The School of Economics invites you to a School seminar presented by Morten Ø. Nielsen (Queens University, Canada)

Wild bootstrap and asymptotic inference with multiway clustering

Co-authors: 

James G. MacKinnon (Queens University, Canada) and Matt Webb (Carleton University)

Abstract 

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.