Identifying Cointegration by Eigenanalysis. Issue 526 (3rd April 2019)

• Record Type: Journal Article
• Authors: Zhang, Rongmao; Robinson, Peter; Yao, Qiwei
• Is Part Of: Journal of the American Statistical Association. Volume 114:Issue 526(2019)
• Page Start: 916
• Page End: 927
• Publication Date: 2019-04-03
• Subjects: Cointegration -- Eigenanalysis -- I(d) -- Nonstationary processes -- Vector time series; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2018.1458620
• Languages: English
• ISSNs: 0162-1459

Abstract

ABSTRACT: We propose a new and easy-to-use method for identifying cointegrated components of nonstationary time series, consisting of an eigenanalysis for a certain nonnegative definite matrix. Our setting is model-free, and we allow the integer-valued integration orders of the observable series to be unknown, and to possibly differ. Consistency of estimates of the cointegration space and cointegration rank is established both when the dimension of the observable time series is fixed as sample size increases, and when it diverges slowly. The proposed methodology is also extended and justified in a fractional setting. A Monte Carlo study of finite-sample performance, and a small empirical illustration, are reported. Supplementary materials for this article are available online.