Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/87536
Authors: 
Koopman, Siem Jan
Nguyen, Thuy Minh
Year of Publication: 
2012
Series/Report no.: 
Tinbergen Institute Discussion Paper 12-008/4
Abstract: 
We show that efficient importance sampling for nonlinear non-Gaussian state space models can be implemented by computationally efficient Kalman filter and smoothing methods. The result provides some new insights but it primarily leads to a simple and fast method for efficient importance sampling. A simulation study and empirical illustration provide some evidence of the computational gains.
Subjects: 
Kalman filter
Monte Carlo maximum likelihood
Simulation smoothing
JEL: 
C32
C51
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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