Bayesian inference for periodic regime-switching models

Eric Ghysels, Robert E. Mcculloch, Ruey S. Tsay

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


We present a general class of nonlinear time-series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for non-trivial dependencies between seasonal, cyclical and long-term patterns in the data. To overcome the computational burden we adopt a Bayesian approach to estimation and inference. This paper contains two empirical examples as illustration, one uses housing starts data while the other employs US post-Second World War industrial production.

Original languageEnglish (US)
Pages (from-to)129-143
Number of pages15
JournalJournal of Applied Econometrics
Issue number2
StatePublished - 1998
Externally publishedYes

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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