Wild Bootstrap Randomization Inference for Few Treated Clusters

James MacKinnon and Matthew Webb

No 274730, Queen's Economics Department Working Papers from Queen's University - Department of Economics

Abstract: When there are few treated clusters in a pure treatment or difference-in-differences setting, t tests based on a cluster-robust variance estimator (CRVE) can severely over- reject. Although procedures based on the wild cluster bootstrap often work well when the number of treated clusters is not too small, they can either over-reject or under- reject seriously when it is. In a previous paper, we showed that procedures based on randomization inference (RI) can work well in such cases. However, RI can be imprac- tical when the number of clusters is small. We propose a bootstrap-based alternative to randomization inference, which mitigates the discrete nature of RI P values in the few-clusters case.

Keywords: Financial Economics; Research Methods/Statistical Methods (search for similar items in EconPapers)
Pages: 17
Date: 2018-03
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https://ageconsearch.umn.edu/record/274730/files/qed_wp_1404.pdf (application/pdf)

Related works:
Chapter: Wild Bootstrap Randomization Inference for Few Treated Clusters (2019)
Working Paper: Wild Bootstrap Randomization Inference For Few Treated Clusters (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:quedwp:274730

DOI: 10.22004/ag.econ.274730

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