Bayesian analysis of multiple thresholds autoregressive model

Tools

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;
CORE (COnnecting REpositories)