The simulation smoother for time series models

Piet De Jong*, Neil Shephard

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

285 Citations (Scopus)


Recently suggested procedures for simulating from the posterior density of states given a Gaussian state space time series are refined and extended. We introduce and study the simulation smoother, which draws from the multivariate posterior distribution of the disturbances of the model, so avoiding the degeneracies inherent in state samplers. The technique is important in Gibbs sampling with non-Gaussian time series models, and for performing Bayesian analysis of Gaussian time series.

Original languageEnglish
Pages (from-to)339-350
Number of pages12
Issue number2
Publication statusPublished - Jun 1995
Externally publishedYes


  • Gibbs sampling
  • Kalman filter
  • Simulation smoother
  • Smoothing
  • State space model


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