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Three-stage estimation method for non-linear multiple time-series

Author

Listed:
  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Giovanni de Luca

    (Parthenope University - PARTHENOPE - Università degli Studi di Napoli “Parthenope” = University of Naples)

  • Giorgia Rivieccio

    (Parthenope University - PARTHENOPE - Università degli Studi di Napoli “Parthenope” = University of Naples)

Abstract

We present the three-stage pseudo maximum likelihood estimation in order to reduce the computational burdens when a copula-based model is applied to multiple time-series in high dimensions. The method is applied to general stationary Markov time series, under some assumptions which include a time-invariant copula as well as marginal distributions, extending the results of Yi and Liao [2010]. We explore, via simulated and real data, the performance of the model compared to the classical vectorial autoregressive model, giving the implications of misspecified assumptions for margins and/or joint distribution and providing tail dependence measures of economic variables involved in the analysis.

Suggested Citation

  • Dominique Guegan & Giovanni de Luca & Giorgia Rivieccio, 2017. "Three-stage estimation method for non-linear multiple time-series," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01439860, HAL.
    • Handle: RePEc:hal:cesptp:halshs-01439860
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01439860
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    References listed on IDEAS

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    Keywords

    Copula function; Three stage estimator; Multiple time series;
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