Group LASSO for Structural Break Time Series

• 成果类型: Article
• 署名作者: Chan, Ngai Hang; Yau, Chun Yip; Zhang, Rong-Mao
• 署名单位: Chinese University of Hong Kong; Renmin University of China; Zhejiang University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.866566
• 发表日期: 2014
• 页码: 590-599
• 关键词: Change-point; segmentation

摘要

Consider a structural break autoregressive (SBAR) process Y-1 = Sigma(m+1)(j=1) (sic)beta jT Yt-1 + sigma(Yt-1, ...,Yt-q)epsilon 1(sic) I(t(j-1) <= t < t(1) < ... < t(m) vertical bar 1 = n + 1, sigma (.) is a measurable function on R-q, and {epsilon(t)} are white noise with unit variance. In practice, the number of change-points m is usually assumed to be known and small, because a large m would involve a huge amount of computational burden for parameters estimation. By reformulating the problem in a variable selection context, the group least absolute shrinkage and selection operator (LASSO) is proposed to estimate an SBAR model when m is unknown. It is shown that both m and the locations of the change-points {t(1), ..., t(m)} can be consistently estimated from the data, and the computation can be efficiently performed. An improved practical version that incorporates group LASSO and the stepwise regression variable selection technique are discussed. Simulation studies are conducted to assess the finite sample performance. Supplementary materials for this article are available online.