Probabilistic Properties of Parametric Dual and Inverse Time Series Models Generated by ARMA Models

被引：0

作者：

El Ghini, Ahmed ^{[1
]}

机构：

[1] Mohammed V Univ Rabat, FSJES Souissi, Rabat, Morocco

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2015年 / 44卷 / 21期

关键词：

All-pass time series models; Inverse and dual processes; Inverse and ordinary autocorrelations; Non linear time series; Time reversibility; Weak ARMA models; AUTOCORRELATIONS; VIDEO;

D O I：

10.1080/03610926.2014.887113

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

For the class of autoregressive-moving average (ARMA) processes, we examine the relationship between the dual and the inverse processes. It is demonstrated that the inverse process generated by a causal and invertible ARMA (p, q) process is a causal and invertible ARMA (q, p) model. Moreover, it is established that this representation is strong if and only if the generating process is Gaussian. More precisely, it is derived that the linear innovation process of the inverse process is an all-pass model. Some examples and applications to time reversibility are given to illustrate the obtained results.

引用

页码：4651 / 4661

页数：11

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