Modelling multiple time series via common factors

Jiazhu Pan, Qiwei Yao

Research output: Contribution to journalArticle

54 Citations (Scopus)
60 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)365-379
Number of pages15
JournalBiometrika
Volume95
Issue number2
Early online date1 Sep 2007
DOIs
Publication statusPublished - 2008

Keywords

  • factor models
  • cross-correlation functions
  • dimension reduction
  • multivariate time series
  • non stationarity
  • portmanteau tests
  • white noise

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