Detecting multiple change-points in general causal time series using penalized quasi-likelihood - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Electronic Journal of Statistics Année : 2012

Detecting multiple change-points in general causal time series using penalized quasi-likelihood

Résumé

This paper is devoted to the off-line multiple change-point detection in a semiparametric framework. The time series is supposed to belong to a large class of models including AR($\infty$), ARCH($\infty$), TARCH($\infty$),... models where the coefficients change at each instant of breaks. The different unknown parameters (number of changes, change dates and parameters of successive models) are estimated using a penalized contrast built on conditional quasi-likelihood. Under Lipshitzian conditions on the model, the consistency of the estimator is proved when the moment order $r$ of the process satisfies $r\geq 2$. If $r\geq 4$, the same convergence rates for the estimators than in the case of independent random variables are obtained. The particular cases of AR($\infty$), ARCH($\infty$) and TARCH($\infty$) show that our method notably improves the existing results.
Fichier principal
Vignette du fichier
Change_causalmodels18.pdf (341.68 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00507759 , version 1 (30-07-2010)

Identifiants

Citer

Jean-Marc Bardet, William Charky Kengne, Olivier Wintenberger. Detecting multiple change-points in general causal time series using penalized quasi-likelihood. Electronic Journal of Statistics , 2012, 6, pp.435-477. ⟨hal-00507759⟩
238 Consultations
196 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More