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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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  1. David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Guilherme Moura & Jean-Francois Richard, 2007. "Efficient Likelihood Evaluation in State-Space Representations," Working Papers 374, University of Pittsburgh, Department of Economics, revised Dec 2008.
  2. Doh, Taeyoung, 2011. "Yield curve in an estimated nonlinear macro model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(8), pages 1229-1244, August.
  3. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor and Francis Journals, vol. 26(2-4), pages 211-219.
  4. Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342.
  5. Smets, Frank & Wouters, Rafael, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," CEPR Discussion Papers 6112, C.E.P.R. Discussion Papers.
  6. Richard, Jean-Francois & Zhang, Wei, 2007. "Efficient high-dimensional importance sampling," Journal of Econometrics, Elsevier, vol. 141(2), pages 1385-1411, December.
  7. Gianni Amisano & Oreste Tristani, 2007. "Euro area inflation persistence in an estimated nonlinear DSGE model," Working Paper Series 754, European Central Bank.
  8. Stephanie Schmitt-Grohe & Martin Uribe, 2002. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," NBER Technical Working Papers 0282, National Bureau of Economic Research, Inc.
  9. Ingvar Strid, 2006. "Parallel particle filters for likelihood evaluation in DSGE models: An assessment," Computing in Economics and Finance 2006 395, Society for Computational Economics.
  10. An, Sungbae & Schorfheide, Frank, 2005. "Bayesian Analysis of DSGE Models," CEPR Discussion Papers 5207, C.E.P.R. Discussion Papers.
  11. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2001. "Nominal rigidities and the dynamic effects of a shock to monetary policy," Working Paper Series WP-01-08, Federal Reserve Bank of Chicago.
  12. Martin M. Andreasen, 2010. "Non-linear DSGE Models and The Central Difference Kalman Filter," CREATES Research Papers 2010-30, School of Economics and Management, University of Aarhus.
  13. De Graeve, Ferre & Emiris, Marina & Wouters, Raf, 2009. "A structural decomposition of the US yield curve," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 545-559, May.
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