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Reworking Wild Bootstrap Based Inference for Clustered Errors

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Listed author(s):
  • Matthew D. Webb

    ()

    (University of Calgary)

Abstract

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
Handle: RePEc:qed:wpaper:1315
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Web page: http://qed.econ.queensu.ca/
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Keywords: CRVE; grouped data; clustered data; panel data; wild bootstrap; wild cluster bootstrap; difference in differences; placebo laws;

Find related papers by JEL classification:

  • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
  • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
  • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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  17. Jonathan Gruber & James Poterba, 1994. "Tax Incentives and the Decision to Purchase Health Insurance: Evidence from the Self-Employed," The Quarterly Journal of Economics, Oxford University Press, vol. 109(3), pages 701-733.
  18. James G. MacKinnon & Matthew D. Webb, 2015. "Wild Bootstrap Inference for Wildly Different Cluster Sizes," Working Papers 1314, Queen's University, Department of Economics.
  19. 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.
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