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Applications Of The Fast Double Bootstrap

Author

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  • James G. MacKinnon

    (Queen's University)

Abstract

The fast double bootstrap, or FDB, is a procedure for calculating bootstrapP values that is much more computationally efficient than the double bootstrap itself. In many cases, it can provide more accurate results than ordinary bootstrap tests. For the fast double bootstrap to be valid, thetest statistic must be asymptotically independent of the random parts of thebootstrap data generating process. This paper presents simulation evidenceon the performance of FDB tests in three cases of interest toeconometricians. One of the cases involves both symmetric and equal-tailbootstrap tests, which, interestingly, can have quite different power properties. Another highlights the importance of imposing the nullhypothesis on the bootstrap DGP.

Suggested Citation

  • James G. MacKinnon, 2006. "Applications Of The Fast Double Bootstrap," Working Paper 1023, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:1023
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    File URL: https://www.econ.queensu.ca/sites/econ.queensu.ca/files/qed_wp_1023.pdf
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    References listed on IDEAS

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    Cited by:

    1. Davidson, Russell & MacKinnon, James G., 2007. "Improving the reliability of bootstrap tests with the fast double bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3259-3281, April.
    2. Richard, Patrick, 2009. "Modified fast double sieve bootstraps for ADF tests," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4490-4499, October.

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    More about this item

    Keywords

    double bootstrap; weak instruments; ARCH errors; serial correlation; bootstrap test;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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