Solving Linear Rational Expectations Models: A Horse Race
39 Pages Posted: 10 Nov 2006
Date Written: June 2006
Abstract
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.
Keywords: Linear Rational Expectations, Blanchard-Kahn, Saddle Point Solution
JEL Classification: C62, C63
Suggested Citation: Suggested Citation
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