Non-linear DSGE models and the optimized central difference particle filter
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.
Year of publication: |
2011
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Authors: | Andreasen, Martin M. |
Published in: |
Journal of Economic Dynamics and Control. - Elsevier, ISSN 0165-1889. - Vol. 35.2011, 10, p. 1671-1695
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Publisher: |
Elsevier |
Keywords: | Likelihood inference Non-linear DSGE models Non-normal shocks Particle filtering |
Saved in:
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