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Tail behaviours of multiple-regime threshold AR models with heavy-tailed innovations

  • Jiazhu Pan EMAIL logo and Yali He
Published/Copyright: June 27, 2022
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Studies in Nonlinear Dynamics & Econometrics
From the journal Studies in Nonlinear Dynamics & Econometrics Volume 27 Issue 3

Abstract

This paper studies the tail behaviours of the stationary distribution of multiple-regime threshold AR models with multiple heavy-tailed innovations. It is shown that the marginal tail probability has the same order as that of the innovation with the heaviest tail. Other new results in this paper include the geometric ergodicity and the tail dependence of TAR models with multiple heavy-tailed innovations.

Keywords: ergodicity; heavy-tailed distribution; tail probability; threshold AR model
JEL Classification: C22; C32
Corresponding author: Jiazhu Pan, Department of Mathematics and Statistics, University of Strathclyde, Glasgow G1 1XH, UK; and School of Mathematics and Statistics, Yangtze Normal University, Chongqing 408100, China, E-mail:

Acknowledgments

We thank the Editor and the Referee(s) for their insightful comments and suggestions that make us improve our paper significantly. The first author wishes to thank Professor Hongzhi An and Professor Howell Tong for stimulating discussions on TAR models.

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

  2. Research funding: This work was partially supported by the National Natural Science Foundation of China (Grant No.12171161).

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

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Received: 2020-06-27
Revised: 2022-06-08
Accepted: 2022-06-08
Published Online: 2022-06-27

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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  1. Frontmatter
  2. Research Articles
  3. On determination of the number of factors in an approximate factor model
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  5. Panel data models with two threshold variables
  6. What will drive global economic growth in the digital age?
  7. On the nonlinear relationships between shadow economy and the three pillars of sustainable development: new evidence from panel threshold analysis
  8. Tail behaviours of multiple-regime threshold AR models with heavy-tailed innovations
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https://doi.org/10.1515/snde-2020-0071
Keywords for this article
ergodicity; heavy-tailed distribution; tail probability; threshold AR model

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. On determination of the number of factors in an approximate factor model
  4. Clean energy consumption and economic growth in China: a time-varying analysis
  5. Panel data models with two threshold variables
  6. What will drive global economic growth in the digital age?
  7. On the nonlinear relationships between shadow economy and the three pillars of sustainable development: new evidence from panel threshold analysis
  8. Tail behaviours of multiple-regime threshold AR models with heavy-tailed innovations
  9. Stock price prediction using multi-scale nonlinear ensemble of deep learning and evolutionary weighted support vector regression
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