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How to Maximize the Likelihood Function for a DSGE Model

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  • Martin Andreasen

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Abstract

This paper extends two optimization routines to deal with objective functions for DSGE models. The optimization routines are i) a version of Simulated Annealing developed by Corana, Marchesi & Ridella (1987), and ii) the evolutionary algorithm CMA-ES developed by Hansen, Müller & Koumoutsakos (2003). Following these extensions, we examine the ability of the two routines to maximize the likelihood function for a sequence of test economies. Our results show that the CMA- ES routine clearly outperforms Simulated Annealing in its ability to find the global optimum and in efficiency. With 10 unknown structural parameters in the likelihood function, the CMA-ES routine finds the global optimum in 95% of our test economies compared to 89% for Simulated Annealing. When the number of unknown structural parameters in the likelihood function increases to 20 and 35, then the CMA-ES routine finds the global optimum in 85% and 71% of our test economies, respectively. The corresponding numbers for Simulated Annealing are 70% and 0%.

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File URL: http://hdl.handle.net/10.1007/s10614-009-9182-6
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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 35 (2010)
Issue (Month): 2 (February)
Pages: 127-154

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Handle: RePEc:kap:compec:v:35:y:2010:i:2:p:127-154

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Web page: http://www.springerlink.com/link.asp?id=100248
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Keywords: CMA-ES optimization routine; Multimodel objective function; Nelder–Mead simplex routine; Non-convex search space; Resampling; Simulated Annealing; C61; C88; E30;

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References

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  1. Frank Smets & Raf Wouters, 2007. "Shocks and Frictions in US Business Cycles : a Bayesian DSGE Approach," Working Paper Research 109, National Bank of Belgium.
  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. Altig, David & Christiano, Lawrence & Eichenbaum, Martin & Lindé, Jesper, 2004. "Firm-Specific Capital, Nominal Rigidities and the Business Cycle," Working Paper Series 176, Sveriges Riksbank (Central Bank of Sweden).
  4. Alejandro Justiniano & Giorgio E. Primiceri, 2006. "The Time Varying Volatility of Macroeconomic Fluctuations," NBER Working Papers 12022, National Bureau of Economic Research, Inc.
  5. Lawrence J. Christiano & Martin Eichenbaum & Charles Evans, 2001. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," NBER Working Papers 8403, National Bureau of Economic Research, Inc.
  6. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
  7. 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.
  8. Sungbae An & Frank Schorfheide, 2006. "Bayesian analysis of DSGE models," Working Papers 06-5, Federal Reserve Bank of Philadelphia.
  9. 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.
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Citations

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Cited by:
  1. Benjamin Born & Johannes Pfeifer, 2011. "Policy Risk and the Business Cycle," Bonn Econ Discussion Papers bgse06_2011, University of Bonn, Germany.
  2. Burgess, Stephen & Fernandez-Corugedo, Emilio & Groth, Charlotta & Harrison, Richard & Monti, Francesca & Theodoridis, Konstantinos & Waldron, Matt, 2013. "The Bank of England's forecasting platform: COMPASS, MAPS, EASE and the suite of models," Bank of England working papers 471, Bank of England.
  3. Martin Møller Andreasen, 2008. "Explaining Macroeconomic and Term Structure Dynamics Jointly in a Non-linear DSGE Model," CREATES Research Papers 2008-43, School of Economics and Management, University of Aarhus.
  4. Martin Møller Andreasen, 2008. "Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter," CREATES Research Papers 2008-33, School of Economics and Management, University of Aarhus.
  5. Dario Caldara & Richard Harrison & Anna Lipinska, 2012. "Practical tools for policy analysis in DSGE models with missing channels," Finance and Economics Discussion Series 2012-72, Board of Governors of the Federal Reserve System (U.S.).
  6. Liran Einav & Amy Finkelstein & Paul Schrimpf, 2013. "The Response of Drug Expenditures to Non-Linear Contract Design: Evidence from Medicare Part D," NBER Working Papers 19393, National Bureau of Economic Research, Inc.
  7. Boris Blagov, 2013. "Financial crises and time- varying risk premia in a small open economy: a Markov-Switching DSGE model for Estonia," Bank of Estonia Working Papers wp2013-8, Bank of Estonia, revised 09 Dec 2013.
  8. Blagov , Boris & Funke, Michael, 2013. "The regime-dependent evolution of credibility: A fresh look at Hong Kong’s linked exchange rate system," BOFIT Discussion Papers 24/2013, Bank of Finland, Institute for Economies in Transition.
  9. Tae Bong Kim, 2013. "Monetary Policy in Korea through the lense of Taylor Rule in DSGE model," 2013 Meeting Papers 746, Society for Economic Dynamics.
  10. Solomon, Bernard Daniel, 2010. "Firm leverage, household leverage and the business cycle," MPRA Paper 26504, University Library of Munich, Germany.
  11. Andreasen, Martin, 2011. "An estimated DSGE model: explaining variation in term premia," Bank of England working papers 441, Bank of England.

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