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.
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Bibliographic InfoPaper provided by COMISEF in its series Working Papers with number 009.
Length: 37 pages
Date of creation: 20 Feb 2009
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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)
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