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COINTEGRATION RANK ESTIMATION FOR HIGH-DIMENSIONAL TIME SERIES WITH BREAKS

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

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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.
Original languageEnglish
Pages (from-to)1193-1217
Number of pages25
JournalStatistica Sinica
Volume33
Issue numberOnline Special Issue
DOIs
Publication statusPublished - May 2023

Funding

This research was supported in part by grants from HKSAR-RGC-GRF No. 14325216, 14307921, and the Theme-based Research Scheme of HKSAR-RGC-TBS T32- 101/15-R (Chan), and NSFC (Nos. 12171427, 11771390, U21A20426), Zhejiang provincial natural science foundation (No. LZ21A010002) and the Fundamental Research Funds for the Central Universities (Zhang).

Research Keywords

  • Cointegration
  • eigenanalysis
  • high-dimensional time series
  • nonstationary processes
  • structural break

Publisher's Copyright Statement

  • COPYRIGHT TERMS OF DEPOSITED FINAL PUBLISHED VERSION FILE: Statistica Sinica © 2023 Institute of Statistical Science, Academia Sinica. Use of this article is permitted solely for educational and research purposes. Chan, N. H., & Zhang, R. (2023). COINTEGRATION RANK ESTIMATION FOR HIGH-DIMENSIONAL TIME SERIES WITH BREAKS. Statistica Sinica, 33 (Online Special Issue), 1193-1217. https://doi.org/10.5705/ss.202021.0117

RGC Funding Information

  • RGC-funded

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