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An exponential FISTAR model applied to the US real effective exchange rate

Author

Listed:
  • Mohamed Boutahar

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Imene Mootamri

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Anne Peguin-Feissolle

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

The aim of this paper is to study the dynamics of the US real effective exchange rate by capturing nonlinearity and long memory features. In this context, we used the family of fractionally integrated STAR (FISTAR) models proposed by van Dijk, Franses and Paap (2002) to the case when the transition function is an exponential function and we develop an estimation procedure. Indeed, these models can take into account processes characterized by several distinct dynamic regimes and persistence phenomena.

Suggested Citation

  • Mohamed Boutahar & Imene Mootamri & Anne Peguin-Feissolle, 2007. "An exponential FISTAR model applied to the US real effective exchange rate," Working Papers halshs-00353836, HAL.
    • Handle: RePEc:hal:wpaper:halshs-00353836
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00353836
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    References listed on IDEAS

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    Cited by:

    1. Mohamed Boutahar & Gilles Dufrénot & Anne Péguin-Feissolle, 2008. "A Simple Fractionally Integrated Model with a Time-varying Long Memory Parameter d t," Computational Economics, Springer;Society for Computational Economics, vol. 31(3), pages 225-241, April.

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