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
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Series: Queen’s Economics Department Working Paper ; No. 1415
- Klassifikation
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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
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CRVE
grouped data
clustered data
cluster-robust variance estimator
two-way clustering
robust inference
wild cluster bootstrap
- Ereignis
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Geistige Schöpfung
- (wer)
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MacKinnon, James G.
Nielsen, Morten Ørregaard
Webb, Matthew
- Ereignis
-
Veröffentlichung
- (wer)
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Queen's University, Department of Economics
- (wo)
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Kingston (Ontario)
- (wann)
-
2019
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:42 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- MacKinnon, James G.
- Nielsen, Morten Ørregaard
- Webb, Matthew
- Queen's University, Department of Economics
Entstanden
- 2019