Regime-switching factor models for high-dimensional time series

Xialu Liu, Rong Chen

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


We consider a factor model for high-dimensional time series with regimeswitching dynamics. The switching is assumed to be driven by an unobserved Markov chain; the mean, factor loading matrix, and covariance matrix of the noise process are different among the regimes. The model is an extension of the traditional factor models for time series and provides flexibility in dealing with applications in which underlying states may be changing over time. We propose an iterative approach to estimating the loading space of each regime and clustering the data points, combining eigenanalysis and the Viterbi algorithm. The theoretical properties of the procedure are investigated. Simulation results and the analysis of a data example are presented.

Original languageEnglish (US)
Pages (from-to)1427-1451
Number of pages25
JournalStatistica Sinica
Issue number4
StatePublished - Oct 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Factor model
  • Hidden Markov process
  • High-dimensional time series
  • Nonstationary process
  • Regime switch
  • Viterbi algorithm


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