Bayesian analysis of multiple thresholds autoregressive model

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• 作者：Jiazhu Pan ; Qiang Xia ; Jinshan Liu
• 关键词：Threshold autoregressive model ; Bayesian inference ; MCMC ; Metropolis–Hastings algorithm ; Model selection
• 刊名：Computational Statistics
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：32
• 期：1
• 页码：219-237
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics, general; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Economic Theory/Quantitative Economics/Mathematical Methods;
• 出版者：Springer Berlin Heidelberg
• ISSN：1613-9658
• 卷排序：32

文摘

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.