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

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Authors Martin M. Andreasen
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Paper 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.
Date of publication 2011
Code Programming Language MATLAB
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