Identifying Cointegration by Eigenanalysis

• 成果类型: Article
• 署名作者: Zhang, Rongmao; Robinson, Peter; Yao, Qiwei
• 署名单位: Zhejiang University; Zhejiang University; University of London; London School Economics & Political Science; University of London; London School Economics & Political Science; Peking University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2018.1458620
• 发表日期: 2019
• 页码: 916-927
• 关键词: asymptotic properties; inference; rank

摘要

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.