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Wild Bootstrap and Asymptotic Inference With Multiway Clustering

posted on 03.12.2019, 00:18 by James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb

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.


MacKinnon and Webb thank the Social Sciences and Humanities Research Council of Canada (SSHRC) for financial support grant 435-2016-0871. Nielsen thanks the Canada Research Chairs program, the SSHRC (grant 435-2017-0131), and the Center for Research in Econometric Analysis of Time Series (CREATES, funded by the Danish National Research Foundation, DNRF78) for financial support. Some of the computations were performed at the Centre for Advanced Computing at Queen’s University.