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Erscheinungsjahr: 
2017
Schriftenreihe/Nr.: 
Queen's Economics Department Working Paper No. 1383
Verlag: 
Queen's University, Department of Economics, Kingston (Ontario)
Zusammenfassung: 
We study asymptotic inference based on cluster-robust variance estimators for regression models with clustered errors, focusing on the wild cluster bootstrap and the ordinary wild bootstrap. We state conditions under which both asymptotic and bootstrap tests and confidence intervals will be asymptotically valid. These conditions put limits on the rates at which the cluster sizes can increase as the number of clusters tends to infinity. To include power in the analysis, we allow the data to be generated under sequences of local alternatives. Simulation experiments illustrate the theoretical results and show that all methods can work poorly in certain cases.
Schlagwörter: 
Clustered data
cluster-robust variance estimator
CRVE
inference
wild bootstrap
wild cluster bootstrap
JEL: 
C15
C21
C23
Dokumentart: 
Working Paper

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