Asymptotic properties of quasi-maximum likelihood estimators in observation-driven time series models

Abstract : We study a general class of quasi-maximum likelihood estimators for observation-driven time series models. Our main focus is on models related to the exponential family of distributions like Poisson based models for count time series or duration models. However, the proposed approach is more general and covers a variety of time series models including the ordinary GARCH model which has been studied extensively in the literature. We provide general conditions under which quasi-maximum likelihood estimators can be analyzed for this class of time series models and we prove that these estimators are consistent and asymptotically normally distributed regardless of the true data generating process. We illustrate our results using classical examples of quasi-maximum likelihood estimation including standard GARCH models, duration models, Poisson type autoregressions and ARMA models with GARCH errors. Our contribution unifies the existing theory and gives conditions for proving consistency and asymptotic normality in a variety of situations.
Type de document :
Article dans une revue
Electronic journal of statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2017, 11 (2), pp.2707 - 2740. 〈10.1214/17-EJS1299〉
Liste complète des métadonnées

https://hal.inria.fr/hal-01668243
Contributeur : Eric Moulines <>
Soumis le : mardi 19 décembre 2017 - 22:29:59
Dernière modification le : jeudi 10 mai 2018 - 02:05:54

Lien texte intégral

Identifiants

Citation

Randal Douc, Konstantinos Fokianos, Eric Moulines. Asymptotic properties of quasi-maximum likelihood estimators in observation-driven time series models. Electronic journal of statistics , Shaker Heights, OH : Institute of Mathematical Statistics, 2017, 11 (2), pp.2707 - 2740. 〈10.1214/17-EJS1299〉. 〈hal-01668243〉

Partager

Métriques

Consultations de la notice

194