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

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 overrej...

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Bibliographic Details
Published in:Journal of econometrics 2020-10, Vol.218 (2), p.435-450
Main Authors: MacKinnon, James G., Webb, Matthew D.
Format: Article
Language:English
Subjects:
Cluster analysis
Cluster-robust inference
Clustered data
CRVE
DiD
Difference-in-differences
Estimating techniques
Grouped data
Inference
Randomization inference
Statistical analysis
Statistical inference
Statistics
Variants
Wild cluster bootstrap
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Summary: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.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2020.04.024