Approximate Solutions to Dynamic Models - Linear Methods
Linear Methods are often used to compute approximate solutions to dynamic models, as these models often cannot be solved analytically. Linear methods are very popular, as they can easily be implemented. Also, they provide a useful starting point for understanding more elaborate numerical methods. It shall be described here first for the example of a simple real business cycle model, including how to easily generate the log-linearized equations needed before solving the linear system. For a general framework, formulas are provided for calculating the recursive law of motion. The algorithm described here is implemented with the "toolkit" programs available per
|Date of creation:||Apr 2006|
|Contact details of provider:|| Postal: Spandauer Str. 1,10178 Berlin|
Web page: http://sfb649.wiwi.hu-berlin.de
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- Kydland, Finn E & Prescott, Edward C, 1982.
"Time to Build and Aggregate Fluctuations,"
Econometric Society, vol. 50(6), pages 1345-1370, November.
- Finn E. Kydland & Edward C. Prescott, 1982. "Web interface for "Time to Build and Aggregate Fluctuations"," QM&RBC Codes 4a, Quantitative Macroeconomics & Real Business Cycles.
- Finn E. Kydland & Edward C. Prescott, 1982. "Executable program for "Time to Build and Aggregate Fluctuations"," QM&RBC Codes 4, Quantitative Macroeconomics & Real Business Cycles.
- 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.
- Roger E. A. Farmer, 1999. "Macroeconomics of Self-fulfilling Prophecies, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262062038.
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"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.
- Binder, M. & Pesaran, H., 1996.
"Multivariate Linear Rational Expectations Models: Characterisation of the Nature of the Solutions and Their Fully Recursive Computation,"
Cambridge Working Papers in Economics
9619, Faculty of Economics, University of Cambridge.
- Michael Binder & M. Hashem Pesaran, 1997. "GAUSS and Matlab codes for Multivariate Linear Rational Expectations Models: Characterization of the Nature of the Solutions and Their Fully Recursive Computation," QM&RBC Codes 73, Quantitative Macroeconomics & Real Business Cycles.
- King, Robert G & Plosser, Charles I & Rebelo, Sergio T, 2002.
"Production, Growth and Business Cycles: Technical Appendix,"
Springer;Society for Computational Economics, vol. 20(1-2), pages 87-116, October.
- Sims, Christopher A, 2002.
"Solving Linear Rational Expectations Models,"
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.
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