Anderson-Moore Algorithm (AMA)

The Anderson-Moore Algorithm is a fast and reliable method for solving linear saddle point models. It's the fastest available method for solving large problems. The algorithm has proven useful in a wide array of applications, including analyzing linear perfect-foresight models and providing initial solutions and asymptotic constraints for nonlinear models. The technique works well both for symbolic and numerical computation.

The algorithm was developed at the Federal Reserve Board by Gary Anderson and George Moore. At the Board of Governors, economists commonly refer to this algorithm and an array of other related tools as "AIM". The moniker comes from a metaphor relating our approach to the "shooting method" for solving two point boundary value problems.

All of the code implementing the algorithm is in the public domain and may be used freely. However, the authors would appreciate acknowledgement of the source by citation of any of the following papers:
- Anderson, G. and Moore, G. "A Linear Algebraic Procedure for Solving Linear Perfect Foresight Models." Economics Letters, 17, 1985.
- Anderson, G. "Solving Linear Rational Expectations Models: A Horse Race." Computational Economics, 2008, vol. 31, issue 2, pp. 95-113.
- Anderson, G. "A Reliable and Computationally Efficient Algorithm for Imposing the Saddle Point Property in Dynamic Models." Journal of Economic Dynamics and Control, 2010, vol. 34, issue 3, pp. 472-489.

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Last Update: March 10, 2017