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.
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)。