%0 Web Page %A MacKinnon, James G. %A Nielsen, Morten Ørregaard %A Webb, Matthew D. %D 2019 %T Wild Bootstrap and Asymptotic Inference with Multiway Clustering %U https://tandf.figshare.com/articles/online_resource/Wild_Bootstrap_and_Asymptotic_Inference_with_Multiway_Clustering/9976895 %R 10.6084/m9.figshare.9976895.v1 %K CRVE %K grouped data %K clustered data %K cluster-robust variance estimator %K two-way clustering %K robust inference %K wild cluster bootstrap %K C15 %K C21 %K C23 %X

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.

%I Taylor & Francis