On Bivariate Self-Exciting Hysteretic Integer-Valued Autoregressive Processes

被引：0
|
作者：
Yang, Kai [2 ]
Chen, Xiaoman [2 ]
Li, Han [1 ]
Xia, Chao [2 ]
Wang, Xinyang [3 ]
机构：
[1] Changchun Univ, Sch Math & Stat, Changchun 130012, Peoples R China
[2] Changchun Univ Technol, Sch Math & Stat, Changchun 130012, Peoples R China
[3] Liaoning Univ, Sch Math & Stat, Shenyang, Peoples R China
来源：
JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY | 2024年
基金：
中国国家自然科学基金;
关键词：
Bivariate integer-valued time series; buffered autoregressive process; count data; hysteretic autoregressive process; TGSM algorithm; TIME-SERIES;
D O I：
10.1007/s11424-024-4027-x
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
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.
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页数：22
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