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Optimization Using Genetic Algorithms: An Application to the Real Business Cycle Model

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Christian Johnson

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Abstract

This paper uses genetic algorithms (GAs) to find the optimal parameter values in the solution of the Real Business Cycle model. To generate the policy functions of the individual, we approximate the conditional expectation of the Euler equation using an exponential polynomial function, based on the method proposed by Marcet (1991). The ambiguity in the selection of the starting values for the proposed algorithm allows the application of the GAs methodology to improve the macroeconomic simulations.

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Publisher Info
Paper provided by Central Bank of Chile in its series Working Papers Central Bank of Chile with number 10.

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Date of creation: Mar 1997
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Handle: RePEc:chb:bcchwp:10

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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.
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  2. den Haan, Wouter J & Marcet, Albert, 1990. "Solving the Stochastic Growth Model by Parameterizing Expectations," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 31-34, January.
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  3. Albert Marcet, 1991. "Solving Non-Linear Stochastic Models by Parameterizing Expectations: An Application to Asset Pricing with Production," Economics Working Papers 5, Department of Economics and Business, Universitat Pompeu Fabra. [Downloadable!]
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