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

2012/10/17 by Randal Douc, Paul Doukhan, Douc, Randal +4
Economics, Econometrics and Finance · Mathematics · #FOS: Mathematics #Financial Risk and Volatility Modeling #Statistical Methods and Inference #Statistics Theory (math.ST) #Stochastic processes and financial applications #math.ST #stat.TH

paper · pdf · doi:10.48550/arxiv.1210.4739

openalex publication_date 2012/10/17 · arxiv created 2012/10/22 · arxiv updated 2012/10/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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