Bayesian Estimation and Model Selection for the Spatiotemporal Autoregressive Model with Autoregressive Conditional Heteroscedasticity Errors

作     者：Bing SU Fu-kang ZHU Ju HUANG Bing SU;Fu-kang ZHU;Ju HUANG

作者机构：Department of Statistics&Actuarial ScienceUniversity of Hong KongHong Kong 999077China School of MathematicsJilin UniversityChangchun 130012China National Energy Administration Information CenterBeijing 100045China 

出 版 物：《Acta Mathematicae Applicatae Sinica》 (应用数学学报（英文版）)

年 卷 期：2023年第39卷第4期

页      面：972-989页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：supported by National Natural Science Foundation of China (No.12271206) Natural Science Foundation of Jilin Province (No.20210101143JC) Science and Technology Research Planning Project of Jilin Provincial Department of Education (No.JJKH20231122KJ) 

主　　题：autoregressive conditional heteroscedasticity model Bayesian estimation model selection spatial ARCH model spatial panel model spatiotemporal model 

摘      要：The spatial and spatiotemporal autoregressive conditional heteroscedasticity(STARCH) models receive increasing attention. In this paper, we introduce a spatiotemporal autoregressive(STAR) model with STARCH errors, which can capture the spatiotemporal dependence in mean and variance simultaneously. The Bayesian estimation and model selection are considered for our model. By Monte Carlo simulations, it is shown that the Bayesian estimator performs better than the corresponding maximum-likelihood estimator, and the Bayesian model selection can select out the true model in most times. Finally, two empirical examples are given to illustrate the superiority of our models in fitting those data.