Randomization inference for difference-in-differences with few treated clusters
By James G. MacKinnon, Matthew D. Webb. 2020
162 citations recorded by the source corpus.
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.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1970e2561f369e01d18097ea65d2542dc987f877
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In Statistics and Probability
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- Does a Carbon Tax Reduce CO2 Emissions? Evidence from British Columbia (2019, methodology): “05 ** * 41 .2 1* ** 1 3 construction of standard errors with few treated units, I also use randomisation inference (see e.g. MacKinnon and Webb 2019; Buchmueller et al.”