A MATLAB Solver for Nonlinear Rational Expectations Models
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
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Volume (Year): 26 (2005)
Issue (Month): 2 (October)
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References listed on IDEAS
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- 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.
- John B. Taylor & Harald Uhlig, 1989. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," NBER Working Papers 3117, National Bureau of Economic Research, Inc.
- Marimon, Ramon & Scott, Andrew (ed.), 1999. "Computational Methods for the Study of Dynamic Economies," OUP Catalogue, Oxford University Press, number 9780198294979, April. Full references (including those not matched with items on IDEAS)