Wild Bootstrap and Asymptotic Inference With Multiway Clustering

James MacKinnon, Morten Nielsen and Matthew Webb

Journal of Business & Economic Statistics, 2021, vol. 39, issue 2, 505-519

Abstract: We study two cluster-robust variance estimators (CRVEs) for regression models with clustering in two dimensions and give conditions under which t-statistics based on each of them yield asymptotically valid inferences. In particular, one of the CRVEs requires stronger assumptions about the nature of the intra-cluster correlations. We then propose several wild bootstrap procedures and state conditions under which they are asymptotically valid for each type of t-statistic. Extensive simulations suggest that using certain bootstrap procedures with one of the t-statistics generally performs very well. An empirical example confirms that bootstrap inferences can differ substantially from conventional ones.

Date: 2021
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Citations: View citations in EconPapers (33)

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Related works:
Working Paper: Wild Bootstrap and Asymptotic Inference with Multiway Clustering (2020)
Working Paper: Wild Bootstrap and Asymptotic Inference with Multiway Clustering (2019)
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DOI: 10.1080/07350015.2019.1677473

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