Reworking Wild Bootstrap Based Inference for Clustered Errors

Matthew Webb

No 274640, Queen's Economics Department Working Papers from Queen's University - Department of Economics

Abstract: Many empirical projects involve estimation with clustered data. While esti- mation 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: Financial Economics; Research Methods/Statistical Methods (search for similar items in EconPapers)
Pages: 23
Date: 2014-11
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/274640/files/qed_wp_1315.pdf (application/pdf)

Related works:
Journal Article: Reworking wild bootstrap‐based inference for clustered errors (2023)
Working Paper: Reworking Wild Bootstrap Based Inference For Clustered Errors (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ags:quedwp:274640

DOI: 10.22004/ag.econ.274640

Access Statistics for this paper

More papers in Queen's Economics Department Working Papers from Queen's University - Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().