Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/22041 
Year of Publication: 
2007
Series/Report no.: 
Economics Working Paper No. 2007-25
Publisher: 
Kiel University, Department of Economics, Kiel
Abstract: 
We develop a numerical filtering procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-gaussian state-space models. The procedure approximates necessary integrals using continuous or piecewise-continuous approximations of target densities. Construction is achieved via efficient importance sampling, and approximating densities are adapted to fully incorporate current information.
Subjects: 
particle filter
adaption
efficient importance sampling
kernel density approximation
Document Type: 
Working Paper

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