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Reworking Wild Bootstrap Based Inference for Clustered Errors

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  • Matthew D. Webb

    ()
    (University of Calgary)

Abstract

Many empirical projects are well suited to incorporating a linear difference-in-differences research design. While estimation is straightforward, reliable inference can be a challenge. Past research has not only demonstrated that estimated standard errors are biased dramatically downwards in models possessing a group clustered design, but has also suggested a number of bootstrap-based improvements to the inference procedure. In this paper, I first demonstrate using Monte Carlo experiments, that these bootstrap-based procedures and traditional cluster-robust standard errors perform poorly in situations with fewer than eleven clusters - a setting faced in many empirical applications. With few clusters, the wild cluster bootstrap-t procedure results in p-values that are not point identified. I subsequently introduce two easy-to-implement alternative procedures that involve the wild bootstrap. Further Monte Carlo simulations provide evidence that the use of a 6-point distribution with the wild bootstrap can improve the reliability of inference.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1315.pdf
File Function: First version 2013
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Bibliographic Info

Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1315.

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Length: 21 pages
Date of creation: Aug 2013
Date of revision:
Handle: RePEc:qed:wpaper:1315

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Related research

Keywords: CRVE; grouped data; clustered data; panel data; wild bootstrap; cluster wild bootstrap; difference in differences; placebo laws;

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References

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  1. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2007. "Bootstrap-Based Improvements for Inference with Clustered Errors," NBER Technical Working Papers 0344, National Bureau of Economic Research, Inc.
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  5. 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.
  6. James G. MacKinnon & Matthew D. Webb, 2014. "Wild Bootstrap Inference for Wildly Different Cluster Sizes," Working Papers 1314, Queen's University, Department of Economics.
  7. 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.
  8. Emmanuel Flachaire, 2001. "The Wild Bootstrap, Tamed at Last," STICERD - Distributional Analysis Research Programme Papers 58, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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Cited by:
  1. Makram El-Shagi & Claus Michelsen & Sebastian Rosenschon, 2014. "Regulation, Innovation and Technology Diffusion: Evidence from Building Energy Efficiency Standards in Germany," Discussion Papers of DIW Berlin 1371, DIW Berlin, German Institute for Economic Research.
  2. James G. MacKinnon & Matthew D. Webb, 2014. "Wild Bootstrap Inference for Wildly Different Cluster Sizes," Working Papers 1314, Queen's University, Department of Economics.
  3. Brewer, Mike & Crossley, Thomas F. & Joyce, Robert, 2013. "Inference with Difference-in-Differences Revisited," IZA Discussion Papers 7742, Institute for the Study of Labor (IZA).

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