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Validity of Wild Bootstrap Inference with Clustered Errors

Author

Listed:
  • Antoine Djogbenou

    () (Queen's University)

  • James G. MacKinnon

    () (Queen's University)

  • Morten Ørregaard Nielsen

    () (Queen's University)

Abstract

We study asymptotic inference based on cluster-robust variance estimators for regression models with clustered errors, focusing on the wild cluster bootstrap and the ordinary wild bootstrap. We state conditions under which both asymptotic and bootstrap tests and confidence intervals will be asymptotically valid. These conditions put limits on the rates at which the cluster sizes can increase as the number of clusters tends to infinity. To include power in the analysis, we allow the data to be generated under sequences of local alternatives. Simulation experiments illustrate the theoretical results and show that all methods can work poorly in certain cases.

Suggested Citation

  • Antoine Djogbenou & James G. MacKinnon & Morten Ørregaard Nielsen, 2017. "Validity of Wild Bootstrap Inference with Clustered Errors," Working Papers 1383, Queen's University, Department of Economics.
  • Handle: RePEc:qed:wpaper:1383
    as

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    File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1383.pdf
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    References listed on IDEAS

    as
    1. Guido W. Imbens & Michal Kolesár, 2016. "Robust Standard Errors in Small Samples: Some Practical Advice," The Review of Economics and Statistics, MIT Press, vol. 98(4), pages 701-712, October.
    2. James G. MacKinnon & Matthew D. Webb, 2017. "The Wild Bootstrap for Few (Treated) Clusters," Working Papers 1364, Queen's University, Department of Economics.
    3. 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.
    4. Davidson, Russell & Flachaire, Emmanuel, 2008. "The wild bootstrap, tamed at last," Journal of Econometrics, Elsevier, vol. 146(1), pages 162-169, September.
    5. James G. MacKinnon, 2002. "Bootstrap inference in econometrics," Canadian Journal of Economics, Canadian Economics Association, vol. 35(4), pages 615-645, November.
    6. repec:tpr:restat:v:99:y:2017:i:4:p:698-709 is not listed on IDEAS
    7. Hansen, Christian B., 2007. "Asymptotic properties of a robust variance matrix estimator for panel data when T is large," Journal of Econometrics, Elsevier, vol. 141(2), pages 597-620, December.
    8. Davidson, Russell & MacKinnon, James G., 1999. "The Size Distortion Of Bootstrap Tests," Econometric Theory, Cambridge University Press, vol. 15(03), pages 361-376, June.
    9. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    10. repec:wly:japmet:v:32:y:2017:i:2:p:233-254 is not listed on IDEAS
    11. Moulton, Brent R., 1986. "Random group effects and the precision of regression estimates," Journal of Econometrics, Elsevier, vol. 32(3), pages 385-397, August.
    12. MacKinnon, James G., 2016. "Inference with Large Clustered Datasets," L'Actualité Economique, Société Canadienne de Science Economique, vol. 92(4), pages 649-665, Décembre.
    13. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    14. Arellano, M, 1987. "Computing Robust Standard Errors for Within-Groups Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 49(4), pages 431-434, November.
    15. MacKinnon, James G. & White, Halbert, 1985. "Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties," Journal of Econometrics, Elsevier, vol. 29(3), pages 305-325, September.
    16. 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.
    17. James G. Mackinnon & Matthew D. Webb, 2017. "Wild Bootstrap Inference for Wildly Different Cluster Sizes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 233-254, March.
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    Cited by:

    1. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2017. "Bootstrap and Asymptotic Inference with Multiway Clustering," Working Papers 1386, Queen's University, Department of Economics.
    2. Antoine A. Djogbenou, 2018. "Comovements in the Real Activity of Developed and Emerging Economies: A Test of Global versus Specific International Factors," Working Papers 1392, Queen's University, Department of Economics.

    More about this item

    Keywords

    clustered data; cluster-robust variance estimator; CRVE; inference; wild bootstrap; wild cluster bootstrap;

    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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