Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator

R. Douca; P. Doukhanb; E. Moulinesc

doi:10.1016/j.spa.2013.04.010

Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator

R. Douca
, P. Doukhanb
, E. Moulinesc

CY Cergy Paris Université
CNRS LTCI

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

Résumé

This paper deals with a general class of observation-driven time series models with a special focus on time series of counts. We provide conditions under which there exist strict-sense stationary and ergodic versions of such processes. The consistency of the maximum likelihood estimators is then derived for wellspecified and misspecified models.

langue originale	Anglais
Pages (de - à)	2620-2647
Nombre de pages	28
journal	Stochastic Processes and their Applications
Volume	123
Numéro de publication	7
Les DOIs	https://doi.org/10.1016/j.spa.2013.04.010
état	Publié - 1 janv. 2013

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10.1016/j.spa.2013.04.010

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title = "Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator",

abstract = "This paper deals with a general class of observation-driven time series models with a special focus on time series of counts. We provide conditions under which there exist strict-sense stationary and ergodic versions of such processes. The consistency of the maximum likelihood estimators is then derived for wellspecified and misspecified models.",

keywords = "Consistency, Ergodicity, Maximum likelihood, Observation-driven models, Stationarity, Time series of counts",

author = "R. Douca and P. Doukhanb and E. Moulinesc",

year = "2013",

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doi = "10.1016/j.spa.2013.04.010",

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T1 - Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator

AU - Douca, R.

AU - Doukhanb, P.

AU - Moulinesc, E.

PY - 2013/1/1

Y1 - 2013/1/1

N2 - This paper deals with a general class of observation-driven time series models with a special focus on time series of counts. We provide conditions under which there exist strict-sense stationary and ergodic versions of such processes. The consistency of the maximum likelihood estimators is then derived for wellspecified and misspecified models.

AB - This paper deals with a general class of observation-driven time series models with a special focus on time series of counts. We provide conditions under which there exist strict-sense stationary and ergodic versions of such processes. The consistency of the maximum likelihood estimators is then derived for wellspecified and misspecified models.

KW - Consistency

KW - Ergodicity

KW - Maximum likelihood

KW - Observation-driven models

KW - Stationarity

KW - Time series of counts

U2 - 10.1016/j.spa.2013.04.010

DO - 10.1016/j.spa.2013.04.010

M3 - Article

AN - SCOPUS:84885074604

SN - 0304-4149

VL - 123

SP - 2620

EP - 2647

JO - Stochastic Processes and their Applications

JF - Stochastic Processes and their Applications

IS - 7

ER -

Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator

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