PQMLE of a Partially Linear Varying Coefficient Spatial Autoregressive Panel Model with Random Effects
This article deals with asymmetrical spatial data which can be modeled by a partially linear varying coefficient spatial autoregressive panel model (PLVCSARPM) with random effects. We constructed its profile quasi-maximum likelihood estimators (PQMLE). The consistency and asymptotic normality of the...
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2021
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oai:doaj.org-article:24e810e88aba4ba89bc00cb5b7e26ea72021-11-25T19:06:23ZPQMLE of a Partially Linear Varying Coefficient Spatial Autoregressive Panel Model with Random Effects10.3390/sym131120572073-8994https://doaj.org/article/24e810e88aba4ba89bc00cb5b7e26ea72021-11-01T00:00:00Zhttps://www.mdpi.com/2073-8994/13/11/2057https://doaj.org/toc/2073-8994This article deals with asymmetrical spatial data which can be modeled by a partially linear varying coefficient spatial autoregressive panel model (PLVCSARPM) with random effects. We constructed its profile quasi-maximum likelihood estimators (PQMLE). The consistency and asymptotic normality of the estimators were proved under some regular conditions. Monte Carlo simulations implied our estimators have good finite sample performance. Finally, a set of asymmetric real data applications was analyzed for illustrating the performance of the provided method.Shuangshuang LiJianbao ChenDanqing ChenMDPI AGarticlePLVCSARPMPQMLEconsistency asymptotic normalityMonte Carlo simulationasymmetric dataMathematicsQA1-939ENSymmetry, Vol 13, Iss 2057, p 2057 (2021) |
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topic |
PLVCSARPM PQMLE consistency asymptotic normality Monte Carlo simulation asymmetric data Mathematics QA1-939 |
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PLVCSARPM PQMLE consistency asymptotic normality Monte Carlo simulation asymmetric data Mathematics QA1-939 Shuangshuang Li Jianbao Chen Danqing Chen PQMLE of a Partially Linear Varying Coefficient Spatial Autoregressive Panel Model with Random Effects |
description |
This article deals with asymmetrical spatial data which can be modeled by a partially linear varying coefficient spatial autoregressive panel model (PLVCSARPM) with random effects. We constructed its profile quasi-maximum likelihood estimators (PQMLE). The consistency and asymptotic normality of the estimators were proved under some regular conditions. Monte Carlo simulations implied our estimators have good finite sample performance. Finally, a set of asymmetric real data applications was analyzed for illustrating the performance of the provided method. |
format |
article |
author |
Shuangshuang Li Jianbao Chen Danqing Chen |
author_facet |
Shuangshuang Li Jianbao Chen Danqing Chen |
author_sort |
Shuangshuang Li |
title |
PQMLE of a Partially Linear Varying Coefficient Spatial Autoregressive Panel Model with Random Effects |
title_short |
PQMLE of a Partially Linear Varying Coefficient Spatial Autoregressive Panel Model with Random Effects |
title_full |
PQMLE of a Partially Linear Varying Coefficient Spatial Autoregressive Panel Model with Random Effects |
title_fullStr |
PQMLE of a Partially Linear Varying Coefficient Spatial Autoregressive Panel Model with Random Effects |
title_full_unstemmed |
PQMLE of a Partially Linear Varying Coefficient Spatial Autoregressive Panel Model with Random Effects |
title_sort |
pqmle of a partially linear varying coefficient spatial autoregressive panel model with random effects |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doaj.org/article/24e810e88aba4ba89bc00cb5b7e26ea7 |
work_keys_str_mv |
AT shuangshuangli pqmleofapartiallylinearvaryingcoefficientspatialautoregressivepanelmodelwithrandomeffects AT jianbaochen pqmleofapartiallylinearvaryingcoefficientspatialautoregressivepanelmodelwithrandomeffects AT danqingchen pqmleofapartiallylinearvaryingcoefficientspatialautoregressivepanelmodelwithrandomeffects |
_version_ |
1718410266384793600 |