 The Subcluster Wild Bootstrap for Few (Treated) ClustersJames MacKinnon and Matthew Webb No 16-13, Carleton Economic Papers from Carleton University, Department of Economics Abstract: Inference based on cluster-robust standard errors is known to fail when the number of clusters is small, and the wild cluster bootstrap fails dramatically when the number of treated clusters is very small. We propose a family of new procedures called the subcluster wild bootstrap. In the case of pure treatment models, where all the observations in each cluster are either treated or not, the new procedures can work astonishingly well. The key requirement is that the sizes of the treated and untreated clusters should be very similar. Unfortunately, the analog 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: CRVE; grouped data; clustered data; wild bootstrap; wild cluster bootstrap subclustering; treatment model; difference in differences; robust inference (search for similar items in EconPapers) Published: Carleton Economic Papers Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:car:carecp:16-13 Ordering information: This working paper can be ordered from Access Statistics for this paper More papers in Carleton Economic Papers from Carleton University, Department of Economics C870 Loeb Building, 1125 Colonel By Drive, Ottawa Ontario, K1S 5B6 Canada. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.