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

By Randal Douc, Paul Doukhan, Eric Moulines. 2012

76 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/11a9f42c47281ddd0f4626e941d6f6b1df075d68

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Citations in the corpus

Poisson QMLE of Count Time Series Models (2016, background): “Under the condition j˛0 C ˇ0j _ j˛0j _ jˇ0j < 1; (32) Douc et al. (2013) showed that the Poisson log-linear model with the log-intensity (31) admits a stationary and ergodic solution.”
QUASI‐LIKELIHOOD INFERENCE FOR NEGATIVE BINOMIAL TIME SERIES MODELS (2013, methodology): “…models for time series of counts include the works by Zeger and Qaqish (1988), Brumback et al. (2000), Fahrmeir and Tutz (2001), Kedem and Fokianos (2002), Davis et al. (2003), Jung et al. (2006) and more recently Fokianos and Tjøstheim (2011), Woodard et al. (2011) and Douc et al. (2013).”
Count Time Series: A Methodological Review (2021, background): “Series properties are established in Neumann (2011), Doukhan, Fokianos, and Tjøstheim (2012), and Douc, Doukhan, and Moulines (2013).”
Self-Excited Threshold Poisson Autoregression (2013, methodology): “The model was also found in another general study of observation-driven time series models by Douc, Doukhan, and Moulines (2013).”