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The Economic Research Institute, Stockholm School of Economics SSE/EFI Working Paper Series in Economics and Finance

No 151:
Computationally Efficient Double Bootstrap Variance Estimation

Sune Karlsson () and Mickael Löthgren ()

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.

Keywords: Bootstrap-t; confidence intervals; influence function; non-parametric bootstrap; (follow links to similar papers)

JEL-Codes: C14; C15; (follow links to similar papers)

14 pages, January 1997

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This paper is published as:
Karlsson, Sune and Mickael Löthgren, (2000), 'Computationally Efficient Double Bootstrap Variance Estimation', Computational Statistics & Data Analysis, Vol. 33, pages 237-247



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