Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/27737
Authors: 
DeJong, David Neil
Dharmarajan, Hariharan
Liesenfeld, Roman
Moura, Guilherme V.
Richard, Jean-François
Year of Publication: 
2009
Series/Report no.: 
Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2009,02
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

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