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

Anderson, Gary, Solving Linear Rational Expectations Models: A Horse Race (June 2006). FEDS Working Paper No. 2006-26, Available at SSRN: https://ssrn.com/abstract=943773 or http://dx.doi.org/10.2139/ssrn.943773

Gary Anderson (Contact Author)

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