Bayesian local influence analysis of skew-normal spatial dynamic panel data models

Ju, Yuanyuan; Tang, Niansheng; Li, Xiaoxia

doi:10.1080/00949655.2018.1462813

Cited by 10 publications

(3 citation statements)

References 20 publications

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“…By using spline approximation, Xu and Zhang [ 16 ] introduced a Bayesian method for the partially linear model with heteroscedasticity based on the variance modelling technique. Based on the assumption that the response variables and random effects follow multivariate skew-normal distributions, a new spatial dynamic panel data model was proposed by Ju et al [ 17 ] and a Bayesian local influence analysis method was developed to simultaneously evaluate the impact of small perturbations on the data, priors, and sampling distributions. Pfarrhofer and Piribauer [ 18 ] studied Bayesian variable selection for high-dimensional spatial autoregressive models based on two shrinkage priors.…”

Section: Introductionmentioning

confidence: 99%

A Semiparametric Bayesian Approach to Heterogeneous Spatial Autoregressive Models

Liu,

Xu,

2024

Entropy

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Many semiparametric spatial autoregressive (SSAR) models have been used to analyze spatial data in a variety of applications; however, it is a common phenomenon that heteroscedasticity often occurs in spatial data analysis. Therefore, when considering SSAR models in this paper, it is allowed that the variance parameters of the models can depend on the explanatory variable, and these are called heterogeneous semiparametric spatial autoregressive models. In order to estimate the model parameters, a Bayesian estimation method is proposed for heterogeneous SSAR models based on B-spline approximations of the nonparametric function. Then, we develop an efficient Markov chain Monte Carlo sampling algorithm on the basis of the Gibbs sampler and Metropolis–Hastings algorithm that can be used to generate posterior samples from posterior distributions and perform posterior inference. Finally, some simulation studies and real data analysis of Boston housing data have demonstrated the excellent performance of the proposed Bayesian method.

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Section: Introductionmentioning

confidence: 99%

A Semiparametric Bayesian Approach to Heterogeneous Spatial Autoregressive Models

Liu,

Xu,

2024

Entropy

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“…Lee and Yu [16] proposed an orthonormal transformation for spatial autoregressive panel models with fxed efects and provided a method which allows the estimation of coefcients without estimating fxed efects. Ju et al [17] estimated parameters of spatial dynamic panel data models by the Bayesian method, and their method can adapt to a skew-normal distribution. Tese research studies investigated the estimators and corresponding large sample properties of spatial panel models; however, little work has been performed on the variable selection of models.…”

Section: Introductionmentioning

confidence: 99%

Variable Selection of High-Dimensional Spatial Autoregressive Panel Models with Fixed Effects

Xia

Zhang

Tian

2023

Journal of Mathematics

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This paper studies the variable selection of high-dimensional spatial autoregressive panel models with fixed effects in which a matrix transformation method is applied to eliminate the fixed effects. Then, a penalized quasi-maximum likelihood is developed for variable selection and parameter estimation in the transformed panel model. Under some regular conditions, the consistency and oracle properties of the proposed estimator are established. Some Monte-Carlo experiments and a real data analysis are conducted to examine the finite sample performance of the proposed variable selection procedure, showing that the proposed variable selection method works satisfactorily.

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“…In this paper, we use the posterior distribution of Ju et al [12] to estimate the parameters in Equation (1) by the Bayesian method. Secondly, the convergence of the Markov Chain is tested by using the EPSR value.…”

Section: Model Introductionmentioning

confidence: 99%

Empirical Analysis of Factors Affecting Air Quality in China-Based on the Skew-Normal Spatial Dynamic Panel Data Models

Yang

et al. 2022

J. Phys.: Conf. Ser.

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To explore the influence factors of air quality, the air quality data of 31 provincial capitals in China from 2014 to 2019 is studied by introducing skew-normal spatial dynamic panel data models in this paper. A Markov Chain Monte Carlo algorithm is developed to estimate the unknown parameters in the model. The main conclusions of the research are followed. (1) The variation of air quality in China could be well reflected by the skew-normal space dynamic panel data model. (2) PM10 had the most significant influence on air quality among the five air quality indexes, such as SO2, CO, NO2, PM10 and O3. (3) There are significant temporal and spatial correlations on air quality among provincial capitals. (4) In China, the air quality in southern and southwestern is excellent, while the air quality in northern is slightly polluted. These conclusions provide theoretical guidance for improving air quality in China and a scientific basis for making decisions of relevant departments.

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Bayesian local influence analysis of skew-normal spatial dynamic panel data models

Cited by 10 publications

References 20 publications

A Semiparametric Bayesian Approach to Heterogeneous Spatial Autoregressive Models

A Semiparametric Bayesian Approach to Heterogeneous Spatial Autoregressive Models

Variable Selection of High-Dimensional Spatial Autoregressive Panel Models with Fixed Effects

Empirical Analysis of Factors Affecting Air Quality in China-Based on the Skew-Normal Spatial Dynamic Panel Data Models

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