Arbeitspapier

Robust inference with multi-way clustering

In this paper we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit and GMM. This variance estimator enables cluster-robust inference when there is two-way or multi-way clustering that is non-nested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already oþer cluster-robust standard errors when there is one-way clustering. The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state-year eþects example of Bertrand et al. (2004) to two dimensions; and by application to studies in the empirical literature where two-way clustering is present.

Sprache
Englisch

Erschienen in
Series: Working Paper ; No. 09-9

Klassifikation
Wirtschaft
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
cluster-robust standard errors
two-way clustering
multi-way clustering

Ereignis
Geistige Schöpfung
(wer)
Miller, Douglas L.
Cameron, A. Colin
Gelbach, Jonah
Ereignis
Veröffentlichung
(wer)
University of California, Department of Economics
(wo)
Davis, CA
(wann)
2009

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.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Miller, Douglas L.
  • Cameron, A. Colin
  • Gelbach, Jonah
  • University of California, Department of Economics

Entstanden

  • 2009

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