Statistical Modeling of Integervalued Time Series: An Introduction
20/04/2016 Wednesday 20th April 2016, 16:00 (Room P3.10, Mathematics Building)
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Manuel Scotto, CEMAT and Instituto Superior Técnico, Universidade de Lisboa
Modeling and predicting the temporal dependence and evolution of low integervalued time series have attracted a lot of attention over the last years. This is partially due to the increasing availability of relevant highquality data sets in various fields of applications ranging from finance and economy to medicine and ecology. It is important to stress, however, that there is no a unifying approach applicable to modeling all integervalued time series and, consequently, the analysis of such time series has to be restricted to special classes of integervalued models. A useful division of these models can be made as being either observationdriven or parameterdriven models. A suitable class of observationdriven models is the one including models based on thinning operators. Models belonging to this class are obtained by replacing the multiplication in the conventional time series models by an appropriate thinning operator, along with considering a discrete distribution for the sequence of innovations in order to preserve the discreteness of the counts. This talk aims at providing an overview of recent developments in thinningbased time series models paying particular attention to models obtained as discrete counterparts of conventional univariate and multivariate autoregressive moving average models, with either finite or infinite support. Finally, we also outline and discuss likely directions of future research.
