A General Method for Third-Order Bias and Variance Corrections on a Nonlinear Estimator

Motivated by a recent study of Bao and Ullah (2007a) on finite sample properties of MLE in the pure SAR (spatial autoregressive) model, a general method for third-order bias and variance corrections on a nonlinear estimator is proposed based on stochastic expansion and bootstrap. Working with concen...

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Main Author: YANG, Zhenlin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2015
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Online Access:https://ink.library.smu.edu.sg/soe_research/1586
https://ink.library.smu.edu.sg/context/soe_research/article/2585/viewcontent/Yang_JOE062014.pdf
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spelling sg-smu-ink.soe_research-25852017-08-04T02:52:46Z A General Method for Third-Order Bias and Variance Corrections on a Nonlinear Estimator YANG, Zhenlin Motivated by a recent study of Bao and Ullah (2007a) on finite sample properties of MLE in the pure SAR (spatial autoregressive) model, a general method for third-order bias and variance corrections on a nonlinear estimator is proposed based on stochastic expansion and bootstrap. Working with concentrated estimating equation simplifies greatly the high-order expansions for bias and variance; a simple bootstrap procedure overcomes a major difficulty in analytically evaluating expectations of various quantities in the expansions. The method is then studied in detail using a more general SAR model, with its effectiveness in correcting bias and improving inference fully demonstrated by extensive Monte Carlo experiments. Compared with the analytical approach, the proposed approach is much simpler and has a much wider applicability. The validity of the bootstrap procedure is formally established. The proposed method is then extended to the case of more than one nonlinear estimator. 2015-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1586 info:doi/10.1016/j.jeconom.2014.07.003 https://ink.library.smu.edu.sg/context/soe_research/article/2585/viewcontent/Yang_JOE062014.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Third-order bias Third-order variance Bootstrap Concentrated estimating equation Monte Carlo Spatial layout Stochastic expansion Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Third-order bias
Third-order variance
Bootstrap
Concentrated estimating equation
Monte Carlo
Spatial layout
Stochastic expansion
Econometrics
spellingShingle Third-order bias
Third-order variance
Bootstrap
Concentrated estimating equation
Monte Carlo
Spatial layout
Stochastic expansion
Econometrics
YANG, Zhenlin
A General Method for Third-Order Bias and Variance Corrections on a Nonlinear Estimator
description Motivated by a recent study of Bao and Ullah (2007a) on finite sample properties of MLE in the pure SAR (spatial autoregressive) model, a general method for third-order bias and variance corrections on a nonlinear estimator is proposed based on stochastic expansion and bootstrap. Working with concentrated estimating equation simplifies greatly the high-order expansions for bias and variance; a simple bootstrap procedure overcomes a major difficulty in analytically evaluating expectations of various quantities in the expansions. The method is then studied in detail using a more general SAR model, with its effectiveness in correcting bias and improving inference fully demonstrated by extensive Monte Carlo experiments. Compared with the analytical approach, the proposed approach is much simpler and has a much wider applicability. The validity of the bootstrap procedure is formally established. The proposed method is then extended to the case of more than one nonlinear estimator.
format text
author YANG, Zhenlin
author_facet YANG, Zhenlin
author_sort YANG, Zhenlin
title A General Method for Third-Order Bias and Variance Corrections on a Nonlinear Estimator
title_short A General Method for Third-Order Bias and Variance Corrections on a Nonlinear Estimator
title_full A General Method for Third-Order Bias and Variance Corrections on a Nonlinear Estimator
title_fullStr A General Method for Third-Order Bias and Variance Corrections on a Nonlinear Estimator
title_full_unstemmed A General Method for Third-Order Bias and Variance Corrections on a Nonlinear Estimator
title_sort general method for third-order bias and variance corrections on a nonlinear estimator
publisher Institutional Knowledge at Singapore Management University
publishDate 2015
url https://ink.library.smu.edu.sg/soe_research/1586
https://ink.library.smu.edu.sg/context/soe_research/article/2585/viewcontent/Yang_JOE062014.pdf
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