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Bayesian inference for order determination of double threshold variables autoregressive models

Author

Listed:
  • Zheng Xiaobing
  • Xia Qiang
  • Liang Rubing

    (College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China)

Abstract

The reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm can generate a jump Markov chain in the parameter space of different dimensions, and select a suitable model effectively. In this paper, when the order of the double threshold variables autoregressive (DT-AR) is unknown, the RJMCMC method is designed to identify the order of the DT-AR model in this paper. The simulation experiments and the real example show that the proposed method works well in identifying the order and estimating the parameters of the DT-AR model simultaneously.

Suggested Citation

  • Zheng Xiaobing & Xia Qiang & Liang Rubing, 2023. "Bayesian inference for order determination of double threshold variables autoregressive models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(4), pages 567-587, September.
    • Handle: RePEc:bpj:sndecm:v:27:y:2023:i:4:p:567-587:n:5
    DOI: 10.1515/snde-2020-0096
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    More about this item

    Keywords

    Bayesian inference; DT-AR model; MCMC algorithm; reversible-jump; unknown order;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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