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Autoren: 
MacKinnon, James G.
Nielsen, Morten Ørregaard
Webb, Matthew
Datum: 
2019
Schriftenreihe/Nr.: 
Queen’s Economics Department Working Paper No. 1415
Zusammenfassung: 
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.
Schlagwörter: 
CRVE
grouped data
clustered data
cluster-robust variance estimator
two-way clustering
robust inference
wild cluster bootstrap
JEL: 
C15
C21
C23
Dokumentart: 
Working Paper

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