Arbeitspapier

Validity of Wild Bootstrap Inference with Clustered Errors

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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 state conditions 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.

Sprache
Englisch

Erschienen in
Series: Queen's Economics Department Working Paper ; No. 1383

Klassifikation
Wirtschaft
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
Thema
Clustered data
cluster-robust variance estimator
CRVE
inference
wild bootstrap
wild cluster bootstrap

Ereignis
Geistige Schöpfung
(wer)
Djogbenou, Antoine
MacKinnon, James G.
Nielsen, Morten Ørregaard
Ereignis
Veröffentlichung
(wer)
Queen's University, Department of Economics
(wo)
Kingston (Ontario)
(wann)
2017

Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
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Objekttyp

  • Arbeitspapier

Beteiligte

  • Djogbenou, Antoine
  • MacKinnon, James G.
  • Nielsen, Morten Ørregaard
  • Queen's University, Department of Economics

Entstanden

  • 2017

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