Arbeitspapier

Fast and reliable jackknife and bootstrap methods for cluster-robust inference

zu Verbundenen Objekten

We provide new and computationally attractive methods, based on jackknifing by cluster, to obtain cluster-robust variance matrix estimators (CRVEs) for linear regres- sion models estimated by least squares. These estimators have previously been com- putationally infeasible except for small samples. We also propose several new variants of the wild cluster bootstrap, which involve the new CRVEs, jackknife-based bootstrap data-generating processes, or both. Extensive simulation experiments suggest that the new methods can provide much more reliable inferences than existing ones in cases where the latter are not trustworthy, such as when the number of clusters is small and/or cluster sizes vary substantially.

Sprache
Englisch

Erschienen in
Series: Queen’s Economics Department Working Paper ; No. 1485

Klassifikation
Wirtschaft
Econometric and Statistical Methods and Methodology: General
Hypothesis Testing: 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
bootstrap
clustered data
grouped data
cluster-robust variance estima-tor
CRVE
cluster sizes
jackknife
wild cluster bootstrap

Ereignis
Geistige Schöpfung
(wer)
MacKinnon, James G.
Nielsen, Morten Ørregaard
Webb, Matthew
Ereignis
Veröffentlichung
(wer)
Queen's University, Department of Economics
(wo)
Kingston (Ontario)
(wann)
2022

Handle
Letzte Aktualisierung
10.03.2025, 11:44 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

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

Entstanden

  • 2022

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