Randomization Inference For Difference-in-differences With Few Treated Clusters

James MacKinnon and Matthew Webb

No 1355, Working Paper from Economics Department, Queen's University

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.

Keywords: CRVE; grouped data; clustered data; panel data; randomization inference; difference-in-differences; wild cluster bootstrap; DiD (search for similar items in EconPapers)
JEL-codes: C12 C21 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2019-01
New Economics Papers: this item is included in nep-ecm
References: Add references at CitEc
Citations: View citations in EconPapers (18)

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https://www.econ.queensu.ca/sites/econ.queensu.ca/files/files/qed_wp_1355.pdf First version 2019 (application/pdf)

Related works:
Journal Article: Randomization inference for difference-in-differences with few treated clusters (2020)
Working Paper: Randomization Inference for Difference-in-Differences with Few Treated Clusters (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1355

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