一阶时变随机系数整数值时间序列模型研究
Research on First-Order Time-Varying Random Coefficient Model for Integer-Valued Time Series via the Poisson Thinning Operator
喻开志 1陶铁来 1康健2
作者信息
- 1. 西南财经大学统计学院,成都 611130
- 2. 密歇根大学安娜堡分校公共卫生学院,Ann Arbor 48109-2029
- 折叠
摘要
整数值时间序列数据通常来源于由不同的稀疏算子及可变参数构成的数据生成过程.鉴于此,文章提出了一个由泊松稀疏算子和时变随机系数构成的一阶非负整数值自回归过程.文章推导了这个随机过程的矩条件,证明了这个过程的遍历性和平稳性.随后文章就这个过程提出了三种估计方法,并且应用蒙特卡罗模拟验证了这些估计方法的性能.最后,文章将所提出的模型和方法应用于上海证券交易所数据,塞浦路斯新冠疫情数据以及全球重大地震频次数据,取得了令人满意的效果.
Abstract
In many instances,integer-valued time series are derived from data gen-erating processes that involve varying parameters and different thinning operators.In this paper,we introduce a first-order non-negative integer-valued autoregressive process,combining the Poisson thinning operator with a time-varying random coef-ficient.We derive the moment condition,stationarity,and ergodicity of this process.Subsequently,we present three estimation methods.To evaluate these methods'per-formance,we employ Monte Carlo simulation results.Additionally,we demonstrate the proposed method's utility through an analysis of data from the Shanghai Stock Exchange,COVID-19 data from Cyprus,and significant annual earthquakes and sat-isfactory results are obtained.
关键词
稀疏算子/整数值自回归模型/随机系数/遍历性Key words
Thinning operator/INAR model/random coefficient/ergodicity引用本文复制引用
出版年
2024
NETL
NSTL
万方数据