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

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  • Donald W. K. Andrews

    (John F. Kennedy School of Government, Harvard University, U.S.A. and NBER)

Abstract

This paper establishes the higher-order equivalence of the "k"-step bootstrap, introduced recently by Davidson and MacKinnon (1999), 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 and nonoverlapping and overlapping block 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. Copyright The Econometric Society 2002.

Suggested Citation

  • Donald W. K. Andrews, 2002. "Higher-Order Improvements of a Computationally Attractive "k"-Step Bootstrap for Extremum Estimators," Econometrica, Econometric Society, vol. 70(1), pages 119-162, January.
    • Handle: RePEc:ecm:emetrp:v:70:y:2002:i:1:p:119-162
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    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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