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

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  • John Duffy

    (University of Pittsburgh)

  • Paul D. McNelis

    (University of Georgetown)

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 Society for Computational Economics in its series Computing in Economics and Finance 1997 with number 63.

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Handle: RePEc:sce:scecf7:63

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Postal: CEF97, Stanford University, Department of Economics, Stanford CA USA
Web page: http://bucky.stanford.edu/cef97/
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  1. Cooley, T.F. & Hansen, G.D., 1988. "The Inflation Tax In A Real Business Cycle Model," RCER Working Papers 155, University of Rochester - Center for Economic Research (RCER).
  2. Uhlig, H., 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper 1995-97, Tilburg University, Center for Economic Research.
  3. 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.
  4. 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.
  5. Wouter J. den Haan & Albert Marcet, 1993. "Accuracy in simulations," Economics Working Papers 42, 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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