 Maximum likelihood estimation of time series models: the Kalman filter and beyondAlessandra Luati and Tommaso Proietti No 2012_02, Working Papers from University of Sydney Business School, Discipline of Business Analytics Abstract: The 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. Keywords: non linear models; state space models; missing data (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:syb:wpbsba:2123/8337 Access Statistics for this paper More papers in Working Papers from University of Sydney Business School, Discipline of Business Analytics Contact information at EDIRC. |
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