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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.

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Bibliographic Info

Paper provided by EconWPA in its series Macroeconomics with number 9706001.

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Length: 17 pages
Date of creation: 04 Jun 1997
Date of revision:
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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Web page: http://128.118.178.162

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

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  1. John B. Taylor & Harald Uhlig, 1990. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," NBER Working Papers 3117, National Bureau of Economic Research, Inc.
  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 733-48, September.
  3. Harald Uhlig, 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper / Institute for Empirical Macroeconomics 101, Federal Reserve Bank of Minneapolis.
  4. Den Haan, Wouter J & Marcet, Albert, 1994. "Accuracy in Simulations," Review of Economic Studies, Wiley Blackwell, vol. 61(1), pages 3-17, 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.
  6. Tauchen, George & Hussey, Robert, 1991. "Quadrature-Based Methods for Obtaining Approximate Solutions to Nonlinear Asset Pricing Models," Econometrica, Econometric Society, vol. 59(2), pages 371-96, March.
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