Time‐varying multi‐regime models fitting by genetic algorithms
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.
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Volume (Year): 32 (2011)
Issue (Month): 3 (05)
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- 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.
- Michael McAller & Marcelo C. Medeiros, 2007.
"A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries,"
Textos para discussão
544, Department of Economics PUC-Rio (Brazil).
- 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.
- Christina Amado & Timo Teräsvirta, 2008.
"Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure,"
CREATES Research Papers
2008-08, Department of Economics and Business Economics, Aarhus University.
- Amado, Cristina & Teräsvirta, Timo, 2008. "Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure," SSE/EFI Working Paper Series in Economics and Finance 691, Stockholm School of Economics.
- Cristina Amado & Timo Teräsvirta, 2008. "Modelling Conditional and Unconditional Heteroskedasticity with Smoothly Time-Varying Structure," NIPE Working Papers 03/2008, NIPE - Universidade do Minho.
- 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.
- 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.
- Dirk Te Velde, 2001. "Balance of payments prospects in EMU," NIESR Discussion Papers 178, National Institute of Economic and Social Research.
- 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.
- 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.
- 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.
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