Group LASSO for structural break time series

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

72 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)590-599
Journal / PublicationJournal of the American Statistical Association
Volume109
Issue number506
Publication statusPublished - 2014
Externally publishedYes

Abstract

Consider a structural break autoregressive (SBAR) process (formula presented), where Yt-1 = (1, Yt-1, …, Yt-p)T, βj = (βj0, …, βjp)T ∈ ℝp+1,j = 1, …, m + 1, {t1, …, tm} are change-points, 1 = t0 < t1 <・・・ <tm+1 = n + 1, σ(・) is a measurable function on ℝq, and {εt} 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 {t1, …, tm} 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.

Research Area(s)

  • Change-points, Information criterion, Nonstationary autoregressive process

Bibliographic Note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to lbscholars@cityu.edu.hk.

Citation Format(s)

Group LASSO for structural break time series. / Chan, Ngai Hang; Yau, Chun Yip; Zhang, Rong-Mao.
In: Journal of the American Statistical Association, Vol. 109, No. 506, 2014, p. 590-599.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review