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Identifying cointegration by eigenanalysis

Yao, Qiwei, Zhang, Rongmao and Robinson, Peter (2018) Identifying cointegration by eigenanalysis. Journal of the American Statistical Association. ISSN 0162-1459

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Identification Number: 10.1080/01621459.2018.1458620

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 non-negative 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.

Item Type: Article
Official URL: https://www.tandfonline.com/toc/uasa20/current
Additional Information: © 2018 Informa UK Limited
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Date Deposited: 11 Apr 2018 15:16
Last Modified: 20 May 2019 00:08
Projects: 11371318/11771390, LR16A010001, ES/J007242/1, EP/L01226X/1
Funders: National Natural Science Foundation of China, ZPNSFC, Economic & Social Research Council, Engineering and Physical Sciences Research Council
URI: http://eprints.lse.ac.uk/id/eprint/87431

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