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Group orthogonal greedy algorithm for change-point estimation of multivariate time series

  • Yuanbo Li
  • , Ngai Hang Chan*
  • , Chun Yip Yau
  • , Rongmao Zhang
    • *Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

8   Link opens in a new tab Citations (Scopus)

Abstract

This paper proposes a three-step method for detecting multiple structural breaks for piecewise stationary vector autoregressive processes. The number of structural breaks can be large and unknown with the locations of the breaks being different among different components. The proposed method is established via a link between a structural break problem and a high-dimensional regression problem. By means of this connection, a group orthogonal greedy algorithm, originated from the high-dimensional variable selection context, is developed for efficiently screening out potential break-points in the first step. A high-dimensional information criterion is proposed for consistent structural breaks estimation in the second step. In the third step, the information criterion further determines the specific components in which structural breaks occur. Monte Carlo experiments are conducted to demonstrate the finite sample performance, and applications to stock data are provided to illustrate the proposed method.
Original languageEnglish
Pages (from-to)14-33
JournalJournal of Statistical Planning and Inference
Volume212
Online published24 Oct 2020
DOIs
Publication statusPublished - May 2021
Externally publishedYes

Research Keywords

  • High-dimensional regression
  • Information criterion
  • Vector autoregressive models

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