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Approximate Solutions to Dynamic Models - Linear Methods Author info | Abstract | Publisher info | Download info | Related research | Statistics Harald Uhlig
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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
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Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number
SFB649DP2006-030.
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Length: 12 pages
Date of creation: Apr 2006Date of revision:
Handle: RePEc:hum:wpaper:sfb649dp2006-030Contact details of provider: Postal: Spandauer Str. 1,10178 Berlin Phone: +49-30-2093-5708 Fax: +49-30-2093-5617 Email: Web page: http://sfb649.wiwi.hu-berlin.de More information through EDIRC
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Keywords: numerical methods linear solution method loglinearization dynamic stochastic general equilibrium methods recursive law of motion Other versions of this item:
Find related papers by JEL classification: C60 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - General C61 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Optimization Techniques; Programming Models; Dynamic Analysis C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
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