OPTIMAL CHANGE-POINT ESTIMATION IN TIME SERIES

被引:1
|
作者
Chan, Ngai Hang [1 ]
Ng, Wai Leong [2 ]
Yau, Chun Yip [1 ]
Yu, Haihan [3 ]
机构
[1] Chinese Univ Hong Kong, Dept Stat, Hong Kong, Peoples R China
[2] Hang Seng Univ Hong Kong, Dept Math Stat & Insurance, Hong Kong, Peoples R China
[3] Iowa State Univ, Dept Stat, Ames, IA USA
来源
ANNALS OF STATISTICS | 2021年 / 49卷 / 04期
关键词
Bayes-type estimator; confidence interval; double-sided random process; piecewise stationary time series; structural break; BOOTSTRAP; GARCH; CALIBRATION;
D O I
10.1214/20-AOS2039
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 establishes asymptotic theory for optimal estimation of change points in general time series models under alpha-mixing conditions. We show that the Bayes-type estimator is asymptotically minimax for change-point estimation under squared error loss. Two bootstrap procedures are developed to construct confidence intervals for the change points. An approximate limiting distribution of the change-point estimator under small change is also derived. Simulations and real data applications are presented to investigate the finite sample performance of the Bayes-type estimator and the bootstrap procedures.
引用
收藏
页码:2336 / 2355
页数:20
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