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Activity Number: 30 - SPEED: Statistics and Econometrics
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #329468
Title: Bootstrap and Asymptotic Inference with Multiway Clustering
Author(s): Matthew Webb* and James Gordon MacKinnon and Morten Ø Nielsen
Companies: Carleton University and Queen's University and Queen's University and CREATES
Keywords: CRVE; clustered data; multiway clustering; robust inference; wild bootstrap; grouped data

We study a cluster-robust variance estimator (CRVE) for regression models with clustering in two dimensions that was proposed in Cameron, Gelbach, and Miller (2011). We prove that this CRVE is consistent and yields valid inferences under precisely stated assumptions about moments and cluster sizes. We then propose several wild bootstrap procedures and prove that they are asymptotically valid. Simulations suggest that bootstrap inference tends to be much more accurate than inference based on the t - distribution, especially when there are few clusters in at least one dimension. An empirical example confirms that bootstrap inferences can differ substantially from conventional ones.

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