Bootstrap and Asymptotic Inference with Multiway Clustering

James MacKinnon, Morten Orregaard Nielsen and Matthew Webb

No 274712, Queen's Economics Department Working Papers from Queen's University - Department of Economics

Abstract: We study a cluster-robust variance estimator (CRVE) for regression models with clustering in two dimensions that was proposed in Cameron, Gelbach, 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: Financial; Economics (search for similar items in EconPapers)
Pages: 26
Date: 2017-08
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Citations: View citations in EconPapers (2)

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Working Paper: Bootstrap And Asymptotic Inference With Multiway Clustering (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:quedwp:274712

DOI: 10.22004/ag.econ.274712

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