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Multi–regime models for nonlinear nonstationary time series

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Abstract

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.

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Authors and Affiliations

  1. Department of Statistics, Sapienza University of Rome, Piazzale Aldo Moro 5, 00100, Rome, Italy

    Francesco Battaglia & Mattheos K. Protopapas

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  1. Francesco Battaglia
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  2. Mattheos K. Protopapas
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Correspondence to Francesco Battaglia.

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Battaglia, F., Protopapas, M.K. Multi–regime models for nonlinear nonstationary time series. Comput Stat 27, 319–341 (2012). https://doi.org/10.1007/s00180-011-0259-z

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  • DOI: https://doi.org/10.1007/s00180-011-0259-z

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