Bayesian analysis of multiple thresholds autoregressive model

Jiazhu Pan, Qiang Xia, Jinshan Liu

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
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Bayesian analysis of threshold autoregressive (TAR) model with various possible thresholds is considered. A method of Bayesian stochastic search selection is introduced to identify a threshold-dependent sequence with highest probability. All model parameters are computed by a hybrid Markov chain Monte Carlo (MCMC) method, which combines Metropolis-Hastings (M-H) algorithm and Gibbs sampler. The main innovation of the method introduced here is to estimate the TAR model without assuming the fixed number of threshold values, thus is more flexible and useful. Simulation experiments and a real data example lend further support to the proposed approach.
Original languageEnglish
Pages (from-to)219–237
Number of pages19
JournalComputational Statistics
Issue number1
Early online date9 Aug 2016
Publication statusPublished - 1 Mar 2017


  • threshold autoregressive model
  • Bayesian inference
  • Markov chain Monte Carlo
  • Metropolis-Hastings algorithm
  • model selection


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