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Computationally Efficient Double Bootstrap Variance Estimation

Author

Listed:
  • Karlsson, Sune

    (Department of Economic Statistics)

  • Löthgren, Mickael

    (Department of Economic Statistics)

Abstract

The double bootstrap provides a useful tool for bootstrapping approximately pivotal quantities by using an "inner" bootstrap loop to estimate the variance. When the estimators are computationally intensive, the double bootstrap may become infeasible. We propose the use of a new variance estimator for the nonparametric bootstrap which effectively removes the requirement to perform the inner loop of the double bootstrap. Simulation results indicate that the proposed estimator produce bootstrap-t confidence intervals with coverage accuracy which replicates the coverage accuracy for the standard double bootstrap.

Suggested Citation

  • Karlsson, Sune & Löthgren, Mickael, 1997. "Computationally Efficient Double Bootstrap Variance Estimation," SSE/EFI Working Paper Series in Economics and Finance 151, Stockholm School of Economics.
    • Handle: RePEc:hhs:hastef:0151
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    References listed on IDEAS

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    1. Karlsson, Sune & Lothgren, Mickael, 2000. "Computationally efficient double bootstrap variance estimation," Computational Statistics & Data Analysis, Elsevier, vol. 33(3), pages 237-247, May.
    2. Vinod, H. D., 1995. "Double bootstrap for shrinkage estimators," Journal of Econometrics, Elsevier, vol. 68(2), pages 287-302, August.
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    Cited by:

    1. Karlsson, Sune & Lothgren, Mickael, 2000. "Computationally efficient double bootstrap variance estimation," Computational Statistics & Data Analysis, Elsevier, vol. 33(3), pages 237-247, May.

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    More about this item

    Keywords

    Bootstrap-t; confidence intervals; influence function; non-parametric bootstrap;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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