July 2006

Solving Linear Rational Expectations Models: A Horse Race

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.

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Keywords: Linear Rational Expectations, Blanchard-Kahn, Saddle Point Solution

PDF: Full Paper

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Last Update: November 23, 2020