Solution Methods for DSGE models
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This RePEc Biblio topic is edited by Christian Zimmermann (pzi1). It was first published on 2012-11-29 21:13:35 and last updated on 2012-11-30 04:42:59.
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.htmlMost 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
- King, Robert G & Plosser, Charles I & Rebelo, Sergio T, 2002, "Production, Growth and Business Cycles: Technical Appendix," Computational Economics, Springer;Society for Computational Economics, volume 20, issue 1-2, pages 87-116, October.
- Morten Ravn, undated, "King-Plosser-Rebelo GAUSS programmes," QM&RBC Codes, Quantitative Macroeconomics & Real Business Cycles, number 104, revised .
- Blanchard, Olivier Jean & Kahn, Charles M, 1980, "The Solution of Linear Difference Models under Rational Expectations," Econometrica, Econometric Society, volume 48, issue 5, pages 1305-1311, July.
- 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, volume 8, issue 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, National Bureau of Economic Research, Inc, number 3117, Sep.
- Harald Uhlig, 1995, "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper / Institute for Empirical Macroeconomics, Federal Reserve Bank of Minneapolis, number 101, DOI: 10.21034/dp.101.
- Uhlig, H.F.H.V.S., 1995, "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper, Tilburg University, Center for Economic Research, number 1995-97.
- Harald Uhlig, 1998, "A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily," QM&RBC Codes, Quantitative Macroeconomics & Real Business Cycles, number 123, revised .
- Sims, Christopher A, 2002, "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, volume 20, issue 1-2, pages 1-20, October.
- Christopher Sims, 2001, "Matlab Code for Solving Linear Rational Expectations Models," QM&RBC Codes, Quantitative Macroeconomics & Real Business Cycles, number 11, revised .
- Christiano, Lawrence J, 2002, "Solving Dynamic Equilibrium Models by a Method of Undetermined Coefficients," Computational Economics, Springer;Society for Computational Economics, volume 20, issue 1-2, pages 21-55, October.
- Lawrence J. Christiano, 1998, "Solving dynamic equilibrium models by a method of undetermined coefficients," Working Papers (Old Series), Federal Reserve Bank of Cleveland, number 9804, DOI: 10.26509/frbc-wp-199804.
- Lawrence J. Christiano, 1998, "Solving Dynamic Equilibrium Models by a Method of Undetermined Coefficients," NBER Technical Working Papers, National Bureau of Economic Research, Inc, number 0225, Feb.
- Wouter J. Den Haan & Albert Marcet, 1994, "Accuracy in Simulations," The Review of Economic Studies, Review of Economic Studies Ltd, volume 61, issue 1, pages 3-17.
- Wouter J. den Haan & Albert Marcet, 1993, "Accuracy in simulations," Economics Working Papers, Department of Economics and Business, Universitat Pompeu Fabra, number 42, Sep.
- Orazio P. Attanasio & Hamish Low, 2004, "Estimating Euler Equations," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, volume 7, issue 2, pages 405-435, April, DOI: 10.1016/j.red.2003.09.003.
- Orazio Attanasio & Hamish Low, 2002, "Estimating Euler equations," IFS Working Papers, Institute for Fiscal Studies, number W02/06, Mar.
- Orazio P. Attanasio & Hamish Low, 2000, "Estimating Euler Equations," NBER Technical Working Papers, National Bureau of Economic Research, Inc, number 0253, May.
- Sydney Ludvigson & Christina H. Paxson, 2001, "Approximation Bias In Linearized Euler Equations," The Review of Economics and Statistics, MIT Press, volume 83, issue 2, pages 242-256, May.
- Sydney C. Ludvigson & Christina H. Paxson, 1997, "Approximation bias in linearized Euler equations," Research Paper, Federal Reserve Bank of New York, number 9712.
- Sydney Ludvigson & Christina H. Paxson, 1999, "Approximation Bias in Linearized Euler Equations," NBER Technical Working Papers, National Bureau of Economic Research, Inc, number 0236, Mar.
- Sungbae An & Frank Schorfheide, 2007, "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, volume 26, issue 2-4, pages 113-172, DOI: 10.1080/07474930701220071.
- Schorfheide, Frank & An, Sungbae, 2005, "Bayesian Analysis of DSGE Models," CEPR Discussion Papers, Centre for Economic Policy Research, number 5207, Sep.
- Sungbae An & Frank Schorfheide, 2006, "Bayesian analysis of DSGE models," Working Papers, Federal Reserve Bank of Philadelphia, number 06-5.