Bayesian inference of trend and difference-stationarity

Robert E. Mcculloch, Ruey S. Tsay

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


This paper proposes a general Bayesian framework for distinguishing between trend- and difference-stationarity. Usually, in model selection, we assume that all of the data were generated by one of the models under consideration. In studying time series, however, we may be concerned that the process is changing over time, so that the preferred model changes over time as well. To handle this possibility, we compute the posterior probabilities of the competing models for each observation. This way we can see if different segments of the series behave differently with respect to the competing models. The proposed method is a generalization of the usual odds ratio for model discrimination in Bayesian inference. In application, we employ the Gibbs sampler to overcome the computational difficulty. The procedure is illustrated by a real example.

Original languageEnglish (US)
Pages (from-to)596-608
Number of pages13
JournalEconometric Theory
Issue number3-4
StatePublished - Aug 1994
Externally publishedYes

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics


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