吉林大学学报(理学版) ›› 2021, Vol. 59 ›› Issue (6): 1395-1399.

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一阶混合整数值二项自回归模型

刘子健, 桂尚珂, 陈硕, 杨凯, 金虹桥   

  1. 长春工业大学 数学与统计学院, 长春 130012
  • 收稿日期:2021-02-21 出版日期:2021-11-26 发布日期:2021-11-26
  • 通讯作者: 杨凯 E-mail:yangkai@ccut.edu.cn

First-Order Mixed Integer-Valued Binomial Autoregressive Model

LIU Zijian, GUI Shangke, CHEN Shuo, YANG Kai, JIN Hongqiao   

  1. School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
  • Received:2021-02-21 Online:2021-11-26 Published:2021-11-26

摘要: 首先, 针对复杂整数值时间序列数据的建模问题, 提出一类一阶混合整数值二项自回归模型; 其次, 证明该模型的严平稳遍历性, 给出模型的转移概率、 期望、 方差等概率统计性质, 并用最大似然估计方法估计模型参数; 最后, 将模型应用于消费者价格协调指数(HICP)数据的拟合中.  实例分析结果表明, 该模型比现有模型的拟合效果更好.

关键词: 整数值时间序列, 一阶混合二项自回归模型, 平稳性, 极大似然估计

Abstract: Firstly, aiming at the modelling problem of complex integer-valued time series data, we proposed a first-order mixed integer-valued binomial autoregressive model. Secondly, we proved the strict stationary ergodicity of the model, gave the probabilistic statistics properties of the model, such as transition probability, expectation and variance, and estimated the model parameters by maximum likelihood method. Finally, the proposed model was applied to the fitting of harmonised index of consumer price (HICP)  data. The example analysis results show that the fitting effect of the proposed model is better than that of the existing model.

Key words: integer-valued time series, first-order mixed , binomial autoregressive model, stationarity, maximum likelihood estimation

中图分类号: 

  • O212.8