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Randomization inference for difference-in-differences with few treated clusters

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MacKinnon, James G., Webb, Matthew D.·2020·Journal of Econometrics

Abstract

Inference using difference-in-differences with clustered data requires care. Previous research has shown that, when there are few treated clusters, t-tests based on cluster-robust variance estimators (CRVEs) severely overreject, and different variants of the wild cluster bootstrap can either overreject or underreject dramatically. We study two randomization inference (RI) procedures. A procedure based on estimated coefficients may be unreliable when clusters are heterogeneous. A procedure based on t-statistics typically performs better (although by no means perfectly) under the null, but at the cost of some power loss. An empirical example demonstrates that alternative procedures can yield dramatically different inferences.

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Metadata

Authors
MacKinnon, J. G., & Webb, M. D.
Venue
Journal of Econometrics
Publisher
Elsevier BV
Published
2020-10
Volume
218
Issue
2
Pages
435-450
ISSN
0304-4076 (ISSN-L)
OpenAlex
W2258423801
Cited by
167
References
7
FWCI
12.09
Type
journal article
Language
en
Open access
green
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
Social Sciences and Humanities Research Council of Canada

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MacKinnon, J. G., & Webb, M. D. (2020). Randomization inference for difference-in-differences with few treated clusters. Journal of Econometrics. https://doi.org/10.1016/j.jeconom.2020.04.024

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