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Approximating and Simulating the Real Business Cycle: Linear Quadratic Methods, Parameterized Expectations, and Genetic Algorithms

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  • Paul McNelis

    (Georgetown University)

  • John Duffy

    (University of Pittsburgh)

Abstract

This paper compares three approximation methods for solving and simulating real business cycle models: linear quadratic (including log- linear quadratic) methods, the method of parameterized expectations, and the genetic algorithm. Linear quadratic (LQ), log-linear quadratic (log- LQ) and parameterized expectations (PE) methods are commonly used in numerical approximation and simulation of wide classes of real business cycle models. This papers examines what differences the genetic algorithm (GA) may turn up, as the volatility of the stochastic shocks and the relative risk parameter increase in value. Our results show that the GA either closely matches or outperforms the LQ, loq-LQ and PE for approximating an exact solution. For higher degrees of nonlinearity and stochastic volatility, the GA gives slightly different results than the LQ and PE methods. Our results suggest that the GA should at least compliment these approaches for approximating such models.

Suggested Citation

  • Paul McNelis & John Duffy, 1997. "Approximating and Simulating the Real Business Cycle: Linear Quadratic Methods, Parameterized Expectations, and Genetic Algorithms," Macroeconomics 9706001, EconWPA.
  • Handle: RePEc:wpa:wuwpma:9706001
    Note: Type of Document - Word 7.0; prepared on IBM PC ; to print on HP; pages: 17 ; figures: included
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    References listed on IDEAS

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    1. Tauchen, George & Hussey, Robert, 1991. "Quadrature-Based Methods for Obtaining Approximate Solutions to Nonlinear Asset Pricing Models," Econometrica, Econometric Society, vol. 59(2), pages 371-396, March.
    2. Cooley, Thomas F & Hansen, Gary D, 1989. "The Inflation Tax in a Real Business Cycle Model," American Economic Review, American Economic Association, vol. 79(4), pages 733-748, September.
    3. Wouter J. Den Haan & Albert Marcet, 1994. "Accuracy in Simulations," Review of Economic Studies, Oxford University Press, vol. 61(1), pages 3-17.
    4. 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.
    5. Albert Marcet, 1991. "Simulation analysis of dynamic stochastic models: Applications to theory and estimation," Economics Working Papers 6, Department of Economics and Business, Universitat Pompeu Fabra.
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    Keywords

    Approximating and Simulating the Real Business Cycle: Linear Quadratic Methods; Parameterized Expectations; and Genetic Algorithms;

    JEL classification:

    • E - Macroeconomics and Monetary Economics

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