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 well- specified and misspecified models.

References in corpus (1)

• Theory and Inference for a Class of Observation-driven Models with Application to Time Series of Counts

Cited by in corpus (1)

• Approximate Inference for Observation Driven Time Series Models with Intractable Likelihoods