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A MATLAB Solver for Nonlinear Rational Expectations Models

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  • Paul Fackler

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. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Paul Fackler, 2005. "A MATLAB Solver for Nonlinear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 26(2), pages 173-181, October.
    • Handle: RePEc:kap:compec:v:26:y:2005:i:2:p:173-181
    DOI: 10.1007/s10614-005-1784-z
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    References listed on IDEAS

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    1. Taylor, John B & Uhlig, Harald, 1990. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 1-17, January.
    2. Marimon, Ramon & Scott, Andrew (ed.), 1999. "Computational Methods for the Study of Dynamic Economies," OUP Catalogue, Oxford University Press, number 9780198294979.
    3. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, December.
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    Cited by:

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    2. Gouel, Christophe, 2013. "Optimal food price stabilisation policy," European Economic Review, Elsevier, vol. 57(C), pages 118-134.
    3. Christophe Gouel, 2013. "Comparing Numerical Methods for Solving the Competitive Storage Model," Computational Economics, Springer;Society for Computational Economics, vol. 41(2), pages 267-295, February.

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    Keywords

    projection methods; rational expectations;

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