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: https://orcid.org/0000-0001-7346-2052, Xia, Qiang and Liu, Jinshan;-
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Item type: Article ID code: 57442 Dates: DateEvent1 March 2017Published9 August 2016Published Online1 August 2016AcceptedNotes: The final publication is available at Springer via http://dx.doi.org/10.1007/s00180-016-0673-3 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: 16 Sep 2024 00:42 URI: https://strathprints.strath.ac.uk/id/eprint/57442