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Nonlinear Persistence and Copersistence

Author

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  • Christian Gourieroux

    (Crest)

  • Joanna Jasiak

    (Crest)

Abstract

Theoretical research on long-term relationships between economic time series has a history that spans several decades during which various linear and nonlinear comovements were unveiled, such as the Phillips curve, and the purchasing power parity. In contrast, the econometric analysis of long-term relationships is much more recent, and has been conducted mainly in the linear framework. This is the case of the cointegration theory for nonstationary time series (see Granger, 1986; Engle and Granger, 1987; Johansen, 1998), and the codependence theory for stationary series (Gourieroux and Peaucelle, 1992; Engle and Kozicki, 1993; Kugler and Neusser, 1993). Under both approaches, the dynamics of the time series of interest (i.e. VAR model) as well as their long-term relationships are assumed to be linear.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Christian Gourieroux & Joanna Jasiak, 1999. "Nonlinear Persistence and Copersistence," Working Papers 99-63, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:99-63
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Non-linear Functions of Non-Stationary Data Can be Stationary
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2013-02-27 03:17:00

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    2. Dittmann, Ingolf & Granger, Clive W. J., 2002. "Properties of nonlinear transformations of fractionally integrated processes," Journal of Econometrics, Elsevier, vol. 110(2), pages 113-133, October.
    3. Darolles, Serge & Florens, Jean-Pierre & Gourieroux, Christian, 2004. "Kernel-based nonlinear canonical analysis and time reversibility," Journal of Econometrics, Elsevier, vol. 119(2), pages 323-353, April.

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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