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Multivariate Self-Exciting Threshold Autoregressive Modeling by Genetic Algorithms

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  • Roberto Baragona

    ()
    (Sapienza University of Rome)

  • Domenico Cucina

    ()
    (Sapienza University of Rome)

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    Abstract

    Several nonlinear time series models have been proposed in the literature to explain various empirical nonlinear features of many observed financial and economic time series. One model that has gained much attention is the so-called self-exciting threshold autoregressive (SETAR) model. It has been found very effective for modeling and forecasting nonlinear time series in a wide range of application fields. Furthermore, SETAR model is able to capture nonlinear characteristics as limit cycles, jump resonance, and time irreversibility. In this work the attention is focused on a multivariate SETAR (MSETAR) model where each linear regime follows a vector autoregressive (VAR) process and the thresholds are multivariate. We propose a methodology based on genetic algorithms (GAs) for building MSETAR models. The GA is designed to estimate the structural parameters, i. e. to determine the appropriate number of regimes and find multivariate threshold parameters. The behavior of the proposed methodology has been observed on a simulation experiment involving three artificial data sets.

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    Bibliographic Info

    Article provided by Justus-Liebig University Giessen, Department of Statistics and Economics in its journal Journal of Economics and Statistics.

    Volume (Year): 233 (2013)
    Issue (Month): 1 (January)
    Pages: 3-21

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    Handle: RePEc:jns:jbstat:v:233:y:2013:i:1:p:3-21

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    Related research

    Keywords: Genetic algorithms; Monte Carlo simulation; Multivariate self-exciting threshold autoregression;

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    References

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    1. Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
    2. 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.
    3. Ivan Savin & Peter Winker, 2011. "Heuristic model selection for leading indicators in Russia and Germany," Working Papers 046, COMISEF.
    4. Francesco Battaglia & Mattheos K. Protopapas, 2010. "Multi-regime models for nonlinear nonstationary time series," Working Papers 026, COMISEF.
    5. 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.
    6. Roberto Baragona & Francesco Battaglia & Domenico Cucina, 2004. "Estimating threshold subset autoregressive moving-average models by genetic algorithms," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 39-61.
    7. Yang, Zheng & Tian, Zheng & Yuan, Zixia, 2007. "GSA-based maximum likelihood estimation for threshold vector error correction model," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 109-120, September.
    8. Arifovic, Jasmina & Gençay, Ramazan, 2001. "Using genetic algorithms to select architecture of a feedforward artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 289(3), pages 574-594.
    9. Medeiros, Marcelo & Veiga, Alvaro & Resende, Mauricio, 2000. "A Combinatorial Approach to Piecewise Linear Time Series Analysis," Working Paper Series in Economics and Finance 393, Stockholm School of Economics.
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