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

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 well-specified and misspecified models.

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Year of publication:	2013
Authors:	Douc, R. ; Doukhan, P. ; Moulines, E.
Published in:	Stochastic Processes and their Applications. - Elsevier, ISSN 0304-4149. - Vol. 123.2013, 7, p. 2620-2647
Publisher:	Elsevier
Subject:	Consistency \| Ergodicity \| Time series of counts \| Maximum likelihood \| Observation-driven models \| Stationarity

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010875058