Reworking Wild Bootstrap Based Inference For Clustered Errors

Matthew Webb

No 1315, Working Paper from Economics Department, Queen's University

Abstract: Many empirical projects involve estimation with clustered data. While estimation is straightforward, reliable inference can be challenging. Past research has suggested a number of bootstrap procedures when there are few clusters. I demonstrate, using Monte Carlo experiments, that these bootstrap procedures perform poorly with fewer than eleven clusters. With few clusters, the wild cluster bootstrap results in p-values that are not point identified. I suggest two alternative wild bootstrap procedures. Monte Carlo simulations provide evidence that a 6-point bootstrap weight distribution improves the reliability of inference. A brief empirical example concerning education tax credits highlights the implications of these findings.

Keywords: CRVE; grouped data; clustered data; panel data; wild bootstrap; wild cluster bootstrap; difference in differences; placebo laws (search for similar items in EconPapers)
JEL-codes: C15 C21 C23 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2014-11
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 (107)

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https://www.econ.queensu.ca/sites/econ.queensu.ca/files/qed_wp_1315.pdf First version 2014 (application/pdf)

Related works:
Journal Article: Reworking wild bootstrap‐based inference for clustered errors (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1315

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