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

Douc, R. and Fokianos, K. and Moulines, E. (2017) Asymptotic properties of quasi-maximum likelihood estimators in observation-driven time series models∗. Electronic Journal of Statistics, 11 (2). pp. 2707-2740. ISSN 1935-7524

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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.

Item Type:
Journal Article
Journal or Publication Title:
Electronic Journal of Statistics
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2613
Subjects:
ID Code:
127725
Deposited By:
Deposited On:
26 Sep 2018 11:00
Refereed?:
Yes
Published?:
Published
Last Modified:
26 Sep 2020 05:31