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Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter

  • Martin Møller Andreasen

    ()

    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

This paper shows how non-linear DSGE models with potential non-normal shocks can be estimated by Quasi-Maximum Likelihood based on the Central Difference Kalman Filter (CDKF). The advantage of this estimator is that evaluating the quasi log-likelihood function only takes a fraction of a second. The second contribution of this paper is to derive a new particle filter which we term the Mean Shifted Particle Filter (MSPFb). We show that the MSPFb outperforms the standard Particle Filter by delivering more precise state estimates, and in general the MSPFb has lower Monte Carlo variation in the reported log-likelihood function.

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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2008-33.

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Length: 45
Date of creation: 20 Jun 2008
Date of revision:
Handle: RePEc:aah:create:2008-33
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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  1. 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.
  2. Alejandro Justiniano & Giorgio E. Primiceri, 2006. "The Time Varying Volatility of Macroeconomic Fluctuations," NBER Working Papers 12022, National Bureau of Economic Research, Inc.
  3. Duffie, Darrell & Singleton, Kenneth J, 1993. "Simulated Moments Estimation of Markov Models of Asset Prices," Econometrica, Econometric Society, vol. 61(4), pages 929-52, July.
  4. Seonghoon Cho & Antonio Moreno & Geert Bekaert, 2005. "New-Keynesian Macroeconomics and the Term Structure," Faculty Working Papers 04/05, School of Economics and Business Administration, University of Navarra.
  5. Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez & Manuel Santos, 2005. "Convergence Properties of the Likelihood of Computed Dynamic Models," Levine's Bibliography 122247000000000822, UCLA Department of Economics.
  6. Martin Møller Andreasen, 2008. "How to Maximize the Likelihood Function for a DSGE Model," CREATES Research Papers 2008-32, School of Economics and Management, University of Aarhus.
  7. Altig, David E & Christiano, Lawrence J. & Eichenbaum, Martin & Lindé, Jesper, 2005. "Firm-Specific Capital, Nominal Rigidities and the Business Cycle," CEPR Discussion Papers 4858, C.E.P.R. Discussion Papers.
  8. Martin Møller Andreasen, 2008. "Ensuring the Validity of the Micro Foundation in DSGE Models," CREATES Research Papers 2008-26, School of Economics and Management, University of Aarhus.
  9. 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.
  10. Stephanie Schmitt-Grohe & Martin Uribe, 2001. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," Departmental Working Papers 200106, Rutgers University, Department of Economics.
  11. An, Sungbae & Schorfheide, Frank, 2005. "Bayesian Analysis of DSGE Models," CEPR Discussion Papers 5207, C.E.P.R. Discussion Papers.
  12. Jinill Kim & Sunghyun Kim & Ernst Schaumburg & Christopher A. Sims, 2003. "Calculating and Using Second Order Accurate Solutions of Discrete Time," Levine's Bibliography 666156000000000284, UCLA Department of Economics.
  13. 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.
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