Wild Bootstrap Inference For Wildly Different Cluster Sizes

James MacKinnon and Matthew Webb

No 1314, Working Paper from Economics Department, Queen's University

Abstract: The cluster robust variance estimator (CRVE) relies on the number of clusters being sufficiently large. Monte Carlo evidence suggests that the "rule of 42" is not true for unbalanced clusters. Rejection frequencies are higher for datasets with 50 clusters proportional to U.S. state populations than with 50 balanced clusters. Using critical values based on the wild cluster bootstrap performs much better. However, this procedure fails when a small number of clusters is treated. We explain why CRVE t statistics and the wild bootstrap fail in this case, study the "effective number" of clusters, and simulate placebo laws with dummy variable regressors.

Keywords: CRVE; grouped data; clustered data; panel data; wild cluster bootstrap; placebo laws; effective number of clusters; bootstrap failure; difference in differences (search for similar items in EconPapers)
JEL-codes: C15 C21 C23 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2015-12
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (35)

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https://www.econ.queensu.ca/sites/econ.queensu.ca/files/qed_wp_1314.pdf First version 2015 (application/pdf)

Related works:
Journal Article: Wild Bootstrap Inference for Wildly Different Cluster Sizes (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1314

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