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Modelling multiple time series via common factors

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  • Jiazhu Pan
  • Qiwei Yao

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 by expanding the white noise space step by step, thereby solving a high-dimensional optimization problem by several low-dimensional sub-problems. Asymptotic properties of the estimation are investigated. The proposed methodology is illustrated with both simulated and real datasets. Copyright 2008, Oxford University Press.

Suggested Citation

  • Jiazhu Pan & Qiwei Yao, 2008. "Modelling multiple time series via common factors," Biometrika, Biometrika Trust, vol. 95(2), pages 365-379.
    • Handle: RePEc:oup:biomet:v:95:y:2008:i:2:p:365-379
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    File URL: http://hdl.handle.net/10.1093/biomet/asn009
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