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Wild Bootstrap Inference for Wildly Different Cluster Sizes

  • James G. MacKinnon

    ()

    (Queen)

  • Matthew D. Webb

    ()

    (University of Calgary)

The cluster robust variance estimator (CRVE) relies on the number of clusters being large. A shorthand "rule of 42'' has emerged, but we show that unbalanced clusters invalidate it. Monte Carlo evidence suggests that rejection frequencies are higher for datasets with 50 clusters proportional to US state populations rather than 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 this happens, study the ``effective number'' of clusters, and simulate placebo laws with dummy variable regressors to provide further evidence.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1314.pdf
File Function: First version 2015
Download Restriction: no

Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1314.

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Length: 43 pages
Date of creation: Feb 2015
Date of revision:
Handle: RePEc:qed:wpaper:1314
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  1. Russell Davidson & Emmanuel Flachaire, 2001. "The wild bootstrap, tamed at last," LSE Research Online Documents on Economics 6560, London School of Economics and Political Science, LSE Library.
  2. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
  3. Kloek, T, 1981. "OLS Estimation in a Model Where a Microvariable Is Explained by Aggregates and Contemporaneous Disturbances Are Equicorrelated," Econometrica, Econometric Society, vol. 49(1), pages 205-07, January.
  4. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
  5. Bester, C. Alan & Conley, Timothy G. & Hansen, Christian B., 2011. "Inference with dependent data using cluster covariance estimators," Journal of Econometrics, Elsevier, vol. 165(2), pages 137-151.
  6. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
  7. Matthew D. Webb, 2014. "Reworking Wild Bootstrap Based Inference for Clustered Errors," Working Papers 1315, Queen's University, Department of Economics.
  8. Stephen G. Donald & Kevin Lang, 2007. "Inference with Difference-in-Differences and Other Panel Data," The Review of Economics and Statistics, MIT Press, vol. 89(2), pages 221-233, May.
  9. Ibragimov, Rustam & Müller, Ulrich K., 2010. "t-Statistic Based Correlation and Heterogeneity Robust Inference," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(4), pages 453-468.
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