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

James MacKinnon and Matthew Webb

Journal of Econometrics, 2020, vol. 218, issue 2, 435-450

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 RI procedures can yield inferences that differ dramatically from those of other methods.

Keywords: Cluster-robust inference; CRVE; Grouped data; Clustered data; Wild cluster bootstrap; Randomization inference; Difference-in-differences; DiD (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (56)

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Related works:
Working Paper: Randomization Inference For Difference-in-differences With Few Treated Clusters (2019)
Working Paper: Randomization Inference for Difference-in-Differences with Few Treated Clusters (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:218:y:2020:i:2:p:435-450

DOI: 10.1016/j.jeconom.2020.04.024

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