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Non-linear DSGE Models and The Optimized Particle Filter

  • 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 School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2010-05.

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

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  1. David Altig & Lawrence Christiano & Martin Eichenbaum & Jesper Linde, 2005. "Firm-Specific Capital, Nominal Rigidities and the Business Cycle," NBER Working Papers 11034, National Bureau of Economic Research, Inc.
  2. Lawrence J. Christiano & Martin Eichenbaum & Charles Evans, 2001. "Nominal rigidities and the dynamic effects of a shock to monetary policy," Proceedings, Federal Reserve Bank of San Francisco, issue Jun.
  3. 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.
  4. 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.
  5. Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez, 2006. "Estimating Macroeconomic Models: A Likelihood Approach," Levine's Bibliography 122247000000000849, UCLA Department of Economics.
  6. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
  7. 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.
  8. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
  9. Jesus Fernandez-Villaverde & Juan F. Rubio-Ramirez, 2004. "Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood," PIER Working Paper Archive 04-005, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  10. Christopher A. Sims & Jinill Kim & Sunghyun Kim, 2003. "Calculating and Using Second Order Accurate Solution of Discrete Time Dynamic Equilibrium Models," Computing in Economics and Finance 2003 162, Society for Computational Economics.
  11. Schmitt-Grohé, Stephanie & Uribe, Martín, 2001. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," CEPR Discussion Papers 2963, C.E.P.R. Discussion Papers.
  12. Amisano, Gianni & Tristani, Oreste, 2007. "Euro area inflation persistence in an estimated nonlinear DSGE model," Working Paper Series 0754, European Central Bank.
  13. Thomas Flury & Neil Shephard, 2008. "Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models," OFRC Working Papers Series 2008fe32, Oxford Financial Research Centre.
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