Bias-Corrected Estimation for Spatial Autocorrelation

The biasedness issue arising from the maximum likelihood estimation of the spatial autoregressive model (SAR) is further investigated under a broader set-up than that in Bao and Ullah (2007a). A major difficulty in analytically evaluating the expectations of ratios of quadratic forms is overcome by...

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Main Author: YANG, Zhenlin
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Language:English
Published: Institutional Knowledge at Singapore Management University 2010
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Online Access:https://ink.library.smu.edu.sg/soe_research/1251
https://ink.library.smu.edu.sg/context/soe_research/article/2250/viewcontent/ZLYangOct10.pdf
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spelling sg-smu-ink.soe_research-22502019-04-20T16:38:52Z Bias-Corrected Estimation for Spatial Autocorrelation YANG, Zhenlin The biasedness issue arising from the maximum likelihood estimation of the spatial autoregressive model (SAR) is further investigated under a broader set-up than that in Bao and Ullah (2007a). A major difficulty in analytically evaluating the expectations of ratios of quadratic forms is overcome by a simple bootstrap procedure. With that, the corrections on bias and variance of the spatial estimator can easily be made up to third-order, and once this is done, the estimators of other model parameters become nearly unbiased. Compared with the analytical approach, the new approach is much simpler, and can easily be extended to other models of a similar structure. Extensive Monte Carlo results show that the new approach performs excellently in general. 2010-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1251 https://ink.library.smu.edu.sg/context/soe_research/article/2250/viewcontent/ZLYangOct10.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 Quasi-MLE Spatial layout. 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
Quasi-MLE
Spatial layout.
Econometrics
spellingShingle Third-order bias
Third-order variance
Bootstrap
Concentrated estimating equation
Monte Carlo
Quasi-MLE
Spatial layout.
Econometrics
YANG, Zhenlin
Bias-Corrected Estimation for Spatial Autocorrelation
description The biasedness issue arising from the maximum likelihood estimation of the spatial autoregressive model (SAR) is further investigated under a broader set-up than that in Bao and Ullah (2007a). A major difficulty in analytically evaluating the expectations of ratios of quadratic forms is overcome by a simple bootstrap procedure. With that, the corrections on bias and variance of the spatial estimator can easily be made up to third-order, and once this is done, the estimators of other model parameters become nearly unbiased. Compared with the analytical approach, the new approach is much simpler, and can easily be extended to other models of a similar structure. Extensive Monte Carlo results show that the new approach performs excellently in general.
format text
author YANG, Zhenlin
author_facet YANG, Zhenlin
author_sort YANG, Zhenlin
title Bias-Corrected Estimation for Spatial Autocorrelation
title_short Bias-Corrected Estimation for Spatial Autocorrelation
title_full Bias-Corrected Estimation for Spatial Autocorrelation
title_fullStr Bias-Corrected Estimation for Spatial Autocorrelation
title_full_unstemmed Bias-Corrected Estimation for Spatial Autocorrelation
title_sort bias-corrected estimation for spatial autocorrelation
publisher Institutional Knowledge at Singapore Management University
publishDate 2010
url https://ink.library.smu.edu.sg/soe_research/1251
https://ink.library.smu.edu.sg/context/soe_research/article/2250/viewcontent/ZLYangOct10.pdf
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