Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/188895
Authors: 
Djogbenou, Antoine
MacKinnon, James G.
Nielsen, Morten Ørregaard
Year of Publication: 
2017
Series/Report no.: 
Queen's Economics Department Working Paper 1383
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. Simulation experiments illustrate the theoretical results and show that all methods can work poorly in certain cases.
Subjects: 
Clustered data
cluster-robust variance estimator
CRVE
inference
wild bootstrap
wild cluster bootstrap
JEL: 
C15
C21
C23
Document Type: 
Working Paper
Social Media Mentions:

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.