Arbeitspapier

Wild bootstrap and asymptotic inference with multiway clustering

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.

Sprache
Englisch

Erschienen in
Series: Queen’s Economics Department Working Paper ; No. 1415

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
CRVE
grouped data
clustered data
cluster-robust variance estimator
two-way clustering
robust inference
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)
2019

Handle
Letzte Aktualisierung
10.03.2025, 11:42 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.

Objekttyp

  • Arbeitspapier

Beteiligte

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

Entstanden

  • 2019

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