Computational Statistics

, Volume 27, Issue 2, pp 319–341 | Cite as

Multi–regime models for nonlinear nonstationary time series

  • Francesco Battaglia
  • Mattheos K. Protopapas
Original Paper


Nonlinear nonstationary models for time series are considered, where the series is generated from an autoregressive equation whose coefficients change both according to time and the delayed values of the series itself, switching between several regimes. The transition from one regime to the next one may be discontinuous (self-exciting threshold model), smooth (smooth transition model) or continuous linear (piecewise linear threshold model). A genetic algorithm for identifying and estimating such models is proposed, and its behavior is evaluated through a simulation study and application to temperature data and a financial index.


Smooth transition autoregression Threshold model Genetic algorithm 

Mathematics Subject Classification (2000)

62M10 90C59 91B84 


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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Department of StatisticsSapienza University of RomeRomeItaly

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