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Higher-Order Improvements of a Computationally Attractive-Step Bootstrap for Extremum Estimators Author info | Abstract | Publisher info | Download info | Related research | Statistics Donald W.K. Andrews () (Cowles Foundation, Yale University )
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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. The optimal block length for the accuracy of tests and confidence intervals is shown to be proportional to N^{1/4} for both non-overlapping and overlapping block bootstraps in the context considered. In addition, the paper provides some results that establish the equivalence of the higher-order efficiency of non-bootstrap k-step statistics and extremum statistics. These results extend results of Pfanzagl (1974), Robinson (1988), and others.
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Paper provided by Cowles Foundation, Yale University in its series Cowles Foundation Discussion Papers with number
1230.
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Length: 74 pages
Date of creation: Jul 1999Date of revision:
Handle: RePEc:cwl:cwldpp:1230Contact details of provider: Postal: Yale University, Box 208281, New Haven, CT 06520-8281 USA Phone: (203) 432-3702 Fax: (203) 432-6167 Web page: http://cowles.econ.yale.edu/ More information through EDIRC
Order Information: Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA
For technical questions regarding this item, or to correct its listing, contact: (Glena Ames).
Keywords: Asymptotics ; block bootstrap ; Edgeworth expansion ; extremum estimator ; Gauss-Newton ; generalized method of moments estimator ; higher-order efficiency ; k-step bootstrap ; maximum likelihood estimator ; Newton-Raphson ; parametric bootstrap ; t statistic ; test of over-identifying restrictions ; Other versions of this item:
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