Quasi-Maximum Likelihood Estimation for Spatial Panel Data Regressions
This article considers quasi-maximum likelihood estimations (QMLE) for two spatial panel data regression models: mixed effects model with spatial errors and transformed mixed effects model (where response and covariates are transformed) with spatial errors. One aim of transformation is to normalize...
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sg-smu-ink.soe_research-25742014-07-14T01:56:59Z Quasi-Maximum Likelihood Estimation for Spatial Panel Data Regressions YANG, Zhenlin This article considers quasi-maximum likelihood estimations (QMLE) for two spatial panel data regression models: mixed effects model with spatial errors and transformed mixed effects model (where response and covariates are transformed) with spatial errors. One aim of transformation is to normalize the data, thus the transformed models are more robust with respect to the normality assumption compared with the standard ones. QMLE method provides additional protection against violation of normality assumption. Asymptotic properties of the QMLEs are investigated. Numerical illustrations are provided. 2013-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1575 https://ink.library.smu.edu.sg/context/soe_research/article/2574/viewcontent/504635201f04d7950f.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Asymptotics Flexible functional form Fixed effects Quasi-maximum likelihood Random Effects Spatial error correlation Demand equation Econometrics |
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Asymptotics Flexible functional form Fixed effects Quasi-maximum likelihood Random Effects Spatial error correlation Demand equation Econometrics YANG, Zhenlin Quasi-Maximum Likelihood Estimation for Spatial Panel Data Regressions |
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This article considers quasi-maximum likelihood estimations (QMLE) for two spatial panel data regression models: mixed effects model with spatial errors and transformed mixed effects model (where response and covariates are transformed) with spatial errors. One aim of transformation is to normalize the data, thus the transformed models are more robust with respect to the normality assumption compared with the standard ones. QMLE method provides additional protection against violation of normality assumption. Asymptotic properties of the QMLEs are investigated. Numerical illustrations are provided. |
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text |
author |
YANG, Zhenlin |
author_facet |
YANG, Zhenlin |
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YANG, Zhenlin |
title |
Quasi-Maximum Likelihood Estimation for Spatial Panel Data Regressions |
title_short |
Quasi-Maximum Likelihood Estimation for Spatial Panel Data Regressions |
title_full |
Quasi-Maximum Likelihood Estimation for Spatial Panel Data Regressions |
title_fullStr |
Quasi-Maximum Likelihood Estimation for Spatial Panel Data Regressions |
title_full_unstemmed |
Quasi-Maximum Likelihood Estimation for Spatial Panel Data Regressions |
title_sort |
quasi-maximum likelihood estimation for spatial panel data regressions |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2013 |
url |
https://ink.library.smu.edu.sg/soe_research/1575 https://ink.library.smu.edu.sg/context/soe_research/article/2574/viewcontent/504635201f04d7950f.pdf |
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