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Biometrika 2002 89(3):603-616; doi:10.1093/biomet/89.3.603
© 2002 by Biometrika Trust
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A simple and efficient simulation smoother for state space time series analysis

J.Durbin1 and S.J. Koopman2

1 Department of Statistics, London School of Economics and Political Science, London WC2A 2AE, U.Kdurbinja{at}aol.com 2 Department of Econometrics, Free University Amsterdam, NL-1081 HV Amsterdam, The Netherlands s.j.koopman{at}feweb.vu.nl

A simulation smoother in state space time series analysis is a procedure for drawing samples from the conditional distribution of state or disturbance vectors given the observations.We present a new technique for this which is both simple and computationally efficient. The treatment includes models with diffuse initial conditions and regression effects. Computational comparisons are made with the previous standard method. Two applications are provided to illustrate the use of the simulation smoother for Gibbs sampling for Bayesian inference and importance sampling for classical inference.

Key Words: Diffuse initialisation; Disturbance smoothing; Gibbs sampling; Importance sampling; Kalman filter; Markov chain Monte Carlo

Received January 2001. Revised April 2002


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