A new smoothed QMLE for AR processes with LARCH errorsReport as inadecuate

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1 SAMOS - Statistique Appliquée et MOdélisation Stochastique 2 CREST - Centre de Recherche en Économie et Statistique 3 CES - Centre d-économie de la Sorbonne 4 IRMAR - Institut de Recherche Mathématique de Rennes

Abstract : We introduce a smoothed version of the quasi maximum likelihood estimator QMLE in order to fit heteroschedastic time series with possibly vanishing conditional variance. We apply this procedure to a finite-order autoregressive process with linear ARCH errors. We prove both the almost sure consistency and the asymptotic normality of our estimator. This estimator is more robust that QMLE with the same type of assumptions. A numerical study confirms the qualities of our procedure.

Keywords : Inference for time series

Author: Lionel Truquet -

Source: https://hal.archives-ouvertes.fr/


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