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State Space Models

In: Statistical Modeling and Computation

Author

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  • Dirk P. Kroese

    (The University of Queensland, School of Mathematics and Physics)

  • Joshua C. C. Chan

    (Australian National University, Department of Economics)

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.

Suggested Citation

  • Dirk P. Kroese & Joshua C. C. Chan, 2014. "State Space Models," Springer Books, in: Statistical Modeling and Computation, edition 127, chapter 0, pages 323-348, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-8775-3_11
    DOI: 10.1007/978-1-4614-8775-3_11
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