Non-linear DSGE Models and The Optimized Central Difference Particle Filter

45 Pages Posted: 28 Jan 2010 Last revised: 15 Dec 2010

See all articles by Martin M. Andreasen

Martin M. Andreasen

Aarhus University; CREATES, Aarhus University

Date Written: October 30, 2010

Abstract

This paper improves the accuracy and speed of particle filtering for non-linear DSGE models with potentially non-normal shocks. This is done by introducing a new proposal distribution which i) incorporates information from new observables and ii) has a small optimization step that minimizes the distance to the optimal proposal distribution. A particle filter with this proposal distribution is shown to deliver a high level of accuracy even with relatively few particles, and this filter is therefore much more efficient than the standard particle filter.

Keywords: Likelihood inference, Non-linear DSGE models, Non-normal shocks, Particle filtering

JEL Classification: C13, C15, E1, E32

Suggested Citation

Andreasen, Martin M., Non-linear DSGE Models and The Optimized Central Difference Particle Filter (October 30, 2010). Available at SSRN: https://ssrn.com/abstract=1543743 or http://dx.doi.org/10.2139/ssrn.1543743

Martin M. Andreasen (Contact Author)

Aarhus University ( email )

Aarhus
Denmark

CREATES, Aarhus University ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

HOME PAGE: http://econ.au.dk/research/research-centres/creates/people/junior-fellows/martin-andreasen/

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