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Higher-Order Improvements of a Computationally Attractive-Step Bootstrap for Extremum Estimators

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Abstract

This paper establishes the higher-order equivalence of the k-step bootstrap, introduced recently by Davidson and MacKinnon (1999a), and the standard bootstrap. The k-step bootstrap is a very attractive alternative computationally to the standard bootstrap for statistics based on nonlinear extremum estimators, such as generalized method of moment and maximum likelihood estimators. The paper also extends results of Hall and Horowitz (1996) to provide new results regarding the higher-order improvements of the standard bootstrap and the k-step bootstrap for extremum estimators (compared to procedures based on first-order asymptotics). The results of the paper apply to Newton-Raphson (NR), default NR, line-search NR, and Gauss-Newton k-step bootstrap procedures. The results apply to the nonparametric iid bootstrap, non-overlapping and overlapping block bootstraps, and restricted and unrestricted parametric bootstraps. The results cover symmetric and equal-tailed two-sided t tests and confidence intervals, one-sided t tests and confidence intervals, Wald tests and confidence regions, and J tests of over-identifying restrictions.

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File URL: http://cowles.econ.yale.edu/P/cd/d12a/d1230-r.pdf
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Bibliographic Info

Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1230R.

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Length: 66 pages
Date of creation: Jul 1999
Date of revision: Jan 2001
Publication status: Published in Econometrica (January 2002), 70(1): 70(1): 119-162
Handle: RePEc:cwl:cwldpp:1230r

Note: CFP 1031.
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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

Related research

Keywords: Asymptotics; block bootstrap; Edgeworth expansion; extremum estimator; Gauss-Newton; generalized method of moments estimator; k-step bootstrap; maximum likelihood estimator; Newton-Raphson; parametric bootstrap; t statistic; test of over-identifying;

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