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Bayesian inference for order determination of double threshold variables autoregressive models

  • Xiaobing Zheng , Qiang Xia EMAIL logo and Rubing Liang EMAIL logo
Published/Copyright: November 2, 2022
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Studies in Nonlinear Dynamics & Econometrics
From the journal Studies in Nonlinear Dynamics & Econometrics Volume 27 Issue 4

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.

Keywords: Bayesian inference; DT-AR model; MCMC algorithm; reversible-jump; unknown order
JEL Classification: C11; C13
Corresponding authors: Qiang Xia and Rubing Liang, College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China, E-mail: (Q. Xia) and (R. Liang)

Funding source: The Major Research Plan of the National Natural Science Foundation of China

Award Identifier / Grant number: 91746102

Acknowledgments

We thank the Editor and the Referee(s) for their insightful comments and suggestions that help us significantly to improve our manuscript.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This research was supported by the National Natural Science Foundation of China (No.12171161, 91746102), the Natural Science Foundation of Guangdong Province of China (No. 2022A1515011754).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2020-08-14
Accepted: 2022-09-19
Published Online: 2022-11-02

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Approximate Bayesian inference for agent-based models in economics: a case study
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https://doi.org/10.1515/snde-2020-0096
Keywords for this article
Bayesian inference; DT-AR model; MCMC algorithm; reversible-jump; unknown order

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Approximate Bayesian inference for agent-based models in economics: a case study
  4. Anticipating extreme losses using score-driven shape filters
  5. Does real interest rate parity really work? Historical evidence from a discrete wavelet perspective
  6. The impact of forward guidance and large-scale asset purchase programs on commodity markets
  7. Middle-income traps and complexity in economic development
  8. Bayesian inference for order determination of double threshold variables autoregressive models
  9. Score-driven multi-regime Markov-switching EGARCH: empirical evidence using the Meixner distribution
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