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A New Approach to Drawing States in State Space Models

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Author Info

  • McCAUSLAND, William J.
  • MILLER, Shirley
  • PELLETIER, Denis

Abstract

We introduce a new method for drawing state variables in Gaussian state space models from their conditional distribution given parameters and observations. Unlike standard methods, our method does not involve Kalman filtering. We show that for some important cases, our method is computationally more efficient than standard methods in the literature. We consider two applications of our method.

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File URL: http://www.cireqmontreal.com/wp-content/uploads/cahiers/07-2007-cah.pdf
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Bibliographic Info

Paper provided by Centre interuniversitaire de recherche en économie quantitative, CIREQ in its series Cahiers de recherche with number 07-2007.

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Length: 26 pages
Date of creation: 2007
Date of revision:
Handle: RePEc:mtl:montec:07-2007

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Related research

Keywords: State space models; Stochastic volatility; Count data;

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References

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  1. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1996. "Stochastic Volatility: Likelihood Inference And Comparison With Arch Models," Econometrics 9610002, EconWPA.
  2. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
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Cited by:
  1. McCAUSLAND, William, 2008. "The Hessian Method (Highly Efficient State Smoothing, In a Nutshell)," Cahiers de recherche 03-2008, Centre interuniversitaire de recherche en économie quantitative, CIREQ.

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