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Smooth Transition Autoregressive Models -- New Approaches to the Model Selection Problem

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  • Maringer Dietmar G.

    () (University of Essex)

  • Meyer Mark

    () (Justus-Liebig-University Giessen)

Abstract

It has been shown in the literature that the task of estimating the parameters of nonlinear models may be tackled with optimization heuristics. Thus, we attempt to carry these intuitions over to the estimation procedure of smooth transition autoregressive (STAR, Teräsvirta, 1994) models by introducing the following three stochastic optimization algorithms: Simulated Annealing, (Kirkpatrick, Gelatt, and Vecchi, 1983), Threshold Accepting (Dueck and Scheuer, 1990) and Differential Evolution (Storn and Price, 1995, 1997). Besides considering the performance of these heuristics in estimating STAR model parameters, our paper additionally picks up the problem of identifying redundant parameters which, according to our view, has not been addressed in a satisfactory way by now. The resulting findings of our simulation studies seem to argue for an implementation of heuristic approaches within the STAR modeling cycle. In particular for the case of STAR model specification, an application of these heuristics might offer valuable information to empirical researchers.

Suggested Citation

  • Maringer Dietmar G. & Meyer Mark, 2008. "Smooth Transition Autoregressive Models -- New Approaches to the Model Selection Problem," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(1), pages 1-21, March.
  • Handle: RePEc:bpj:sndecm:v:12:y:2008:i:1:n:5
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    References listed on IDEAS

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    1. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, 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. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    4. Felix Chan & Michael McAleer, 2002. "Maximum likelihood estimation of STAR and STAR-GARCH models: theory and Monte Carlo evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 509-534.
    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. Chen, Yi-Ting, 2003. "Discriminating between competing STAR models," Economics Letters, Elsevier, vol. 79(2), pages 161-167, May.
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    Cited by:

    1. Ardia, David & Boudt, Kris & Carl, Peter & Mullen, Katharine M. & Peterson, Brian, 2010. "Differential Evolution (DEoptim) for Non-Convex Portfolio Optimization," MPRA Paper 22135, University Library of Munich, Germany.
    2. Maciel, Leandro & Gomide, Fernando & Ballini, Rosangela, 2016. "A differential evolution algorithm for yield curve estimation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 129(C), pages 10-30.
    3. Chen, XiaoHua & Maringer, Dietmar, 2011. "Detecting time-variation in corporate bond index returns: A smooth transition regression model," Journal of Banking & Finance, Elsevier, vol. 35(1), pages 95-103, January.

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