Non-linear DSGE models and the optimized central difference particle filter

Martin Andreasen

Journal of Economic Dynamics and Control, 2011, vol. 35, issue 10, 1671-1695

Abstract: We improve 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 it 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)
Date: 2011
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Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:35:y:2011:i:10:p:1671-1695

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