QML estimation of dynamic panel data models with spatial errors
We propose quasi maximum likelihood (QML) estimation of dynamic panel models with spatial errors when the cross-sectional dimension n is large and the time dimension T is fixed. We consider both the random effects and fixed effects models, and prove consistency and derive the limiting distributions...
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sg-smu-ink.soe_research-24842020-04-01T08:30:01Z QML estimation of dynamic panel data models with spatial errors SU, Liangjun YANG, Zhenlin We propose quasi maximum likelihood (QML) estimation of dynamic panel models with spatial errors when the cross-sectional dimension n is large and the time dimension T is fixed. We consider both the random effects and fixed effects models, and prove consistency and derive the limiting distributions of the QML estimators under different assumptions on the initial observations. We propose a residual-based bootstrap method for estimating the standard errors of the QML estimators. Monte Carlo simulation shows that both the QML estimators and the bootstrap standard errors perform well in finite samples under a correct assumption on initial observations, but may perform poorly when this assumption is not met. 2015-03-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1485 info:doi/10.1016/j.jeconom.2014.11.002 https://ink.library.smu.edu.sg/context/soe_research/article/2484/viewcontent/SuYang_JOE10112014.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Bootstrap Standard Errors Dynamic Panel Fixed Effects Random Effects Spatial Error Dependence Quasi Maximum Likelihood Initial Observations. Econometrics |
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Bootstrap Standard Errors Dynamic Panel Fixed Effects Random Effects Spatial Error Dependence Quasi Maximum Likelihood Initial Observations. Econometrics SU, Liangjun YANG, Zhenlin QML estimation of dynamic panel data models with spatial errors |
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We propose quasi maximum likelihood (QML) estimation of dynamic panel models with spatial errors when the cross-sectional dimension n is large and the time dimension T is fixed. We consider both the random effects and fixed effects models, and prove consistency and derive the limiting distributions of the QML estimators under different assumptions on the initial observations. We propose a residual-based bootstrap method for estimating the standard errors of the QML estimators. Monte Carlo simulation shows that both the QML estimators and the bootstrap standard errors perform well in finite samples under a correct assumption on initial observations, but may perform poorly when this assumption is not met. |
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SU, Liangjun YANG, Zhenlin |
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SU, Liangjun YANG, Zhenlin |
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SU, Liangjun |
title |
QML estimation of dynamic panel data models with spatial errors |
title_short |
QML estimation of dynamic panel data models with spatial errors |
title_full |
QML estimation of dynamic panel data models with spatial errors |
title_fullStr |
QML estimation of dynamic panel data models with spatial errors |
title_full_unstemmed |
QML estimation of dynamic panel data models with spatial errors |
title_sort |
qml estimation of dynamic panel data models with spatial errors |
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Institutional Knowledge at Singapore Management University |
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2015 |
url |
https://ink.library.smu.edu.sg/soe_research/1485 https://ink.library.smu.edu.sg/context/soe_research/article/2484/viewcontent/SuYang_JOE10112014.pdf |
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