Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/27737 
Year of Publication: 
2009
Series/Report no.: 
Economics Working Paper No. 2009-02
Publisher: 
Kiel University, Department of Economics, Kiel
Abstract: 
We develop a numerical procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-Gaussian state-space models. The procedure approximates necessary integrals using continuous approximations of target densities. Construction is achieved via efficient importance sampling, and approximating densities are adapted to fully incorporate current information. We illustrate our procedure in applications to dynamic stochastic general equilibrium models.
Subjects: 
particle filter
adaption
efficient importance sampling
kernel density approximation
dynamic stochastic general equilibrium model
Document Type: 
Working Paper

Files in This Item:
File
Size
443.12 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.