Bootstrap And Asymptotic Inference With Multiway Clustering

James MacKinnon, Matthew Webb and Morten Nielsen

No 1386, Working Paper from Economics Department, Queen's University

Abstract: We study a cluster-robust variance estimator (CRVE) for regression models with clustering in two dimensions that was proposed in Cameron, Gelback, and Miller (2011). We prove that this CRVE is consistent and yields valid inferences under precisely stated assumptions about moments and cluster sizes. We then propose several wild bootstrap procedures and prove that they are asymptotically valid. Simulations suggest that bootstrap inference tends to be much more accurate than inference based on the t distribution, especially when there are few clusters in at least one dimension. An empirical example confirms that bootstrap inferences can differ substantially from conventional ones.

Keywords: clustered data; cluster-robust variance estimator; CRVE; wild bootstrap; wild cluster bootstrap; two-way clustering (search for similar items in EconPapers)
JEL-codes: C15 C21 C23 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2017-08
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1386

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