Innovations algorithm for periodically stationary time series

被引：18

作者：

Anderson, PL

Meerschaert, MM ^{[1
]}

Vecchia, AV

机构：

[1] Univ Nevada, Dept Math, Reno, NV 89557 USA

[2] US Geol Survey, Div Water Resources, Washington, DC USA

来源：

STOCHASTIC PROCESSES AND THEIR APPLICATIONS | 1999年 / 83卷 / 01期

关键词：

time series; periodically stationary; Yule-Walker estimates; innovations algorithm; heavy tails; regular variation;

D O I：

10.1016/S0304-4149(99)00027-7

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：

Periodic ARMA, or PARMA, time series are used to model periodically stationary time series. In this paper we develop the innovations algorithm for periodically stationary processes. We then show how the algorithm can be used to obtain parameter estimates for the PARMA model. These estimates are proven to be weakly consistent for PARMA processes whose underlying noise sequence has either finite or infinite fourth moment. Since many time series from the fields of economics and hydrology exhibit heavy tails, the results regarding the infinite fourth moment case are of particular interest. (C) 1999 Elsevier Science B.V. All rights reserved.

引用

页码：149 / 169

页数：21

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