Asymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Dependence Model
In studying the asymptotic and finite-sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In...
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sg-smu-ink.soe_research-25982019-04-19T07:31:28Z Asymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Dependence Model LIU, Shew Fan YANG, Zhenlin In studying the asymptotic and finite-sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite-sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the application of this model. Contrary to the common perceptions, both the large and small sample behaviors of the QML estimators for the SED model can be different from those for the SLD model in terms of the rate of convergence and the magnitude of bias. Monte Carlo results show that the bias can be severe and the proposed bias correction procedure is very effective. 2014-09-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1599 https://ink.library.smu.edu.sg/context/soe_research/article/2598/viewcontent/15_2014.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Asymptotics Bias Correction Bootstrap Concentrated estimating equation Monte Carlo Spatial layout Stochastic expansion Econometrics |
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Asymptotics Bias Correction Bootstrap Concentrated estimating equation Monte Carlo Spatial layout Stochastic expansion Econometrics LIU, Shew Fan YANG, Zhenlin Asymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Dependence Model |
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In studying the asymptotic and finite-sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite-sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the application of this model. Contrary to the common perceptions, both the large and small sample behaviors of the QML estimators for the SED model can be different from those for the SLD model in terms of the rate of convergence and the magnitude of bias. Monte Carlo results show that the bias can be severe and the proposed bias correction procedure is very effective. |
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LIU, Shew Fan YANG, Zhenlin |
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LIU, Shew Fan YANG, Zhenlin |
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LIU, Shew Fan |
title |
Asymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Dependence Model |
title_short |
Asymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Dependence Model |
title_full |
Asymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Dependence Model |
title_fullStr |
Asymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Dependence Model |
title_full_unstemmed |
Asymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Dependence Model |
title_sort |
asymptotic distribution and finite-sample bias correction of qml estimators for spatial dependence model |
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Institutional Knowledge at Singapore Management University |
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2014 |
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https://ink.library.smu.edu.sg/soe_research/1599 https://ink.library.smu.edu.sg/context/soe_research/article/2598/viewcontent/15_2014.pdf |
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