Wild Bootstrap and Asymptotic Inference With Multiway Clustering

We study two cluster-robust variance estimators (CRVEs) for regression models with clustering in two dimensions and give conditions under which <i>t</i>-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 <i>t</i>-statistic. Extensive simulations suggest that using certain bootstrap procedures with one of the <i>t</i>-statistics generally performs very well. An empirical example confirms that bootstrap inferences can differ substantially from conventional ones.