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Cowles Foundation Discussion Papers

1999

Newton-Raphson

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Higher-Order Improvements Of A Computationally Attractive K-Step Bootstrap For Extremum Estimators, Donald W.K. Andrews Jul 1999

Higher-Order Improvements Of A Computationally Attractive K-Step Bootstrap For Extremum Estimators, Donald W.K. Andrews

Cowles Foundation Discussion Papers

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 …

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Higher-Order Improvements Of A Computationally Attractive K-Step Bootstrap For Extremum Estimators, Donald W.K. Andrews Jul 1999

Higher-Order Improvements Of A Computationally Attractive K-Step Bootstrap For Extremum Estimators, Donald W.K. Andrews

Cowles Foundation Discussion Papers

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 …

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