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Solution Methods for DSGE models

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This RePEc Biblio topic is edited by Christian Zimmermann. It was first published on 2012-11-29 21:13:35 and last updated on 2012-11-29 22:42:59.

Introduction by the editor

While solving represeantative agents models is now relatively simple with methods based on some sort of linearization, models with heterogeneous agents, or models that involve non-lienarities are much more involved, and may require a lot of computing power. This list provides some introduction into the methods that are available.

Most relevant link for this topic

http://dge.repec.org/codes.html

Most relevant JEL codes

• C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
• C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

Most relevant NEP reports

NEP reports are email or RSS notifications about new research in selected fields. Subscriptions are free.

NEP-DGE (Dynamic General Equilibrium)

Most relevant research

1. King, Robert G & Plosser, Charles I & Rebelo, Sergio T, 2002. "Production, Growth and Business Cycles: Technical Appendix," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 87-116, October.
   • Morten Ravn, . "King-Plosser-Rebelo GAUSS programmes," QM&RBC Codes 104, Quantitative Macroeconomics & Real Business Cycles.
2. Blanchard, Olivier Jean & Kahn, Charles M, 1980. "The Solution of Linear Difference Models under Rational Expectations," Econometrica, Econometric Society, vol. 48(5), pages 1305-1311, July.
3. Taylor, John B & Uhlig, Harald, 1990. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 1-17, January.
   • John B. Taylor & Harald Uhlig, 1989. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," NBER Working Papers 3117, National Bureau of Economic Research, Inc.
4. Harald Uhlig, 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper / Institute for Empirical Macroeconomics 101, Federal Reserve Bank of Minneapolis.
   • Uhlig, H.F.H.V.S., 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper 1995-97, Tilburg University, Center for Economic Research.
   • Harald Uhlig, 1998. "A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily," QM&RBC Codes 123, Quantitative Macroeconomics & Real Business Cycles.
5. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
   • Christopher Sims, 2001. "Matlab Code for Solving Linear Rational Expectations Models," QM&RBC Codes 11, Quantitative Macroeconomics & Real Business Cycles.
6. Christiano, Lawrence J, 2002. "Solving Dynamic Equilibrium Models by a Method of Undetermined Coefficients," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 21-55, October.
   • Lawrence J. Christiano, 1998. "Solving dynamic equilibrium models by a method of undetermined coefficients," Working Paper 9804, Federal Reserve Bank of Cleveland.
   • Lawrence J. Christiano, 1998. "Solving Dynamic Equilibrium Models by a Method of Undetermined Coefficients," NBER Technical Working Papers 0225, National Bureau of Economic Research, Inc.
7. Wouter J. Den Haan & Albert Marcet, 1994. "Accuracy in Simulations," Review of Economic Studies, Oxford University Press, vol. 61(1), pages 3-17.
   • Wouter J. den Haan & Albert Marcet, 1993. "Accuracy in simulations," Economics Working Papers 42, Department of Economics and Business, Universitat Pompeu Fabra.
8. Orazio P. Attanasio & Hamish Low, 2004. "Estimating Euler Equations," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 7(2), pages 405-435, April.
   • Orazio Attanasio & Hamish Low, 2002. "Estimating Euler equations," IFS Working Papers W02/06, Institute for Fiscal Studies.
   • Orazio P. Attanasio & Hamish Low, 2000. "Estimating Euler Equations," NBER Technical Working Papers 0253, National Bureau of Economic Research, Inc.
9. Sydney Ludvigson & Christina H. Paxson, 2001. "Approximation Bias In Linearized Euler Equations," The Review of Economics and Statistics, MIT Press, vol. 83(2), pages 242-256, May.
   • Sydney Ludvigson & Christina H. Paxson, 1997. "Approximation bias in linearized Euler equations," Research Paper 9712, Federal Reserve Bank of New York.
   • Sydney Ludvigson & Christina H. Paxson, 1999. "Approximation Bias in Linearized Euler Equations," NBER Technical Working Papers 0236, National Bureau of Economic Research, Inc.
10. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    • Sungbae An & Frank Schorfheide, 2006. "Bayesian analysis of DSGE models," Working Papers 06-5, Federal Reserve Bank of Philadelphia.
    • An, Sungbae & Schorfheide, Frank, 2005. "Bayesian Analysis of DSGE Models," CEPR Discussion Papers 5207, C.E.P.R. Discussion Papers.

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