Generalized Gaussian quasi-maximum likelihood estimation for most common time series - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Communications in Statistics - Theory and Methods Année : 2022

Generalized Gaussian quasi-maximum likelihood estimation for most common time series

Résumé

We propose a consistent estimator for the parameter shape of the generalized gaussian noise in the class of causal time series including ARMA, AR(∞), GARCH, ARCH(∞), ARMA-GARCH, APARCH, ARMA-APARCH,..., processes. As well we prove the consistency and the asymptotic normality of the Generalized Gaussian Quasi-Maximum Likelihood Estimator (GGQMLE) for this class of causal time series with any fixed parameter shape, which over-performs the efficiency of the classical Gaussian QMLE.
Fichier principal
Vignette du fichier
BOULAROUK & BARDET.pdf (218.25 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02902614 , version 1 (20-07-2020)

Identifiants

Citer

Yakoub Boularouk, Jean-Marc Bardet. Generalized Gaussian quasi-maximum likelihood estimation for most common time series. Communications in Statistics - Theory and Methods, 2022, ⟨10.1080/03610926.2022.2103148⟩. ⟨hal-02902614⟩
85 Consultations
432 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More