The Wild Bootstrap For Few (treated) Clusters

James MacKinnon and Matthew Webb

No 1364, Working Paper from Economics Department, Queen's University

Abstract: Inference based on cluster-robust standard errors in linear regression models, using either the Student's t distribution or the wild cluster bootstrap, is known to fail when the number of treated clusters is very small. We propose a family of new procedures calledthe subcluster wild bootstrap, which includes the ordinary wild bootstrap as a limiting case. In the case of pure treatment models, where all observations within clusters are either treated or not, the latter procedure can work remarkably well. The key requirement is that all cluster sizes, regardless of treatment, should be similar. Unfortunately, the analogue of this requirement is not likely to hold for difference-in-differences regressions. Our theoretical results are supported by extensive simulations and an empirical example.

Keywords: robust inference; CRVE; grouped data; clustered data; wild bootstrap; wild cluster bootstrap; subclustering; treatment model; difference in differences (search for similar items in EconPapers)
JEL-codes: C15 C21 C23 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2017-11
New Economics Papers: this item is included in nep-ecm
References: Add references at CitEc
Citations: View citations in EconPapers (9)

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Related works:
Journal Article: The wild bootstrap for few (treated) clusters (2018)
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