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Subset threshold autoregression

Author

Listed:
  • Cathy W. S. Chen

    (Feng-Chia University, Taiwan)

  • Mike K. P. So

    (Hong Kong University of Science and Technology)

Abstract

We develop in this paper an efficient way to select the best subset threshold autoregressive model. The proposed method uses a stochastic search idea. Differing from most conventional approaches, our method does not require us to fix the delay or the threshold parameters in advance. By adopting the Markov chain Monte Carlo techniques, we can identify the best subset model from a very large of number of possible models, and at the same time estimate the unknown parameters. A simulation experiment shows that the method is very effective. In its application to the US unemployment rate, the stochastic search method successfully selects lag one as the time delay and five best models from more than 4000 choices. Copyright © 2003 John Wiley & Sons, Ltd.

Suggested Citation

  • Cathy W. S. Chen & Mike K. P. So, 2003. "Subset threshold autoregression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(1), pages 49-66.
    • Handle: RePEc:jof:jforec:v:22:y:2003:i:1:p:49-66
    DOI: 10.1002/for.859
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    References listed on IDEAS

    as
    1. B. Y. Thanoon, 1990. "Subset Threshold Autoregression With Applications," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(1), pages 75-87, January.
    2. Cathy W. S. Chen & Jack C. Lee, 1995. "Bayesian Inference Of Threshold Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(5), pages 483-492, September.
    3. Nicholas G. Polson & George C. Tiao (ed.), 1995. "Bayesian Inference," Books, Edward Elgar Publishing, volume 0, number 602.
    4. 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.
    5. Bradley P. Carlin & Alan E. Gelfand & Adrian F. M. Smith, 1992. "Hierarchical Bayesian Analysis of Changepoint Problems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 389-405, June.
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    Citations

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    Cited by:

    1. Laurent Ferrara & Dominique Guégan, 2006. "Detection of the Industrial Business Cycle using SETAR Models," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 353-371.
    2. Anne Peguin-Feissolle & Gilles Dufrénot & Dominique Guegan, 2006. "Changing-regime volatility : A fractionally integrated SETAR model," Working Papers halshs-00410540, HAL.
    3. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2004. "Estimating threshold subset autoregressive moving-average models by genetic algorithms," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 39-61.
    4. Liu, Hsiang-Hsi & Chuang, Wen-I & Huang, Jih-Jeng & Chen, Yu-Hao, 2016. "The overconfident trading behavior of individual versus institutional investors," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 518-539.
    5. Florian Huber, 2014. "Forecasting Exchange Rates using Bayesian Threshold Vector Autoregressions," Economics Bulletin, AccessEcon, vol. 34(3), pages 1687-1695.
    6. Dufrenot, Gilles & Guegan, Dominique & Peguin-Feissolle, Anne, 2005. "Long-memory dynamics in a SETAR model - applications to stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(5), pages 391-406, December.
    7. Cathy Chen & Feng Liu & Richard Gerlach, 2011. "Bayesian subset selection for threshold autoregressive moving-average models," Computational Statistics, Springer, vol. 26(1), pages 1-30, March.
    8. Xiaobing Zheng & Kun Liang & Qiang Xia & Dabin Zhang, 2022. "Best Subset Selection for Double-Threshold-Variable Autoregressive Moving-Average Models: The Bayesian Approach," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1175-1201, March.
    9. Philipp Piribauer, 2016. "Heterogeneity in spatial growth clusters," Empirical Economics, Springer, vol. 51(2), pages 659-680, September.
    10. Giannikis, D. & Vrontos, I.D. & Dellaportas, P., 2008. "Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1549-1571, January.

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