Fast Efficient Importance Sampling by State Space Methods

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

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Year of publication:	2014
Authors:	Koopman, Siem Jan
Other Persons:	Lit, Rutger (contributor) ; Nguyen, Thuy Minh (contributor)
Publisher:	[2014]: [S.l.] : SSRN
Subject:	Theorie \| Theory \| Zustandsraummodell \| State space model \| Stichprobenerhebung \| Sampling \| Maximum-Likelihood-Schätzung \| Maximum likelihood estimation

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