Disturbance smoother for state space models

Siem Jan Koopman*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


SUMMARY: This paper develops a method to evaluate the smoothed estimator of the disturbance vector in a state space model together with its mean squared error matrix. This disturbance smoother also leads to an efficient smoother for the state vector. Applications include a method to calculate auxiliary residuals for unobserved components time series models and an EM algorithm for estimating covariance parameters in a state space model.

Original languageEnglish
Pages (from-to)117-126
Number of pages10
Issue number1
Publication statusPublished - 1 Mar 1993
Externally publishedYes


  • Disturbance smoother
  • EM algorithm
  • Kalman filter
  • Residual
  • State smoother
  • State space model
  • Unobserved components time series model


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