有界ℤ值时间序列的三叉差分自回归过程

IF 1.2 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Huaping Chen, Zifei Han, Fukang Zhu
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引用次数: 0

摘要

本文通过提出一种新颖的三叉差分自回归过程,解决了有界值时间序列的建模难题。该过程不仅保持了经典二项 GARCH 模型中的自相关结构,而且有助于分析具有负相关或正相关的有界值时间序列。我们验证了耦合过程(包括观测过程及其条件均值过程)的静态性和遍历性,同时还提出了一些随机特性。我们进一步讨论了条件极大似然估计,并确定了其渐近特性。我们通过模拟研究评估了这些估计方法的有效性,随后将提出的模型应用于两个真实数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A trinomial difference autoregressive process for the bounded ℤ -valued time series

This article tackles the modeling challenge of bounded -valued time series by proposing a novel trinomial difference autoregressive process. This process not only maintains the autocorrelation structure presenting in the classical binomial GARCH model, but also facilitates the analysis of bounded -valued time series with negative or positive correlation. We verify the stationarity and ergodicity of the couple process (comprising both the observed process and its conditional mean process) while also presenting several stochastic properties. We further discuss the conditional maximum likelihood estimation and establish their asymptotic properties. The effectiveness of these estimators is assessed through simulation studies, followed by the application of the proposed models to two real datasets.

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来源期刊
Journal of Time Series Analysis
Journal of Time Series Analysis 数学-数学跨学科应用
CiteScore
2.00
自引率
0.00%
发文量
39
审稿时长
6-12 weeks
期刊介绍: During the last 30 years Time Series Analysis has become one of the most important and widely used branches of Mathematical Statistics. Its fields of application range from neurophysiology to astrophysics and it covers such well-known areas as economic forecasting, study of biological data, control systems, signal processing and communications and vibrations engineering. The Journal of Time Series Analysis started in 1980, has since become the leading journal in its field, publishing papers on both fundamental theory and applications, as well as review papers dealing with recent advances in major areas of the subject and short communications on theoretical developments. The editorial board consists of many of the world''s leading experts in Time Series Analysis.
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