Recent results for linear time series models with non independent innovations

Christian Francq; Jean Michel Zakoïan

doi:10.1007/0-387-24555-3_12

Recent results for linear time series models with non independent innovations

Université de Lille

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Chapitre › Revue par des pairs

Résumé

In this paper, we provide a review of some recent results for ARMA models with uncorrelated but non independent errors. The standard so-called Box-Jenkins methodology rests on the errors independence. When the errors are suspected to be non independent, which is often the case in real situations, this methodology needs to be adapted. We study in detail the main steps of this methodology in the above-mentioned framework.

langue originale	Anglais
titre	Statistical Modeling and Analysis for Complex Data Problems
Editeur	Springer US
Pages	241-265
Nombre de pages	25
ISBN (imprimé)	0387245545, 9780387245546
Les DOIs	https://doi.org/10.1007/0-387-24555-3_12
état	Publié - 1 déc. 2005
Modification externe	Oui

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10.1007/0-387-24555-3_12

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abstract = "In this paper, we provide a review of some recent results for ARMA models with uncorrelated but non independent errors. The standard so-called Box-Jenkins methodology rests on the errors independence. When the errors are suspected to be non independent, which is often the case in real situations, this methodology needs to be adapted. We study in detail the main steps of this methodology in the above-mentioned framework.",

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AB - In this paper, we provide a review of some recent results for ARMA models with uncorrelated but non independent errors. The standard so-called Box-Jenkins methodology rests on the errors independence. When the errors are suspected to be non independent, which is often the case in real situations, this methodology needs to be adapted. We study in detail the main steps of this methodology in the above-mentioned framework.

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PB - Springer US

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Recent results for linear time series models with non independent innovations

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