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Wild Bootstrap and Asymptotic Inference With Multiway Clustering
Version 3 2021-09-29, 15:53
Version 2 2019-12-03, 00:18
Version 1 2019-10-14, 14:03
dataset
posted on 2021-09-29, 15:53 authored by James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. WebbWe 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.