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A New Approach to Drawing States in State Space Models

  • William J. McCausland

    ()

    (Département de sciences économiques, Université de Montréal)

  • Shirley Miller

    ()

    (Département de sciences économiques, Université de Montréal)

  • Denis Pelletier

    ()

    (Department of Economics, North Carolina State University)

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: ftp://ftp.ncsu.edu/pub/ncsu/economics/RePEc/pdf/MMP.pdf
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Paper provided by North Carolina State University, Department of Economics in its series Working Paper Series with number 014.

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Length: 27 pages
Date of creation: Aug 2007
Date of revision: Aug 2007
Handle: RePEc:ncs:wpaper:014
Note: First draft 2007-08
Contact details of provider: Phone: (919) 515-3274
Web page: http://www.mgt.ncsu.edu/faculty/economics.html
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  1. Sangjoon Kim, Neil Shephard & Siddhartha Chib, . "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers W26, revised version of W, Economics Group, Nuffield College, University of Oxford.
  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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