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dc.contributor.authorProietti, Tommaso
dc.contributor.authorLuati, Alessandra
dc.date.accessioned2012-05-09
dc.date.available2012-05-09
dc.date.issued2012-05-01
dc.identifier.urihttp://hdl.handle.net/2123/8337
dc.description.abstractThe purpose of this chapter is to provide a comprehensive treatment of likelihood inference for state space models. These are a class of time series models relating an observable time series to quantities called states, which are characterized by a simple temporal dependence structure, typically a first order Markov process. The states have sometimes substantial interpretation. Key estimation problems in economics concern latent variables, such as the output gap, potential output, the non-accelerating-inflation rate of unemployment, or NAIRU, core inflation, and so forth. Time-varying volatility, which is quintessential to finance, is an important feature also in macroeconomics. In the multivariate framework relevant features can be common to different series, meaning that the driving forces of a particular feature and/or the transmission mechanism are the same. The objective of this chapter is reviewing this algorithm and discussing maximum likelihood inference, starting from the linear Gaussian case and discussing the extensions to a nonlinear and non Gaussian framework.en_AU
dc.language.isoen_AUen_AU
dc.publisherBusiness Analytics.en_AU
dc.relation.ispartofseriesBAWP-2012-02en_AU
dc.subjectstate space modelsen_AU
dc.subjectmissing dataen_AU
dc.subjectnon linear modelsen_AU
dc.titleMaximum likelihood estimation of time series models: the Kalman filter and beyonden_AU
dc.typeWorking Paperen_AU
dc.contributor.departmentBusiness Analyticsen_AU


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