CQR-based inference for the infinite-variance nearly nonstationary autoregressive models

被引：0

作者：

Fu, Ke-Ang ^{[1
]}

Ni, Jialin ^{[2
]}

Dong, Yajuan ^{[2
]}

机构：

[1] Zhejiang Univ City Coll, Dept Stat, Hangzhou 310015, Peoples R China

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

来源：

LITHUANIAN MATHEMATICAL JOURNAL | 2022年 / 62卷 / 01期

关键词：

asymptotic distribution; composite quantile regression; infinite variance; nearly nonstationary autoregressive model; COMPOSITE QUANTILE REGRESSION; ASYMPTOTIC THEORY; LIMIT-THEOREMS; TIME-SERIES; SELECTION;

D O I：

10.1007/s10986-021-09539-4

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

We consider the nearly nonstationary autoregressive model y(t) = q(n)y(t-1)+u(t), where q(n) = 1-c/n, c is a fixed constant, and {u(t), t >= 1} is a sequence of innovations belonging to the domain of attraction of a stable distribution with index 0 < alpha < 2. We construct a composite quantile regression estimator for the autoregressive coefficient and establish the asymptotic distribution of this estimator under some mild conditions.

引用

页码：1 / 9

页数：9

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