State Space Models

Dirk P. Kroese and Joshua Chan
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Dirk P. Kroese: The University of Queensland, School of Mathematics and Physics

Chapter Chapter 11 in Statistical Modeling and Computation, 2014, pp 323-348 from Springer

Abstract: Abstract In this chapter we discuss versatile generalizations of the basic time series models in Sect. 10.1, collectively known under the name state space models. These models not only can capture the serial dependence of the observations (i.e., the dependence across time), but also can describe the persistence and volatility of the measurements. That is, they can model continued periods of high or low measurements and time-varying amounts of random fluctuation. In contrast, the AR(p) model, for example, cannot capture these features, as the model parameters do not depend on time. Throughout this chapter we shall use Bayesian notation when specifying (conditional) densities, even when working in a non-Bayesian setting.

Keywords: State Space Model; Transition Equation; Measurement Equation; Conditional Density; Stochastic Volatility Model (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-8775-3_11

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DOI: 10.1007/978-1-4614-8775-3_11

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