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Keywords = PQMLE

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27 pages, 1665 KiB  
Article
PQMLE and Generalized F-Test of Random Effects Semiparametric Model with Serially and Spatially Correlated Nonseparable Error
by Shuangshuang Li, Jianbao Chen and Danqing Chen
Fractal Fract. 2024, 8(7), 386; https://doi.org/10.3390/fractalfract8070386 - 28 Jun 2024
Cited by 1 | Viewed by 766
Abstract
Semiparametric panel data models are powerful tools for analyzing data with complex characteristics such as linearity and nonlinearity of covariates. This study aims to investigate the estimation and testing of a random effects semiparametric model (RESPM) with serially and spatially correlated nonseparable error, [...] Read more.
Semiparametric panel data models are powerful tools for analyzing data with complex characteristics such as linearity and nonlinearity of covariates. This study aims to investigate the estimation and testing of a random effects semiparametric model (RESPM) with serially and spatially correlated nonseparable error, utilizing a combination of profile quasi-maximum likelihood estimation and local linear approximation. Profile quasi-maximum likelihood estimators (PQMLEs) for unknowns and a generalized F-test statistic FNT are built to determine the beingness of nonlinear relationships. The asymptotic properties of PQMLEs and FNT are proven under regular assumptions. The Monte Carlo results imply that the PQMLEs and FNT performances are excellent on finite samples; however, missing the spatially and serially correlated error leads to estimator inefficiency and bias. Indonesian rice-farming data is used to illustrate the proposed approach, and indicates that landarea exhibits a significant nonlinear relationship with riceyield, in addition, high-yieldvarieties, mixed-yieldvarieties, and seedweight have significant positive impacts on rice yield. Full article
(This article belongs to the Special Issue Fractional Models and Statistical Applications)
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22 pages, 482 KiB  
Article
Estimation and Testing of Random Effects Semiparametric Regression Model with Separable Space-Time Filters
by Shuangshuang Li, Jianbao Chen and Bogui Li
Fractal Fract. 2022, 6(12), 735; https://doi.org/10.3390/fractalfract6120735 - 11 Dec 2022
Cited by 10 | Viewed by 1672
Abstract
This paper focuses on studying a random effects semiparametric regression model (RESPRM) with separable space-time filters. The model cannot only capture the linearity and nonlinearity existing in a space-time dataset, but also avoid the inefficient estimators caused by ignoring spatial correlation and serial [...] Read more.
This paper focuses on studying a random effects semiparametric regression model (RESPRM) with separable space-time filters. The model cannot only capture the linearity and nonlinearity existing in a space-time dataset, but also avoid the inefficient estimators caused by ignoring spatial correlation and serial correlation in the error term of a space-time data regression model. Its profile quasi-maximum likelihood estimators (PQMLE) for parameters and nonparametric functions, and a generalized F-test statistic for checking the existence of nonlinear relationships are constructed. The asymptotic properties of estimators and asymptotic distribution of test statistic are derived. Monte Carlo simulations imply that our estimators and test statistic have good finite sample performance. The Indonesian rice farming data are used to illustrate our methods. Full article
(This article belongs to the Special Issue New Trends in Fractional Stochastic Processes)
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26 pages, 740 KiB  
Article
PQMLE of a Partially Linear Varying Coefficient Spatial Autoregressive Panel Model with Random Effects
by Shuangshuang Li, Jianbao Chen and Danqing Chen
Symmetry 2021, 13(11), 2057; https://doi.org/10.3390/sym13112057 - 1 Nov 2021
Cited by 5 | Viewed by 1738
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Probability, Statistics and Applied Mathematics)
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