Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/281089 
Erscheinungsjahr: 
2022
Schriftenreihe/Nr.: 
Queen’s Economics Department Working Paper No. 1485
Verlag: 
Queen's University, Department of Economics, Kingston (Ontario)
Zusammenfassung: 
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.
Schlagwörter: 
bootstrap
clustered data
grouped data
cluster-robust variance estima-tor
CRVE
cluster sizes
jackknife
wild cluster bootstrap
JEL: 
C10
C12
C21
C23
Dokumentart: 
Working Paper

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