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Séries chronologiques non linéaires à temps discret

Author

Listed:
  • Dominique Guegan

    (CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique)

Abstract

Cet ouvrage présente des modèles récents de séries chronologiques non linéaires à temps discret comme les modèles à seuil, les modèles bilinéaires, les modèles ARCH, mais aussi les processus longue mémoire et à partir d'un point de vue un peu différent les modèles chaotiques. Il s'agit de proposer aux modélisateurs, jeunes chercheurs et chercheurs confirmés des techniques et des idées nouvelles qui permettent de sortir de l'approche classique des modèles linéaires. Aux utilisateurs, il propose une approche qui permet d'envisager des voies différentes quand les problèmes rencontrés sont liés à des non linéarités difficiles à « capter ».

Suggested Citation

  • Dominique Guegan, 1994. "Séries chronologiques non linéaires à temps discret," Post-Print halshs-00196420, HAL.
    • Handle: RePEc:hal:journl:halshs-00196420
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    Cited by:

    1. Jelloul Allal & Saïd El Melhaoui, 2006. "Optimal Detection of Exponential Component in Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(6), pages 793-810, November.
    2. Daniel Ventosa-Santaularia & Alfonso Mendoza, 2005. "Non Linear Moving-Average Conditional Heteroskedasticity," Department of Economics and Finance Working Papers EM200502, Universidad de Guanajuato, Department of Economics and Finance.

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