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Wild Bootstrap and Asymptotic Inference With Multiway Clustering

2019; Taylor & Francis; Volume: 39; Issue: 2 Linguagem: Inglês

10.1080/07350015.2019.1677473

1537-2707

James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb,

Statistical Methods and Inference

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.