Validity Of Wild Bootstrap Inference With Clustered Errors

Antoine Djogbenou, James MacKinnon and Morten Nielsen

No 1383, Working Paper from Economics Department, Queen's University

Abstract: We study asymptotic inference based on cluster-robust variance estimators for regression models with clustered errors, focusing on the wild cluster bootstrap and the ordinary wild bootstrap. We stateconditions under which both asymptotic and bootstrap tests and confidence intervals will be asymptotically valid. These conditions put limits on the rates at which the cluster sizes can increase as the number of clusters tends to infinity. To include power in the analysis, we allow the data to be generated under sequences of local alternatives. Simulation experiments illustrate the theoretical results and show that all methods can work poorly in certain cases.

Keywords: clustered data; cluster-robust variance estimator; CRVE; inference; wild bootstrap; wild cluster bootstrap (search for similar items in EconPapers)
JEL-codes: C15 C21 C23 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2017-06
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1383

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