The tail of the stationary distribution of a random coefficient Ar(q) model

被引：40

作者：

Klüppelberg, C

Pergamenchtchikov, S

机构：

[1] Tech Univ Munich, Ctr Math Sci, D-85747 Garching, Germany

[2] Univ Rouen, CNRS, UMR 6085, Lab Math Raphael Salem, F-76821 Mont St Aignan, France

来源：

ANNALS OF APPLIED PROBABILITY | 2004年 / 14卷 / 02期

关键词：

ARCH model; autoregressive model; geometric ergodicity; heteroscedastic model; random coefficient autoregressive process; random recurrence equation; regular variation; renewal theorem for Markov chains; strong mixing;

D O I：

10.1214/105051604000000189

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：

We investigate a stationary random coefficient autoregressive process. Using renewal type arguments tailor-made for such processes, we show that the stationary distribution has a power-law tail. When the model is normal, we show that the model is in distribution equivalent to an autoregressive process with ARCH errors. Hence, we obtain the tail behavior of any such model of arbitrary order.

引用

页码：971 / 1005

页数：35

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