Tid: 4 maj 2014 kl 15.15-16.00.

Föredragshållare: Randall Douc

Titel: Asymptotic properties of Quasi-Maximum Likelihood Estimators in Observation-Driven Time Series models

Abstract We study a general class of quasi-maximum likelihood estimators for observation-driven time series models. Such models can be found in diverse applications including finance, medicine and several other scientific fields. Our focus is on models related to the exponential family of distributions like Poisson based mod- els for count time series or duration models. However the proposed approach is more general and covers a variety of time series models including the ordi- nary 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 normal 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 variety of situations.

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