Cameron, A. Colin, Gelbach, Jonah B., Miller, Douglas L.·2007·SSRN Electronic Journal
DOI 10.2139/ssrn.956890
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Abstract

Microeconometrics researchers have increasingly realized the essential need to account for any within-group dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate cluster-robust or sandwich standard errors that permit quite general heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. In applications with few (5-30) clusters, standard asymptotic tests can over-reject considerably. We investigate more accurate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the much-cited di¤erences-in-di¤erences example of Bertrand, Mullainathan and Duflo (2004). In situations where standard methods lead to rejection rates in excess of ten percent (or more) for tests of nominal size 0:05, our methods can reduce this to five percent. In principle a pairs cluster bootstrap should work well, but in practice a Wild cluster bootstrap performs better.

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Metadata

Authors
Cameron, A. C., Gelbach, J. B., & Miller, D. L.
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2007
ISSN
1556-5068 (ISSN-L)
DOI
10.2139/ssrn.956890
OpenAlex
W2140968572
Cited by
922
References
5
Type
posted content
Language
en
Open access
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CoChat. (2026). Citation check: Bootstrap-Based Improvements for Inference with Clustered Errors. https://app.cochat.ai/papers/10.2139/ssrn.956890
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Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2007). Bootstrap-Based Improvements for Inference with Clustered Errors. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.956890

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