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Computational Economics

, Volume 26, Issue 2, pp 173–181 | Cite as

A MATLAB Solver for Nonlinear Rational Expectations Models

  • Paul L. Fackler
Article

Abstract

A framework for describing nonlinear rational expectation models is developed that synthesizes previously described approaches. Computational issues for solving such models include how the expectation operator is approximated, what family of approximation is used for the solution function, what criteria are used for choosing approximation parameters and what algorithm is used to identify the parameters. A user-friendly MATLAB procedure that incorporates a wide variety of possible choices is described.

Key words

projection methods rational expectations 

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References

  1. Judd, K.L. (1998). Numerical Methods in Economics. MIT Press, Cambridge, MA.Google Scholar
  2. Marimon, R. and Scott, A. (eds.) (1999). Computational Methods for the Study of Dynamic Economies. Oxford University Press, Oxford.Google Scholar
  3. Miranda, M.J. and Fackler, P.L. (2002). Applied Computational Economics and Finance. MIT Press, Cambridge, MA.Google Scholar
  4. Sims, C.A. (2001). Solving linear rational expectation models. Computational Economics, 20, 1–20.CrossRefGoogle Scholar
  5. Taylor, J.B. and Uhlig, H. (1990). Solving nonlinear stochastic growth models: A comparison of alternative solution methods. Journal of Business and Economic Statistics, 8, 1–18.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Department of Agricultural and Resource EconomicsNorth Carolina State UniversityRaleighU.S.A.

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