The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Wild Bootstrap Inference for Wildly Different Cluster Sizes
Abstract
Summary: The cluster robust variance estimator (CRVE) relies on the number of clusters being sufficiently large. Monte Carlo evidence suggests that the ‘rule of 42’ is not true for unbalanced clusters. Rejection frequencies are higher for datasets with 50 clusters proportional to US state populations than with 50 balanced clusters. Using critical values based on the wild cluster bootstrap performs much better. However, this procedure fails when a small number of clusters is treated. We explain why CRVE t statistics and the wild bootstrap fail in this case, study the ‘effective number’ of clusters and simulate placebo laws with dummy variable regressors. Copyright © 2016 John Wiley & Sons, Ltd.
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In the citing paper
“Although asymptotically consistent, CRVE can be biased downward when the number of clusters is small, leading to hypothesis tests with rejection rates that are too high.”
From Small-Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models (2018) · cites this paper as MacKinnon and Webb 2016
In the citing paper
“Like the bias of the CRVE estimator itself, the accuracy of the asymptotic approximations depends on design features such as the degree of imbalance across clusters, skewness or leverage of the covariates, and the similarity of cluster sizes (Tipton and Pustejovsky 2015; McCaffrey, Bell, and Botts 2001; MacKinnon and Webb 2016; Carter, Schnepel, and Steigerwald 2013).”
From Small-Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models (2018) · cites this paper as MacKinnon and Webb 2016
Citations
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Cited by
A Practitioner’s Guide to Cluster-Robust Inference
Colin Cameron, A., & Miller, D. L. (2015) · Journal of Human Resources · doi:10.3368/jhr.50.2.317 · Cited by 5,037
Fast and wild: Bootstrap inference in Stata using boottest
Roodman, D., Nielsen, M. Ø., MacKinnon, J. G., & Webb, M. D. (2019) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x19830877 · Cited by 1,007
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Cluster-robust inference: A guide to empirical practice
MacKinnon, J. G., Nielsen, M. Ø., & Webb, M. D. (2023) · Journal of Econometrics · doi:10.1016/j.jeconom.2022.04.001 · Cited by 407
Self-citationImplementing the panel event study
Clarke, D., & Tapia-Schythe, K. (2021) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x211063144 · Cited by 367
Metadata
- Authors
- MacKinnon, J. G., & Webb, M. D.
- Affiliations
- Department of Economics Queen's University Ontario Kingston Canada
- Department of Economics Carleton University Ontario Ottawa Canada
- Venue
- Journal of Applied Econometrics
- Publisher
- Wiley
- Published
- 2016-02-21
- Volume
- 32
- Issue
- 2
- Pages
- 233-254
- ISSN
- 0883-7252
- 1099-1255
- Cited by
- 454
- References
- 6
- Language
- en
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