Wild Bootstrap and Asymptotic Inference with Multiway Clustering
James MacKinnon (),
Morten Nielsen () and
No 1415, Working Paper from Economics Department, Queen's University
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
Keywords: CRVE; grouped data; clustered data; cluster-robust variance estimator; two-way clustering; wild cluster bootstrap; robust inference (search for similar items in EconPapers)
JEL-codes: C15 C21 C23 (search for similar items in EconPapers)
Pages: 33 pages
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Working Paper: Wild Bootstrap and Asymptotic Inference with Multiway Clustering (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1415
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