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Reworking Wild Bootstrap Based Inference for Clustered Errors

  • Matthew D. Webb

    ()

    (University of Calgary)

Many empirical projects involve estimation with clustered data. While estimation is straightforward, reliable inference can be challenging. Past research has suggested a number of bootstrap procedures when there are few clusters. I demonstrate, using Monte Carlo experiments, that these bootstrap procedures perform poorly with fewer than eleven clusters. With few clusters, the wild cluster bootstrap results in p-values that are not point identified. I suggest two alternative wild bootstrap procedures. Monte Carlo simulations provide evidence that a 6-point bootstrap weight distribution improves the reliability of inference. A brief empirical example concerning education tax credits highlights the implications of these findings.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1315.pdf
File Function: First version 2014
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Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1315.

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Length: 22 pages
Date of creation: Nov 2014
Date of revision:
Handle: RePEc:qed:wpaper:1315
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  1. Meyer, Bruce D, 1995. "Natural and Quasi-experiments in Economics," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 151-61, April.
  2. Russell Davidson & Emmanuel Flachaire, 2000. "The Wild Bootstrap, Tamed at Last," Econometric Society World Congress 2000 Contributed Papers 1413, Econometric Society.
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  8. 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.
  9. Timothy G. Conley & Christopher R. Taber, 2011. "Inference with "Difference in Differences" with a Small Number of Policy Changes," The Review of Economics and Statistics, MIT Press, vol. 93(1), pages 113-125, February.
  10. 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.
  11. DiNardo, John & Lee, David S., 2011. "Program Evaluation and Research Designs," Handbook of Labor Economics, Elsevier.
  12. James G. MacKinnon & Matthew D. Webb, 2015. "Wild Bootstrap Inference for Wildly Different Cluster Sizes," Working Papers 1314, Queen's University, Department of Economics.
  13. A. Colin Cameron & Douglas L. Miller, 2010. "Robust Inference with Clustered Data," Working Papers 106, University of California, Davis, Department of Economics.
  14. 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.
  15. Thompson, Samuel B., 2011. "Simple formulas for standard errors that cluster by both firm and time," Journal of Financial Economics, Elsevier, vol. 99(1), pages 1-10, January.
  16. William Rogers, 1994. "Regression standard errors in clustered samples," Stata Technical Bulletin, StataCorp LP, vol. 3(13).
  17. 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.
  18. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," Review of Economic Studies, Oxford University Press, vol. 72(1), pages 1-19.
  19. 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.
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