Thumbnail - No thumbnail

Asymptotic Theory for Maximum Likelihood Estimation of the Memory Parameter in Stationary Gaussian Processes

Lieberman, Offer; Rosemarin, Roy; Rousseau, Judith (2012), Asymptotic Theory for Maximum Likelihood Estimation of the Memory Parameter in Stationary Gaussian Processes, Econometric Theory, 28, 2, p. 457-470. http://dx.doi.org/10.1017/S0266466611000399

Type
Article accepté pour publication ou publié
Lien vers un document non conservé dans cette base
http://hal.archives-ouvertes.fr/hal-00641474/fr/
Date
2012
Nom de la revue
Econometric Theory
Volume
28
Numéro
2
Éditeur
Cambridge University Press
Pages
457-470
Identifiant publication
Métadonnées
Afficher la notice complète
Auteur(s)
Lieberman, Offer
Rosemarin, Roy
Rousseau, Judith
Résumé (EN)
Consistency, asymptotic normality and e ciency of the maximum likelihood estimator for stationary Gaussian time series, were shown to hold in the short memory case by Hannan (1973) and in the long memory case by Dahlhaus (1989). In this paper, we extend these results to the entire stationarity region, including the case of intermediate memory and noninvertibility. In the process of proving the main results, we provide a useful theorem on the limiting behavior of a product of Toeplitz matrices under strictly weaker conditions than those employed by Dahlhaus (1989).
Mots-clés
Intermediate Memory; Long Range Dependence; Maximum Likelihood Estimation; Toeplitz Matrix
JEL
C13 - Estimation: General
C22 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes