Structural break estimation for nonstationary time series models

被引:268
作者
Davis, RA [1 ]
Lee, TCM [1 ]
Rodriguez-Yam, GA [1 ]
机构
[1] Colorado State Univ, Dept Stat, Ft Collins, CO 80523 USA
基金
美国国家科学基金会;
关键词
changepoint; genetic algorithm; minimum description length principle; nonstationarity;
D O I
10.1198/016214505000000745
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 article considers the problem of modeling a class of nonstationary time series using piecewise autoregressive (AR) processes. The number and locations of the piecewise AR segments, as well as the orders of the respective AR processes. are assumed unknown. The minimum description length principle is applied to compare various segmented AR fits to the data. The goal is to find the "best" combination of the number of segments, the lengths of the segments, and the orders of the piecewise AR processes. Such a "best" combination is implicitly defined as the optimizer of an objective function, and a genetic algorithm is implemented to solve this difficult optimization problem. Numerical results from simulation experiments and real data analyses show that the procedure has excellent empirical properties. The segmentation of multivariate time series is also considered. Assuming that the true underlying model is a segmented autoregression, this procedure is shown to be consistent for estimating the location of the breaks.
引用
收藏
页码:223 / 239
页数:17
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