Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86445
Authors: 
Jungbacker, Borus
Koopman, Siem Jan
Year of Publication: 
2005
Series/Report no.: 
Tinbergen Institute Discussion Paper 05-117/4
Abstract: 
We consider likelihood inference and state estimation by means of importance sampling for state space models with a nonlinear non-Gaussian observation y ~ p(y lpha) and a linear Gaussian state alpha ~ p(alpha). The importance density is chosen to be the Laplace approximation of the smoothing density p(alpha). We show that computationally efficient state space methods can be used to perform all necessary computations in all situations. It requires new derivations of the Kalman filter and smoother and the simulation smoother which do not rely on a linear Gaussian observation equation. Furthermore, results are presented that lead to a more effective implementation of importance sampling for state space models. An illustration is given for the stochastic volatility model with leverage.
Subjects: 
Kalman filter
Likelihood function
Monte Carlo integration
Newton-Raphson
Posterior mode estimation
Simulation smoothing
Stochastic volatility model
JEL: 
C15
C32
Document Type: 
Working Paper

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