Quasi-likelihood estimation of structure-changed threshold double autoregressive models

被引：0

作者：

Guo, Feifei ^{[1
]}

Ling, Shiqing ^{[1
]}

机构：

[1] Hong Kong Univ Sci & Technol, Hong Kong, Peoples R China

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2020年 / 205卷

基金：

澳大利亚研究理事会;

关键词：

TAR; Change-point; Compound Poisson process; QMLE; LEAST-SQUARES ESTIMATOR; CHANGE-POINT; INFERENCE; SEQUENCE;

D O I：

10.1016/j.jspi.2019.06.008

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：

This paper investigates the quasi-maximum likelihood estimator (QMLE) of the structure-changed and two-regime threshold double autoregressive model. It is shown that both the estimated threshold and change-point are n-consistent, and they converge weakly to the smallest minimizer of a compound Poisson process and the location of minima of a two-sided random walk, respectively. Other estimated parameters are root n-consistent and asymptotically normal. The performance of the QMLE is assessed via simulation studies and a real example is given to illustrate our procedure. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：138 / 155

页数：18

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