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Non-linear DSGE Models and The Optimized Particle Filter

Listed author(s):
  • Martin M. Andreasen

    ()

    (Bank of England and CREATES)

This paper improves 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 elatively few particles, and this filter is therefore much more efficient than the standard particle filter.

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File URL: ftp://ftp.econ.au.dk/creates/rp/10/rp10_05.pdf
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Paper provided by Department of Economics and Business Economics, Aarhus University in its series CREATES Research Papers with number 2010-05.

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Length: 41
Date of creation: 27 Jan 2010
Handle: RePEc:aah:create:2010-05
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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  1. Gianni Amisano & Oreste Tristani, 2010. "Euro area inflation persistence in an estimated nonlinear dsge model," Post-Print hal-00732762, HAL.
  2. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2006. "Estimating Macroeconomic Models: A Likelihood Approach," NBER Technical Working Papers 0321, National Bureau of Economic Research, Inc.
  3. Jinill Kim & Sunghyun Henry Kim & Ernst Schaumburg & Christopher A. Sims, 2003. "Calculating and using second order accurate solutions of discrete time dynamic equilibrium models," Finance and Economics Discussion Series 2003-61, Board of Governors of the Federal Reserve System (U.S.).
  4. repec:oxf:wpaper:413 is not listed on IDEAS
  5. Godsill, Simon J. & Doucet, Arnaud & West, Mike, 2004. "Monte Carlo Smoothing for Nonlinear Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 156-168, January.
  6. David Altig & Lawrence Christiano & Martin Eichenbaum & Jesper Linde, 2011. "Firm-Specific Capital, Nominal Rigidities and the Business Cycle," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(2), pages 225-247, April.
  7. 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.
  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. Juan F. Rubio-Ramirez & Jesus Fernández-Villaverde, 2005. "Estimating dynamic equilibrium economies: linear versus nonlinear likelihood," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 891-910.
  13. Flury, Thomas & Shephard, Neil, 2011. "Bayesian Inference Based Only On Simulated Likelihood: Particle Filter Analysis Of Dynamic Economic Models," Econometric Theory, Cambridge University Press, vol. 27(05), pages 933-956, October.
  14. 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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