Bayesian analysis of multiple thresholds autoregressive model

Jiazhu Pan (), Qiang Xia () and Jinshan Liu ()
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Jiazhu Pan: University of Strathclyde
Qiang Xia: South China Agricultural University
Jinshan Liu: South China Agricultural University

Computational Statistics, 2017, vol. 32, issue 1, No 10, 219-237

Abstract: 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 method, which combines Metropolis–Hastings 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.

Keywords: Threshold autoregressive model; Bayesian inference; MCMC; Metropolis–Hastings algorithm; Model selection (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00180-016-0673-3

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