Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/188898
Authors: 
MacKinnon, James G.
Nielsen, Morten Ørregaard
Webb, Matthew D.
Year of Publication: 
2017
Series/Report no.: 
Queen's Economics Department Working Paper 1386
Abstract: 
We study a cluster-robust variance estimator (CRVE) for regression models with clustering in two dimensions that was proposed in Cameron, Gelbach, and Miller (2011). We prove that this CRVE is consistent and yields valid inferences under precisely stated assumptions about moments and cluster sizes. We then propose several wild bootstrap procedures and prove that they are asymptotically valid. Simulations suggest that bootstrap inference tends to be much more accurate than inference based on the t distribution, especially when there are few clusters in at least one dimension. An empirical example confirms that bootstrap inferences can differ substantially from conventional ones.
Subjects: 
CRVE
grouped data
clustered data
cluster-robust variance estimator
multiway clustering
robust inference
wild bootstrap
wild cluster bootstrap
JEL: 
C15
C21
C23
Document Type: 
Working Paper
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