IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/6560.html
   My bibliography  Save this paper

The wild bootstrap, tamed at last

Author

Listed:
  • Davidson, Russell
  • Flachaire, Emmanuel

Abstract

Various versions of the wild bootstrap are studied as applied to regression models with heteroskedastic errors. It is shown that some versions can be qualified as 'tamed', in the sense that the statistic bootstrapped is asymptotically independent of the distribution of the wild bootstrap DGP. This can, in one very specific case, lead to perfect bootstrap inference, and leads to substantial reduction in the error in the rejection probability of a bootstrap test much more generally. However, the version of the wild bootstrap with this desirable property does not benefit from the skewness correction afforded by the most popular version of the wild bootstrap in the literature. Edgeworth expansions and simulation experiments are used to show why this defect does not prevent the preferred version from having the smallest error in rejection probability in small and medium-sized samples. It is concluded that this preferred version should always be used in practice.

Suggested Citation

  • Davidson, Russell & Flachaire, Emmanuel, 2001. "The wild bootstrap, tamed at last," LSE Research Online Documents on Economics 6560, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:6560
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/6560/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589833, September.
    2. 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.
    3. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    4. Davidson, Russell & MacKinnon, James G, 1998. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," The Manchester School of Economic & Social Studies, University of Manchester, vol. 66(1), pages 1-26, January.
    5. Chesher, Andrew & Jewitt, Ian, 1987. "The Bias of a Heteroskedasticity Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 55(5), pages 1217-1222, September.
    6. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589819, September.
    7. Davidson, Russell & MacKinnon, James G., 1999. "The Size Distortion Of Bootstrap Tests," Econometric Theory, Cambridge University Press, vol. 15(3), pages 361-376, June.
    8. Joel L. Horowitz, 1996. "Bootstrap Methods in Econometrics: Theory and Numerical Performance," Econometrics 9602009, University Library of Munich, Germany, revised 05 Mar 1996.
    9. Flachaire, Emmanuel, 1999. "A better way to bootstrap pairs," Economics Letters, Elsevier, vol. 64(3), pages 257-262, September.
    10. 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.
    11. Kreps,David M. & Wallis,Kenneth F. (ed.), 1997. "Advances in Economics and Econometrics: Theory and Applications," Cambridge Books, Cambridge University Press, number 9780521589826, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pötscher, Benedikt M. & Preinerstorfer, David, 2023. "How Reliable Are Bootstrap-Based Heteroskedasticity Robust Tests?," Econometric Theory, Cambridge University Press, vol. 39(4), pages 789-847, August.
    2. Torben Klarl, 2014. "Is Spatial Bootstrapping A Panacea For Valid Inference?," Journal of Regional Science, Wiley Blackwell, vol. 54(2), pages 304-312, March.
    3. Joel L. Horowitz, 2018. "Bootstrap Methods in Econometrics," Papers 1809.04016, arXiv.org.
    4. Joel L. Horowitz, 2018. "Bootstrap methods in econometrics," CeMMAP working papers CWP53/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Flachaire, Emmanuel, 2005. "Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 361-376, April.
    6. Davidson, Russell & MacKinnon, James G., 2006. "The power of bootstrap and asymptotic tests," Journal of Econometrics, Elsevier, vol. 133(2), pages 421-441, August.
    7. Emmanuel Flachaire, 2005. "Propriétés en échantillon fini des tests robustes à l'hétéroscédasticité de forme inconnue," Annals of Economics and Statistics, GENES, issue 77, pages 187-199.
    8. James G. MacKinnon, 2012. "Thirty Years Of Heteroskedasticity-robust Inference," Working Paper 1268, Economics Department, Queen's University.
    9. Bergström, Pål, 1999. "Bootstrap Methods and Applications in Econometrics - A Brief Survey," Working Paper Series 1999:2, Uppsala University, Department of Economics.
    10. Jamel Jouini, 2010. "Bootstrap methods for single structural change tests: power versus corrected size and empirical illustration," Statistical Papers, Springer, vol. 51(1), pages 85-109, January.
    11. Emmanuel Flachaire, 2005. "More Efficient Tests Robust to Heteroskedasticity of Unknown Form," Econometric Reviews, Taylor & Francis Journals, vol. 24(2), pages 219-241.
    12. Jamel Jouini, 2006. "Bootstrap Tests in Bivariate VAR Process with Single Structural Change : Power versus Corrected Size and Empirical Illustration," Working Papers halshs-00410759, HAL.
    13. 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.
    14. Matz Dahlberg & Eva Johansson, 2000. "An examination of the dynamic behaviour of local governments using GMM bootstrapping methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(4), pages 401-416.
    15. Kline, Patrick & Santos, Andres, 2012. "Higher order properties of the wild bootstrap under misspecification," Journal of Econometrics, Elsevier, vol. 171(1), pages 54-70.
    16. Joachim Inkmann, 2000. "Finite Sample Properties of One-Step, Two-Step and Bootstrap Empirical Likelihood Approaches to Efficient GMM Estimation," Econometric Society World Congress 2000 Contributed Papers 0332, Econometric Society.
    17. Dufour, Jean-Marie, 2006. "Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics," Journal of Econometrics, Elsevier, vol. 133(2), pages 443-477, August.
    18. repec:ebl:ecbull:v:30:y:2010:i:1:p:55-66 is not listed on IDEAS
    19. van den Berg, Gerard J. & van Vuuren, Aico, 2010. "The effect of search frictions on wages," Labour Economics, Elsevier, vol. 17(6), pages 875-885, December.
    20. Yanna Wu & Subhash C. Ray, 2005. "Technical Efficiency and Stock Market Reaction to Horizontal Mergers," Working papers 2005-05, University of Connecticut, Department of Economics.
    21. Emmanuel Flachaire, 2002. "Bootstrapping heteroskedasticity consistent covariance matrix estimator," Computational Statistics, Springer, vol. 17(4), pages 501-506, December.

    More about this item

    Keywords

    Wild bootstrap; heteroskedasticity consistent covariance matrix estimator; size distortion.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:6560. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.