IDEAS home Printed from https://ideas.repec.org/p/com/wpaper/009.html
   My bibliography  Save this paper

Time-varying Multi-regime Models Fitting by Genetic Algorithms

Author

Listed:
  • Francesco Battaglia
  • Mattheos Protopapas

Abstract

Many time series exhibit both nonlinearity and nonstationarity. Though both features have often been taken into account separately, few attempts have been proposed to model them simultaneously. We consider threshold models, and present a general model allowing for different regimes both in time and in levels, where regime transitions may happen according to self-exciting, or smoothly varying, or piecewise linear threshold modeling. Since fitting such a model involves the choice of a large number of structural parameters, we propose a procedure based on genetic algorithms, evaluating models by means of a generalized identification criterion. The performance of the proposed procedure is illustrated with a simulation study and applications to some real data.

Suggested Citation

  • Francesco Battaglia & Mattheos Protopapas, 2009. "Time-varying Multi-regime Models Fitting by Genetic Algorithms," Working Papers 009, COMISEF.
    • Handle: RePEc:com:wpaper:009
    as

    Download full text from publisher

    File URL: http://comisef.eu/files/wps009.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Baragona, R. & Battaglia, F. & Cucina, D., 2004. "Fitting piecewise linear threshold autoregressive models by means of genetic algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 277-295, September.
    2. Kapetanios, George & Shin, Yongcheol & Snell, Andy, 2003. "Testing for a unit root in the nonlinear STAR framework," Journal of Econometrics, Elsevier, vol. 112(2), pages 359-379, February.
    3. Wu, Berlin & Chang, Chih-Li, 2002. "Using genetic algorithms to parameters (d,r) estimation for threshold autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 38(3), pages 315-330, January.
    4. Chatterjee, Sangit & Laudato, Matthew & Lynch, Lucy A., 1996. "Genetic algorithms and their statistical applications: an introduction," Computational Statistics & Data Analysis, Elsevier, vol. 22(6), pages 633-651, October.
    5. Davis, Richard A. & Lee, Thomas C.M. & Rodriguez-Yam, Gabriel A., 2006. "Structural Break Estimation for Nonstationary Time Series Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 223-239, March.
    6. George Kapetanios, 2000. "Testing for a Unit Root against Nonlinear STAR Models," National Institute of Economic and Social Research (NIESR) Discussion Papers 164, National Institute of Economic and Social Research.
    7. Cathy W. S. Chen & Tsai-Hung Cherng & Berlin Wu, 2001. "On the Selection of Subset Bilinear Time Series Models: a Genetic Algorithm Approach," Computational Statistics, Springer, vol. 16(4), pages 505-517, December.
    8. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
    9. Cristina Amado & Timo Teräsvirta, 2008. "Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure," NIPE Working Papers 03/2008, NIPE - Universidade do Minho.
    10. Lin, Chien-Fu Jeff & Terasvirta, Timo, 1994. "Testing the constancy of regression parameters against continuous structural change," Journal of Econometrics, Elsevier, vol. 62(2), pages 211-228, June.
    11. Carlo Gaetan, 2000. "Subset ARMA Model Identification Using Genetic Algorithms," Journal of Time Series Analysis, Wiley Blackwell, vol. 21(5), pages 559-570, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Frauke Schleer, 2015. "Finding Starting-Values for the Estimation of Vector STAR Models," Econometrics, MDPI, vol. 3(1), pages 1-26, January.
    2. Francesco Battaglia & Mattheos Protopapas, 2012. "An analysis of global warming in the Alpine region based on nonlinear nonstationary time series models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(3), pages 315-334, August.
    3. Zhenxi Chen & Stefan Reitz, 2020. "Dynamics of the European sovereign bonds and the identification of crisis periods," Empirical Economics, Springer, vol. 58(6), pages 2761-2781, June.
    4. Francesco Battaglia & Mattheos K. Protopapas, 2010. "Multi-regime models for nonlinear nonstationary time series," Working Papers 026, COMISEF.
    5. Francesco Battaglia & Mattheos Protopapas, 2012. "Multi–regime models for nonlinear nonstationary time series," Computational Statistics, Springer, vol. 27(2), pages 319-341, June.
    6. Schleer, Frauke, 2013. "Finding starting-values for maximum likelihood estimation of vector STAR models," ZEW Discussion Papers 13-076, ZEW - Leibniz Centre for European Economic Research.
