Non-linear DSGE Models and The Optimized Particle Filter

Martin Andreasen

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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 elatively 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 (search for similar items in EconPapers)
JEL-codes: C13 C15 E10 E32 (search for similar items in EconPapers)
Pages: 41
Date: 2010-01-27
New Economics Papers: this item is included in nep-cba, nep-dge, nep-ecm and nep-ets
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2010-05

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