Asymptotics for the random coefficient first-order autoregressive model with possibly heavy-tailed innovations

被引：4

作者：

Fu, Ke-Ang ^{[1
]}

Fu, Xiaoyong ^{[1
]}

机构：

[1] Zhejiang Gongshang Univ, Sch Stat & Math, Hangzhou 310018, Zhejiang, Peoples R China

来源：

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2015年 / 285卷

基金：

中国国家自然科学基金;

关键词：

Asymptotic normality; Conditional least squares estimator; Domain of attraction of the normal law; Random coefficient AR(1); Self-normalization; LIMIT THEORY; UNIT-ROOT; PARAMETER; INFERENCE;

D O I：

10.1016/j.cam.2015.02.020

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Consider a random coefficient AR(1) model, X-t = (rho(n) + phi(n))Xt-i + u(t), where {rho(n,) n >= 1} is a sequence of real numbers, {phi(n), n >= 1} is a sequence of random variables, and the innovations of the model form a sequence of i.i.d.random variables belonging to the domain of attraction of the normal law. By imposing some weaker conditions, the conditional least squares estimator of the autoregressive coefficient rho(n) is achieved, and shown to be asymptotically normal by allowing the second moment of the innovation to be possibly (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：116 / 124

页数：9

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