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Biometrika 1993 80(1):117-126; doi:10.1093/biomet/80.1.117
© 1993 by Biometrika Trust
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Disturbance smoother for state space models

SIEM JAN KOOPMAN

Department of Statistical and Mathematical Sciences, London School of Economics and Political Science Houghton Street, London WC2A 2AE, U.K.

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.

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


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B. Jungbacker and S. J. Koopman
Monte Carlo Estimation for Nonlinear Non-Gaussian State Space Models
Biometrika, December 1, 2007; 94(4): 827 - 839.
[Abstract] [PDF]


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