Non-Linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter

63 Pages Posted: 27 May 2008 Last revised: 17 Jun 2008

See all articles by Martin M. Andreasen

Martin M. Andreasen

Aarhus University; CREATES, Aarhus University

Multiple version iconThere are 2 versions of this paper

Date Written: June 17, 2008

Abstract

This paper shows how non-linear DSGE models with potential non-normal shocks can be estimated by Quasi-Maximum Likelihood based on the Central Difference Kalman Filter (CDKF). The advantage of this estimator is that evaluating the quasi log-likelihood function only takes a fraction of a second. The second contribution of this paper is to derive a new particle filter which we term the Mean Shifted Particle Filter (MSPF). We show that the MSPF outperforms the standard Particle Filter by delivering more precise state estimates, and in general the MSPF has lower Monte Carlo variation in the reported log-likelihood function.

Keywords: Multivariate Stirling interpolation, Particle Filtering, Non-linear DSGE models, Non-normal shocks, Quasi-Maximum Likelihood

JEL Classification: C13, C15, E10, E32

Suggested Citation

Andreasen, Martin M. and Andreasen, Martin M., Non-Linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter (June 17, 2008). Available at SSRN: https://ssrn.com/abstract=1137619 or http://dx.doi.org/10.2139/ssrn.1137619

Martin M. Andreasen (Contact Author)

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/

Aarhus University ( email )

Aarhus
Denmark

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