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Estimation of Parametric Models with Conditional Heteroscedastic Errors

Christian Lavergne 1 Yann Vernaz 1
1 IS2 - Statistical Inference for Industry and Health
Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
Abstract : We consider a model with conditional heteroscedastic errors. The model requires only the form of conditional mean and conditional variance functions to be specified. We propose an effective approach for fitting this class of model. Our estimator is deduced from quasi-likelihood concept using an iterative and adaptive procedure. The convergence properties are establishe- d. Finally, our method and widely used estimators are compared via numerical experiments.
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https://hal.inria.fr/inria-00073014
Contributor : Rapport de Recherche Inria <>
Submitted on : Wednesday, May 24, 2006 - 11:35:36 AM
Last modification on : Monday, February 10, 2020 - 4:36:45 PM
Long-term archiving on: : Sunday, April 4, 2010 - 11:31:22 PM

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  • HAL Id : inria-00073014, version 1

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Christian Lavergne, Yann Vernaz. Estimation of Parametric Models with Conditional Heteroscedastic Errors. [Research Report] RR-3658, INRIA. 1999. ⟨inria-00073014⟩

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