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Determining the Number of Regimes in a Threshold Autoregressive Model Using Smooth Transition Autoregressions

Author

Listed:
  • Strikholm, Birgit

    () (Dept. of Economic Statistics, Stockholm School of Economics)

  • Teräsvirta, Timo

    () (Dept. of Economic Statistics, Stockholm School of Economics)

Abstract

In this paper we propose a method for determining the number of regimes in threshold autoregressive models using smooth transition autoregression as a tool. As the smooth transition model is just an approximation to the threshold autoregressive one, no asymptotic properties are claimed for the proposed method. Tests available for testing the adequacy of a smooth transition autoregressive model are applied sequentially to determine the number of regimes. A simulation study is performed in order to find out the finite-sample properties of the procedure and to compare it with two other procedures available in the literature. We find that our method works reasonably well for both single and multiple threshold models.

Suggested Citation

  • Strikholm, Birgit & Teräsvirta, Timo, 2005. "Determining the Number of Regimes in a Threshold Autoregressive Model Using Smooth Transition Autoregressions," SSE/EFI Working Paper Series in Economics and Finance 578, Stockholm School of Economics, revised 11 Feb 2005.
  • Handle: RePEc:hhs:hastef:0578
    Note: This is an early version of the paper published under a different title in Econometrics Journal 9, 472-491 (2006).
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    File URL: http://swopec.hhs.se/hastef/papers/hastef0578.pdf
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    References listed on IDEAS

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    1. Koop, Gary & Potter, Simon M, 1999. "Dynamic Asymmetries in U.S. Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 298-312, July.
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    3. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
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    5. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
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    Citations

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    Cited by:

    1. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    2. Bilgili, Faik, 2012. "Linear and nonlinear TAR panel unit root analyses for solid biomass energy supply of European countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6775-6781.
    3. Birgit Strikholm & Timo Teräsvirta, 2006. "A sequential procedure for determining the number of regimes in a threshold autoregressive model," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 472-491, November.
    4. Mohammad Mirbagherijam, 2014. "Thresholds Effect of Money Growth on Inflation in Iran," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 4(9), pages 319-329, September.
    5. Emilio Zanetti Chini, 2013. "Generalizing smooth transition autoregressions," CREATES Research Papers 2013-32, Department of Economics and Business Economics, Aarhus University.
    6. Modena, Matteo, 2008. "The term structure and the expectations hypothesis: a threshold model," MPRA Paper 9611, University Library of Munich, Germany.
    7. Ana Beatriz C. Galvao, 2006. "Structural break threshold VARs for predicting US recessions using the spread," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 463-487.
    8. Strikholm, Birgit, 2006. "Determining the number of breaks in a piecewise linear regression model," SSE/EFI Working Paper Series in Economics and Finance 648, Stockholm School of Economics.
    9. Ana Beatriz Galvão & Michael Artis & Massimiliano Marcellino, 2007. "The transmission mechanism in a changing world," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 39-61.
    10. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, Elsevier.
    11. Lazzarini, S. G. & Madalozzo, R. C & Artes, R. & Siqueira, J. O., 2004. "Measuring trust: An experiment in Brazil," Insper Working Papers wpe_42, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    12. Chang, Ya-Kai & Chen, Yu-Lun & Chou, Robin K. & Gau, Yin-Feng, 2013. "The effectiveness of position limits: Evidence from the foreign exchange futures markets," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4501-4509.
    13. Shahbaba Babak, 2009. "Discovering Hidden Structures Using Mixture Models: Application to Nonlinear Time Series Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-21, May.

    More about this item

    Keywords

    Model specification; model selection criterion; nonlinear modelling; sequential testing; switching regression;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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