Tail behaviours of multiple-regime threshold AR models with heavy-tailed innovations
Author:
Affiliation:
1. Department of Mathematics and Statistics , University of Strathclyde , Glasgow G1 1XH , UK
2. School of Mathematics and Statistics , Yangtze Normal University , Chongqing 408100 , China
Abstract
Publisher
Walter de Gruyter GmbH
Subject
Economics and Econometrics,Social Sciences (miscellaneous),Analysis,Economics and Econometrics,Social Sciences (miscellaneous),Analysis
Link
https://www.degruyter.com/document/doi/10.1515/snde-2020-0071/pdf
Reference37 articles.
1. Adler, R. J., R. Feldaman, and M. Taqqu. 1997. A User’s Guide to Heavy Tails: Statistical Techniques for Analyzing Heavy Tailed Distribution and Processes, 91–6. Boston: Brirkhauser.
2. An, H. Z., and S. G. Chen. 1997. “A Note on the Ergodicity of Non-linear Autoregressive Model.” Statistics & Probability Letters 34: 365–72. https://doi.org/10.1016/s0167-7152(96)00204-0.
3. An, H. Z., and F. Huang. 1996. “The Geometrical Ergodicity of Nonlinear Autoreggressive Models.” Statistica Sinica 6: 943–56.
4. Asimita, A., R. Gerrarda, Y. Houb, and L. Peng. 2016. “Tail Dependence Measure for Examining Financial Extreme Co-movements.” Journal of Econometrics 194: 330–48. https://doi.org/10.1016/j.jeconom.2016.05.011.
5. Borkovec, M., and C. Kluppelberg. 2001. “The Tail of the Stationary Distribution of an Autoregressive Process with Arch(1) Errors.” Annals of Applied Probability 11: 1220–41. https://doi.org/10.1214/aoap/1015345401.
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