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Modelling multiple time series via common factors

Pan, Jiazhu and Yao, Qiwei (2008) Modelling multiple time series via common factors. Biometrika, 95 (2). pp. 365-379. ISSN 1464-3510

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Abstract

We propose a new method for estimating common factors of multiple time series. One distinctive feature of the new approach is that it is applicable to some nonstationary time series. The unobservable (nonstationary) factors are identified via expanding the white noise space step by step; therefore solving a high-dimensional optimization problem by several low-dimensional subproblems. Asymptotic properties of the estimation were investigated. The proposed methodology was illustrated with both simulated and real data sets.

Item type: Article
ID code: 13677
Keywords: factor models, cross-correlation functions, dimension reduction, multivariate time series, non stationarity, portmanteau tests, white noise, Mathematics, Agricultural and Biological Sciences(all), Applied Mathematics, Statistics and Probability, Statistics, Probability and Uncertainty, Mathematics(all), Agricultural and Biological Sciences (miscellaneous)
Subjects: Science > Mathematics
Department: Faculty of Science > Mathematics and Statistics
Depositing user: Mrs Carolynne Westwood
Date Deposited: 12 Jan 2010 15:22
Last modified: 18 Jun 2015 08:58
URI: http://strathprints.strath.ac.uk/id/eprint/13677

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