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Bayesian Subset Selection for Two-Threshold Variable Autoregressive Models

Author

Listed:
  • Ni Shuxia

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

  • Xia Qiang

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

  • Liu Jinshan

    (School of Financial Mathematics and Statistics, Guangdong University of Finance, Guangzhou 510521, China)

Abstract

In this paper, we propose and study an effective Bayesian subset selection method for two-threshold variable autoregressive (TTV-AR) models. The usual complexity of model selection is increased by capturing the uncertainty of the two unknown threshold levels and the two unknown delay lags. By using Markov chain Monte Carlo (MCMC) techniques with driven by a stochastic search, we can identify the best subset model from a large number of possible choices. Simulation experiments show that the proposed method works very well. As applied to the application to the Hang Seng index, we successfully distinguish the best subset TTV-AR model.

Suggested Citation

  • Ni Shuxia & Xia Qiang & Liu Jinshan, 2018. "Bayesian Subset Selection for Two-Threshold Variable Autoregressive Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(4), pages 1-16, September.
  • Handle: RePEc:bpj:sndecm:v:22:y:2018:i:4:p:16:n:5
    DOI: 10.1515/snde-2017-0062
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    References listed on IDEAS

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    1. Frankel, Jeffrey A & Rose, Andrew K, 1996. "Currency Crashes in Emerging Markets: Empirical Indicators," CEPR Discussion Papers 1349, C.E.P.R. Discussion Papers.
    2. Haiqiang Chen & Terence Chong & Jushan Bai, 2012. "Theory and Applications of TAR Model with Two Threshold Variables," Econometric Reviews, Taylor & Francis Journals, vol. 31(2), pages 142-170.
    3. Qiang Xia & Jiazhu Pan & Zhiqiang Zhang & Jinshan Liu, 2010. "A Bayesian nonlinearity test for threshold moving average models," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(5), pages 329-336, September.
    4. Jeffrey D. Sachs & Aaron Tornell & Andrés Velasco, 1996. "Financial Crises in Emerging Markets: The Lessons from 1995," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 27(1), pages 147-216.
    5. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency crashes in emerging markets: An empirical treatment," Journal of International Economics, Elsevier, vol. 41(3-4), pages 351-366, November.
    6. John Geweke & Nobuhiko Terui, 1993. "Bayesian Threshold Autoregressive Models For Nonlinear Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(5), pages 441-454, September.
    7. Graciela L. Kaminsky, 1998. "Currency and banking crises: the early warnings of distress," International Finance Discussion Papers 629, Board of Governors of the Federal Reserve System (U.S.).
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