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Full-Text Articles in Social and Behavioral Sciences
The Block-Block Bootstrap: Improved Asymptotic Refinements, Donald W.K. Andrews
The Block-Block Bootstrap: Improved Asymptotic Refinements, Donald W.K. Andrews
Cowles Foundation Discussion Papers
The asymptotic refinements attributable to the block bootstrap for time series are not as large as those of the nonparametric iid bootstrap or the parametric bootstrap. One reason is that the independence between the blocks in the block bootstrap sample does not mimic the dependence structure of the original sample. This is the join-point problem. In this paper, we propose a method of solving this problem. The idea is not to alter the block bootstrap. Instead, we alter the original sample statistics to which the block bootstrap is applied. We introduce block statistics that possess join-point features that are similar …
Equivalence Of The Higher-Order Asymptotic Efficiency Of K-Step And Extremum Statistics, Donald W.K. Andrews
Equivalence Of The Higher-Order Asymptotic Efficiency Of K-Step And Extremum Statistics, Donald W.K. Andrews
Cowles Foundation Discussion Papers
It is well known that a one-step scoring estimator that starts from any N 1 /2 -consistent estimator has the same first-order asymptotic efficiency as the maximum likelihood estimator. This paper extends this result to k -step estimators and test statistics for k > 1, higher-order asymptotic efficiency, and general extremum estimators and test statistics. The paper shows that a k -step estimator has the same higher-order asymptotic efficiency, to any given order, as the extremum estimator towards which it is stepping, provided (i) k is sufficiently large, (ii) some smoothness and moment conditions hold, and (iii) a condition on the …
Testing When A Parameter Is On The Boundary Of The Maintained Hypothesis, Donald W.K. Andrews
Testing When A Parameter Is On The Boundary Of The Maintained Hypothesis, Donald W.K. Andrews
Cowles Foundation Discussion Papers
This paper considers testing problems where several of the standard regularity conditions fail to hold. We consider the case where (i) parameter vectors in the null hypothesis may lie on the boundary of the maintained hypothesis and (ii) there may be a nuisance parameter that appears under the alternative hypothesis, but not under the null. The paper establishes the asymptotic null and local alternative distributions of quasi-likelihood ratio, rescaled quasi-likelihood ratio, Wald, and score tests in this case. The results apply to tests based on a wide variety of extremum estimators and apply to a wide variety of models. Examples …
Higher-Order Improvements Of A Computationally Attractive K-Step Bootstrap For Extremum Estimators, Donald W.K. Andrews
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 …
Higher-Order Improvements Of A Computationally Attractive K-Step Bootstrap For Extremum Estimators, Donald W.K. Andrews
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 …