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|
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Web page: http://sfb649.wiwi.hu-berlin.de
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Society for Computational Economics, vol. 20(1-2), pages 87-116, October.
- Binder, M. & Pesaran, H., 1996.
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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.
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- Christopher Sims, 2001. "Matlab Code for Solving Linear Rational Expectations Models," QM&RBC Codes 11, Quantitative Macroeconomics & Real Business Cycles.
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