北京航空航天大学学报 ›› 2022, Vol. 48 ›› Issue (5): 756-761.doi: 10.13700/j.bh.1001-5965.2020.0657

• 论文 • 上一篇    下一篇

时变方差自回归模型的联合估计函数方法推断

韩玉, 田宝成, 王书鹏   

  1. 东北电力大学 理学院, 吉林 132012
  • 收稿日期:2020-11-25 发布日期:2022-05-30
  • 通讯作者: 韩玉 E-mail:hanyu@neepu.edu.cn
  • 基金资助:
    吉林省教育厅科研项目(JJKH20200102KJ)

A combined estimation functions method for autoregressive model with time-varying variance

HAN Yu, TIAN Baocheng, WANG Shupeng   

  1. School of Science, Northeast Electric Power University, Jilin 132012, China
  • Received:2020-11-25 Published:2022-05-30
  • Supported by:
    Scientific Research Program of the Education Department of Jilin Province,China (JJKH20200102KJ)

摘要: 针对参数估计问题,利用联合估计函数方法对带有时变方差的自回归模型参数进行统计研究。介绍了带有时变方差自回归模型和联合估计函数理论的研究现状,利用联合估计函数理论,给出带有时变方差自回归模型的参数估计量,证明该参数联合估计量渐近收敛于正态分布。对提出的参数统计量进行数值模拟对比分析,模拟结果表明,与伪极大似然估计量、最小二乘估计量进行对比,提出的参数联合估计量略优于伪极大似然估计量,同时该统计量受误差项分布函数影响较小。

关键词: 估计函数, 联合估计函数, 自回归模型, 时变方差, 伪极大似然估计

Abstract: With regard to the problem of parameter estimation, the combined estimation functions method is used to carry out statistical research on the parameter of autoregressive model with time-varying variance. The research status of the autoregressive model with time-varying variance and the combined estimation functions theory is reported. The combined estimation functions theory is used to obtain the parameter estimators of the autoregressive model with time-varying variance, and it is proved that the parameter estimators of the combined estimation functions method asymptotically converge to normal distribution. The numerical simulation is carried out for the comparative analysis of the proposed parameters. The simulation results show that, compared with the quasi maximum likelihood estimators and the least squares estimators, the proposed parameter estimators of combined estimation functions are slightly better than those of quasi maximum likelihood estimation, and the statistic is less affected by the distribution function of error terms.

Key words: estimation functions, combined estimation functions, autoregressive model, time-varying variance, quasi maximum likelihood estimation

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