Solving Linear Rational Expectations Models: A Horse Race
نویسنده
چکیده
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 procedures 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. ∗I would like to thank Robert Tetlow, Andrew Levin and Brian Madigan for useful discussions and suggestions. I would like to thank Ed Yao for valuable help in obtaining and installing the MATLAB code. The views expressed in this document are my own and do not necessarily reflect the position of the Federal Reserve Board or the Federal Reserve System.
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