Bayesian analysis of multiple thresholds autoregressive model

Pan, Jiazhu and Xia, Qiang and Liu, Jinshan (2017) Bayesian analysis of multiple thresholds autoregressive model. Computational Statistics, 32 (1). 219–237. ISSN 1613-9658 (https://doi.org/10.1007/s00180-016-0673-3)

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Abstract

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.

ORCID iDs

Pan, Jiazhu ORCID logoORCID: https://orcid.org/0000-0001-7346-2052, Xia, Qiang and Liu, Jinshan;
  • Item type:Article
    ID code:57442
    Dates:
    Date
    Event
    1 March 2017
    Published
    9 August 2016
    Published Online
    1 August 2016
    Accepted
    Notes:The final publication is available at Springer via http://dx.doi.org/10.1007/s00180-016-0673-3
    Keywords:threshold autoregressive model, Bayesian inference, Markov chain Monte Carlo, Metropolis-Hastings algorithm, model selection, Probabilities. Mathematical statistics, Computational Mathematics, Statistics and Probability, Statistics, Probability and Uncertainty, Economics, Econometrics and Finance(all)
    Subjects:Science > Mathematics > Probabilities. Mathematical statistics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:17 Aug 2016 10:44
    Last modified:05 Dec 2023 01:52
    URI:https://strathprints.strath.ac.uk/id/eprint/57442
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