Solving linear rational expectations models
a horse race
by Gary S. Anderson
"This paper compares the functionality, accuracy, computational efficiency, and practicalities of alternative approaches to solving linear rational expectations models, including the procedures of (Sims, 1996), (Anderson and Moore, 1983), (Binder and Pesaran, 1994), (King and Watson, 1998), (Klein, 1999), and (Uhlig, 1999). While all six prcedures yield similar results for models with a unique stationary solution, the AIM algorithm of (Anderson and Moore, 1983) provides the highest accuracy; furthermore, this procedure exhibits significant gains in computational efficiency for larger-scale models"--Federal Reserve Board web site.
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Solving linear rational expectations models: a horse race
2006, Federal Reserve Board
electronic resource :
in English
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Title from PDF file as viewed on 12/7/2006.
Includes bibliographical references.
Also available in print.
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Mode of access: World Wide Web.
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