A Bayesian nonlinearity test for threshold moving average models

Qing Xia, Jiazhu Pan, Zhiqiang Zhang, Jinshan Liu

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
169 Downloads (Pure)


We propose a Bayesian test for nonlinearity of threshold moving average (TMA) models. First, we obtain the marginal posterior densities of all parameters, including the threshold and delay, of the TMA model using Gibbs sampler with the Metropolis-Hastings algorithm. And then, we adopt reversible-jump Markov chain Monte Carlo methods to calculate the posterior probabilities for MA and TMA models. Posterior evidence in favour of the TMA model indicates threshold nonlinearity. Simulation experiments and a real example show that our method works very well in distinguishing MA and TMA models.
Original languageEnglish
Pages (from-to)329-336
Number of pages8
JournalJournal of Time Series Analysis
Issue number5
Publication statusPublished - 1 Sept 2010


  • MA models
  • TMA models
  • RJMCMC methods
  • Metropolis-Hastings
  • Gibbs sampler
  • Bayesian inference


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