Wild Bootstrap and Asymptotic Inference with Multiway Clustering
James MacKinnon (),
Morten Nielsen () and
CREATES Research Papers from Department of Economics and Business Economics, Aarhus 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; robust inference; wild cluster bootstrap (search for similar items in EconPapers)
JEL-codes: C15 C21 C23 C25 C36 (search for similar items in EconPapers)
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Working Paper: Wild Bootstrap and Asymptotic Inference with Multiway Clustering (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2020-06
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