Time-varying Multi-regime Models Fitting by Genetic Algorithms
AbstractMany 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by COMISEF in its series Working Papers with number 009.
Length: 37 pages
Date of creation: 20 Feb 2009
Date of revision:
Contact details of provider:
Web page: http://www.comisef.eu
Nonlinear time series; Nonstationary time series; Threshold model;
Other versions of this item:
- Francesco Battaglia & Mattheos K. Protopapas, 2011. "Time‐varying multi‐regime models fitting by genetic algorithms," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 237-252, 05.
- NEP-ALL-2009-12-05 (All new papers)
- NEP-CMP-2009-12-05 (Computational Economics)
- NEP-ECM-2009-12-05 (Econometrics)
- NEP-ETS-2009-12-05 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Amado, Cristina & Teräsvirta, Timo, 2008.
"Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure,"
Working Paper Series in Economics and Finance
691, Stockholm School of Economics.
- Christina Amado & Timo Teräsvirta, 2008. "Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure," CREATES Research Papers 2008-08, School of Economics and Management, University of Aarhus.
- Cristina Amado & Timo Teräsvirta, 2008. "Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure," NIPE Working Papers 03/2008, NIPE - Universidade do Minho.
- 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.
- n/a, 2001. "Balance of payments prospects in EMU," NIESR Discussion Papers 164, National Institute of Economic and Social Research.
- 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.
- Michael McAller & Marcelo C. Medeiros, 2007. "A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries," Textos para discussÃ£o 544, Department of Economics PUC-Rio (Brazil).
- 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.
- 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.
- 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.
- 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.
- 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.
- Francesco Battaglia & Mattheos Protopapas, 2012. "An analysis of global warming in the Alpine region based on nonlinear nonstationary time series models," Statistical Methods and Applications, Springer, vol. 21(3), pages 315-334, August.
- 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 and Applications, Springer, vol. 21(3), pages 371-373, August.
- Francesco Battaglia & Mattheos Protopapas, 2012. "Multi–regime models for nonlinear nonstationary time series," Computational Statistics, Springer, vol. 27(2), pages 319-341, June.
- Francesco Battaglia & Mattheos K. Protopapas, 2010. "Multi-regime models for nonlinear nonstationary time series," Working Papers 026, COMISEF.
- Schleer, Frauke, 2013. "Finding starting-values for maximum likelihood estimation of vector STAR models," ZEW Discussion Papers 13-076, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Anil Khuman).
If references are entirely missing, you can add them using this form.