COINTEGRATION RANK ESTIMATION FOR HIGH-DIMENSIONAL TIME SERIES WITH BREAKS
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 1193-1217 |
| Number of pages | 25 |
| Journal / Publication | Statistica Sinica |
| Volume | 33 |
| Issue number | Online Special Issue |
| Publication status | Published - May 2023 |
Link(s)
| DOI | DOI |
|---|---|
| Attachment(s) | Documents
Publisher's Copyright Statement
|
| Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85161647613&origin=recordpage |
| Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(3ef376e2-a25d-4ea9-9069-c554d7a8a905).html |
Abstract
We propose an intuitive and simple-to-use procedure for estimating the cointegration rank of a high-dimensional time series system with possible breaks. Based on a similar idea to a principal component analysis, the cointegration rank can be estimated by the number of eigenvalues of a certain nonnegative definite matrix. There are several advantages to the new method: (a) the dimension of the cointegrated time series is allowed to vary with the sample size; (b) it is model free; and (c) it is simple to use and robust against possible breaks in trend. The cointegration rank can be estimated without the need for a priori testing and estimating of the break points. The asymptotic properties of the proposed methods are investigated when the dimension of the time series increases with the sample size, which offers a new alternative to deal with high-dimensional time series. Finally, the proposed precedure is demonstrated by means of simulations. © 2023 Institute of Statistical Science. All rights reserved.
Research Area(s)
- Cointegration, eigenanalysis, high-dimensional time series, nonstationary processes, structural break
Citation Format(s)
COINTEGRATION RANK ESTIMATION FOR HIGH-DIMENSIONAL TIME SERIES WITH BREAKS. / Chan, Ngai Hang; Zhang, Rongmao.
In: Statistica Sinica, Vol. 33, No. Online Special Issue, 05.2023, p. 1193-1217.
In: Statistica Sinica, Vol. 33, No. Online Special Issue, 05.2023, p. 1193-1217.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Download Statistics
No data available
