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Asymptotic properties of quasi-maximum likelihood estimators in observation-driven time series models. (English) Zbl 1366.62173
Summary: We study a general class of quasi-maximum likelihood estimators for observation-driven time series models. Our main focus is on models related to the exponential family of distributions like Poisson based models for count time series or duration models. However, the proposed approach is more general and covers a variety of time series models including the ordinary GARCH model which has been studied extensively in the literature. We provide general conditions under which quasi-maximum likelihood estimators can be analyzed for this class of time series models and we prove that these estimators are consistent and asymptotically normally distributed regardless of the true data generating process. We illustrate our results using classical examples of quasi-maximum likelihood estimation including standard GARCH models, duration models, Poisson type autoregressions and ARMA models with GARCH errors. Our contribution unifies the existing theory and gives conditions for proving consistency and asymptotic normality in a variety of situations.

MSC:
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
60J05 Discrete-time Markov processes on general state spaces
62M05 Markov processes: estimation; hidden Markov models
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