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

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  • Andreasen, Martin M.

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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 35 (2011)
Issue (Month): 10 (October)
Pages: 1671-1695

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Handle: RePEc:eee:dyncon:v:35:y:2011:i:10:p:1671-1695

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Web page: http://www.elsevier.com/locate/jedc

Related research

Keywords: Likelihood inference Non-linear DSGE models Non-normal shocks Particle filtering;

References

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Cited by:
  1. Hall, Jamie & Pitt, Michael K. & Kohn, Robert, 2014. "Bayesian inference for nonlinear structural time series models," Journal of Econometrics, Elsevier, vol. 179(2), pages 99-111.

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