Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/188911 
Year of Publication: 
2018
Series/Report no.: 
Queen's Economics Department Working Paper No. 1399
Publisher: 
Queen's University, Department of Economics, Kingston (Ontario)
Abstract: 
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. Under a somewhat stronger set of conditions, we also derive formal Edgeworth expansions for the asymptotic and bootstrap test statistics. Simulation experiments illustrate the theoretical results, and the Edgeworth expansions explain the overrejection of the asymptotic test and shed light on the choice of auxiliary distribution for the wild bootstrap.
Subjects: 
Clustered data
cluster-robust variance estimator
CRVE
Edgeworth expansion
inference
wild bootstrap
wild cluster bootstrap
JEL: 
C15
C21
C23
Document Type: 
Working Paper

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