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