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
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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