IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-10-00284.html
   My bibliography  Save this article

Computationally efficient approximation for the double bootstrap mean bias correction

Author

Listed:
  • Rachida Ouysse

    () (The University of New South Wales)

Abstract

We propose a computationally efficient approximation for the double bootstrap bias adjustment factor without using the inner bootstrap loop. The approximation converges in probability to the population bias correction factor. We study the finite sample properties of the approximation in the context of a linear instrumental variable model. In identified versions of the model considered in our Monte Carlo experiments, the proposed approximation leads to estimators with lower variance than those based on the double bootstrap and, lower adjusted mean-squared error than estimators based on the single bootstrap. Evidence from the experiments we consider suggests that the bootstrap is less effective in reducing the bias when the instrumental variable is weak and endogeneity is strong.

Suggested Citation

  • Rachida Ouysse, 2011. "Computationally efficient approximation for the double bootstrap mean bias correction," Economics Bulletin, AccessEcon, vol. 31(3), pages 2388-2403.
    • Handle: RePEc:ebl:ecbull:eb-10-00284
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2011/Volume31/EB-11-V31-I3-P214.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    1. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, vol. 64(4), pages 891-916, July.
    2. Davidson, Russell & MacKinnon, James G., 2002. "Bootstrap J tests of nonnested linear regression models," Journal of Econometrics, Elsevier, vol. 109(1), pages 167-193, July.
    3. MacKinnon, James G. & Smith Jr., Anthony A., 1998. "Approximate bias correction in econometrics," Journal of Econometrics, Elsevier, vol. 85(2), pages 205-230, August.
    4. Alfonso Flores-Lagunes, 2007. "Finite sample evidence of IV estimators under weak instruments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(3), pages 677-694.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Bias correction; bootstrap; double bootstrap; instrumental variable estimation; Monte Carlo simulation.;

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C0 - Mathematical and Quantitative Methods - - 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:ebl:ecbull:eb-10-00284. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (John P. Conley). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.