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Applications of the Fast Double Bootstrap

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  • MacKinnon, James

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.
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Suggested Citation

  • MacKinnon, James, 2006. "Applications of the Fast Double Bootstrap," Queen's Economics Department Working Papers 273459, Queen's University - Department of Economics.
    • Handle: RePEc:ags:quedwp:273459
    DOI: 10.22004/ag.econ.273459
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    1. Dufour, Jean-Marie & Khalaf, Lynda & Bernard, Jean-Thomas & Genest, Ian, 2004. "Simulation-based finite-sample tests for heteroskedasticity and ARCH effects," Journal of Econometrics, Elsevier, vol. 122(2), pages 317-347, October.
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    12. Donald W.K. Andrews & Marcelo J. Moreira & James H. Stock, 2004. "Optimal Invariant Similar Tests for Instrumental Variables Regression," Cowles Foundation Discussion Papers 1476, Cowles Foundation for Research in Economics, Yale University.
    13. Russell Davidson & James MacKinnon, 2002. "Fast Double Bootstrap Tests Of Nonnested Linear Regression Models," Econometric Reviews, Taylor & Francis Journals, vol. 21(4), pages 419-429.
    14. Durbin, J, 1970. "Testing for Serial Correlation in Least-Squares Regression When Some of the Regressors are Lagged Dependent Variables," Econometrica, Econometric Society, vol. 38(3), pages 410-421, May.
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    2. Richard, Patrick, 2009. "Modified fast double sieve bootstraps for ADF tests," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4490-4499, October.
    3. 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.

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    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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