    7. Francesco Battaglia & Mattheos Protopapas, 2012. "Rejoinder to the discussion of “An analysis of global warming in the Alpine region based on nonlinear nonstationary time series models”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(3), pages 371-373, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francesco Battaglia & Mattheos K. Protopapas, 2010. "Multi-regime models for nonlinear nonstationary time series," Working Papers 026, COMISEF.
    2. Francesco Battaglia & Mattheos Protopapas, 2012. "An analysis of global warming in the Alpine region based on nonlinear nonstationary time series models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(3), pages 315-334, August.
    3. Francesco Battaglia & Mattheos Protopapas, 2012. "Multi–regime models for nonlinear nonstationary time series," Computational Statistics, Springer, vol. 27(2), pages 319-341, June.
    4. 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.
    5. Hasanov, Mübariz & Araç, Aysen & Telatar, Funda, 2010. "Nonlinearity and structural stability in the Phillips curve: Evidence from Turkey," Economic Modelling, Elsevier, vol. 27(5), pages 1103-1115, September.
    6. Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
    7. He, Changli & Sandberg, Rickard, 2005. "Dickey-Fuller Type of Tests against Nonlinear Dynamic Models," SSE/EFI Working Paper Series in Economics and Finance 580, Stockholm School of Economics.
    8. Richard A. Davis & Thomas C. M. Lee & Gabriel A. Rodriguez‐Yam, 2008. "Break Detection for a Class of Nonlinear Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(5), pages 834-867, September.
    9. Juan Carlos Cuestas, 2009. "Purchasing power parity in Central and Eastern European countries: an analysis of unit roots and nonlinearities," Applied Economics Letters, Taylor & Francis Journals, vol. 16(1), pages 87-94.
    10. Vasudeva N. R. Murthy & Emmanuel Anoruo, 2009. "Are Per Capita Real GDP Series in African Countries Non-stationary or Non-linear? What does Empirical Evidence Reveal?," Economics Bulletin, AccessEcon, vol. 29(4), pages 2492-2504.
    11. Fuyu Yang, 2007. "Bayesian Analysis of Deterministic Time Trend and Changes in Persistence Using a Generalised Stochastic Unit Root Model," Discussion Papers in Economics 07/11, Division of Economics, School of Business, University of Leicester.
    12. Frédérique Bec & Mélika Ben Salem & Marine Carrasco, 2010. "Detecting Mean Reversion in Real Exchange Rates from a Multiple Regime star Model," Annals of Economics and Statistics, GENES, issue 99-100, pages 395-427.
    13. Shu-Ling Chen & Hyeongwoo Kim, 2011. "Nonlinear Mean Reversion across National Stock Markets: Evidence from Emerging Asian Markets," International Economic Journal, Taylor & Francis Journals, vol. 25(2), pages 239-250.
    14. Andros Gregoriou & Alexandros Kontonikas, 2006. "Inflation Targeting And The Stationarity Of Inflation: New Results From An Estar Unit Root Test," Bulletin of Economic Research, Wiley Blackwell, vol. 58(4), pages 309-322, October.
    15. Christoph Rothe & Philipp Sibbertsen, 2006. "Phillips-Perron-type unit root tests in the nonlinear ESTAR framework," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(3), pages 439-456, September.
    16. Daiki Maki, 2008. "The Performance of Variance Ratio Unit Root Tests Under Nonlinear Stationary TAR and STAR Processes: Evidence from Monte Carlo Simulations and Applications," Computational Economics, Springer;Society for Computational Economics, vol. 31(1), pages 77-94, February.
    17. Mario Cerrato & Christian De Peretti & Nick Sarantis, 2007. "A nonlinear panel unit root test under cross section dependence," Documents de recherche 07-12, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
    18. Davinson Stev Abril‐Salcedo & Luis Fernando Melo‐Velandia & Daniel Parra‐Amado, 2020. "Nonlinear relationship between the weather phenomenon El niño and Colombian food prices," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(4), pages 1059-1086, October.
    19. Boldea, Otilia & Hall, Alastair R., 2013. "Estimation and inference in unstable nonlinear least squares models," Journal of Econometrics, Elsevier, vol. 172(1), pages 158-167.
    20. Emilio Zanetti Chini, 2013. "Generalizing smooth transition autoregressions," CREATES Research Papers 2013-32, Department of Economics and Business Economics, Aarhus University.

    More about this item

    Keywords

    Nonlinear time series; Nonstationary time series; Threshold model;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:com:wpaper:009. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Anil Khuman (email available below). General contact details of provider: http://www.comisef.eu .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.