QED Working Paper Number
1415

We study two cluster-robust variance estimators (CRVEs) for regression models with clustering in two dimensions and give conditions under which t-statistics based on each of them yield asymptotically valid inferences. In particular, one of the CRVEs requires stronger assumptions about the nature of the intra-cluster correlations. We then propose several wild bootstrap procedures and state conditions under which they are asymptotically valid for each type of t-statistic. Extensive simulations suggest that using certain bootstrap procedures with one of the t-statistics generally performs very well. An empirical example confirms that bootstrap inferences can differ substantially from conventional ones

Author(s)
James G. MacKinnon
Morten Ørregaard Nielsen
Matthew D. Webb
JEL Codes
Keywords
CRVE
grouped data
clustered data
cluster-robust variance estimator
two-way clustering
wild cluster bootstrap
robust inference
Working Paper