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Un test d'heteroscedasticite conditionnelle inspire de la modelisation en termes de reseaux neuronaux artificiels

Author

Listed:
  • Caulet, R.
  • Peguin-Feissolle, A.

Abstract

Ce papier considere un test d'heteroscedasticite conditionnelle basee sur la methode des reseaux neuronaux artificiels et en compare les performances avec des test standards, a l'aide de simulations de Monte-Carlo. L'hypothese alternative d'heteroscedasticite conditionnelle est representee par une variance conditionnelle de forme neuronale: le test du Multiplicateur de Lagrange qui en decoule permet de detecter une grande variete de formes d'heteroscedasticite conditionnelle. Les resultats des simulations, presentes sous forme graphique, montrent que ce test est relativement performant.

Suggested Citation

  • Caulet, R. & Peguin-Feissolle, A., 1999. "Un test d'heteroscedasticite conditionnelle inspire de la modelisation en termes de reseaux neuronaux artificiels," G.R.E.Q.A.M. 99a23, Universite Aix-Marseille III.
  • Handle: RePEc:fth:aixmeq:99a23
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    Cited by:

    1. Anne Péguin-Feissolle & Bilel Sanhaji, 2016. "Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models," Annals of Economics and Statistics, GENES, issue 123-124, pages 77-101.

    More about this item

    Keywords

    TESTS ; ECONOMETRIE ; HETEROSCEDASTIVITE;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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