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Parameter Identification in the Logistic STAR Model

  • Line Elvstrøm Ekner

    (Department of Economics, Copenhagen University)

  • Emil Nejstgaard

    (Department of Economics, Copenhagen University)

We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that the threshold autoregression (TAR) is a limiting case of the LSTAR process. We demonstrate how this fact impedes numerical optimization of the original parametrization, whereas this is not the case for the new parametrization. Next, we show that information criteria provide a tool to choose between an LSTAR model and a TAR model; a choice previously basedsolely on economic theory. Reestimation of two published applications illustrate the usefulness of our findings..

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Paper provided by University of Copenhagen. Department of Economics in its series Discussion Papers with number 13-07.

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Length: 23 pages
Date of creation: 19 Sep 2013
Date of revision:
Handle: RePEc:kud:kuiedp:1307
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  1. Medeiros, Marcelo & Veiga, Alvaro, 2000. "A Flexible Coefficient Smooth Transition Time Series Model," SSE/EFI Working Paper Series in Economics and Finance 360, Stockholm School of Economics, revised 10 Feb 2000.
  2. 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.
  3. Jensen, S ren Tolver & Rahbek, Anders, 2007. "On The Law Of Large Numbers For (Geometrically) Ergodic Markov Chains," Econometric Theory, Cambridge University Press, vol. 23(04), pages 761-766, August.
  4. Zacharias Psaradakis & Martin Sola & Fabio Spagnolo & Nicola Spagnolo, 2009. "Selecting nonlinear time series models using information criteria," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(4), pages 369-394, 07.
  5. Dennis Kristensen & Anders Rahbek, 2010. "Testing and Inference in Nonlinear Cointegrating Vector Error Correction Models," Discussion Papers 10-25, University of Copenhagen. Department of Economics.
  6. Frédérique Bec & Anders Rahbek & Neil Shephard, 2008. "The ACR model: a multivariate dynamic mixture autoregression," THEMA Working Papers 2008-11, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  7. Waldyr Dutra Areosa & Michael McAleer & Marcelo C. Medeiros, 2009. "Moment-Based Estimation of Smooth Transition Regression Models with Endogenous Variables," CIRJE F-Series CIRJE-F-671, CIRJE, Faculty of Economics, University of Tokyo.
  8. Hillebrand Eric & Medeiros Marcelo C. & Xu Junyue, 2013. "Asymptotic Theory for Regressions with Smoothly Changing Parameters," Journal of Time Series Econometrics, De Gruyter, vol. 5(2), pages 133-162, April.
  9. Frédérique Bec & Anders Rahbek & Neil Shephard, 2008. "The ACR Model: A Multivariate Dynamic Mixture Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(5), pages 583-618, October.
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