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

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	2620-2647
Number of pages	28
Journal	Stochastic Processes and their Applications
Volume	123
Issue number	7
DOIs	https://doi.org/10.1016/j.spa.2013.04.010
Publication status	Published - 1 Jan 2013

Keywords

Consistency
Ergodicity
Maximum likelihood
Observation-driven models
Stationarity
Time series of counts

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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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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

Abstract

Keywords

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