Non-linear DSGE Models and The Optimized Central Difference Particle Filter
45 Pages Posted: 28 Jan 2010 Last revised: 15 Dec 2010
Date Written: October 30, 2010
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
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