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Un test d'hétéroscédasticité conditionnelle inspiré de la modélisation en termes de réseaux neuronaux artificiels

Author

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  • Renaud Caulet
  • Anne Peguin-Feissolle

Abstract

Ce papier considère un test d'hétéroscédasticité conditionnelle basé sur la méthode des réseaux neuronaux artificiels et en compare les performances avec des tests standards, à l'aide de simulations de Monte-Carlo. L'hypothèse alternative d'hétéroscédasticité conditionnelle est représentée par une variance conditionnelle de forme neuronale; le test du Multiplicateur de Lagrange qui en découle permet de détecter une grande variété de formes d'hétéroscédasticité conditionnelle. Les résultats des simulations, présentés sous forme graphique, montrent que ce test est relativement performant.

Suggested Citation

  • Renaud Caulet & Anne Peguin-Feissolle, 2000. "Un test d'hétéroscédasticité conditionnelle inspiré de la modélisation en termes de réseaux neuronaux artificiels," Annals of Economics and Statistics, GENES, issue 59, pages 177-197.
    • Handle: RePEc:adr:anecst:y:2000:i:59:p:177-197
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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

    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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