Wild Bootstrap Inference with Multiway Clustering and Serially Correlated Time Effects

This paper studies wild bootstrap-based inference for regression models with multiway clustering. Our proposed methods are multiway counterparts to the (one-way) wild cluster bootstrap approach introduced by Cameron et al. (2008). We establish the validity of our methods for studentized statistics. Theoretical results are provided, accommodating arbitrary serial dependence in the common time effects – an aspect excluded by existing two-way bootstrap-based approaches. Simulation experiments document the potential for enhanced inference with our novel approaches. We illustrate the effectiveness of the methods by revisiting an empirical study involving multiway clustered and serially correlated data.