This paper introduces a bivariate hysteretic integer-valued autoregressive(INAR)process driven by a bivariate Poisson innovation.It deals well with the buffered or hysteretic characteristics of the data.Model properti...This paper introduces a bivariate hysteretic integer-valued autoregressive(INAR)process driven by a bivariate Poisson innovation.It deals well with the buffered or hysteretic characteristics of the data.Model properties such as sationarity and ergodicity are studied in detail.Parameter estimation problem is also well address via methods of two-step conditional least squares(CLS)and conditional maximum likelihood(CML).The boundary parameters are estimated via triangular grid searching algorithm.The estimation effect is verified through simulations based on three scenarios.Finally,the new model is applied to the offence counts in New South Wales(NSW),Australia.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.12471249 and 12101417the Natural Science Foundation of Jilin Province under Grant Nos.YDZJ202301ZYTS393 and20220101038JC+1 种基金Postdoctoral Foundation of Jilin Province under Grant No.2023337Scientific Research Project of Jilin Provincial Department of Education under Grant No.JJKH20230665KJ。
文摘This paper introduces a bivariate hysteretic integer-valued autoregressive(INAR)process driven by a bivariate Poisson innovation.It deals well with the buffered or hysteretic characteristics of the data.Model properties such as sationarity and ergodicity are studied in detail.Parameter estimation problem is also well address via methods of two-step conditional least squares(CLS)and conditional maximum likelihood(CML).The boundary parameters are estimated via triangular grid searching algorithm.The estimation effect is verified through simulations based on three scenarios.Finally,the new model is applied to the offence counts in New South Wales(NSW),Australia.
