Wild Bootstrap and Asymptotic Inference With Multiway Clustering

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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.

Original languageEnglish
JournalJournal of Business and Economic Statistics
Number of pages15
ISSN0735-0015
DOIs
Publication statusE-pub ahead of print - 1 Jan 2019

    Research areas

  • Cluster-robust variance estimator, Clustered data, CRVE, Grouped data, Robust inference, Wild cluster bootstrap

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