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Accuracy in simulations

  • Wouter J. den Haan
  • Albert Marcet

Since the actual solution to intertemporal rational expectations models is usually not known, it is useful to have criteria for judging the accuracy of a given numerical solution. In this paper, the authors propose a test for accuracy that is easy to implement and can be applied to a wide class of models without knowledge of the exact solution. They discuss the power of the test by simulating several models with the linear-quadratic approximation and with the method of parametrized expectations. The authors conclude that the test is powerful. Copyright 1994 by The Review of Economic Studies Limited.

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Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 42.

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Date of creation: Sep 1993
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Handle: RePEc:upf:upfgen:42
Contact details of provider: Web page: http://www.econ.upf.edu/

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  1. Cooley, T.F. & Hansen, G.D., 1988. "The Inflation Tax In A Real Business Cycle Model," Papers 88-05, Rochester, Business - General.
  2. Coleman, Wilbur John, II, 1991. "Equilibrium in a Production Economy with an Income Tax," Econometrica, Econometric Society, vol. 59(4), pages 1091-1104, July.
  3. Ray C. Fair & John B. Taylor, 1980. "Solution and Maximum Likelihood Estimation of Dynamic Nonlinear RationalExpectations Models," NBER Technical Working Papers 0005, National Bureau of Economic Research, Inc.
  4. Albert Marcet & Ramon Marimon, 1992. "Communication, commitment, and growth," Discussion Paper / Institute for Empirical Macroeconomics 74, Federal Reserve Bank of Minneapolis.
  5. 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.
  6. 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.
  7. Novales, Alfonso, 1990. "Solving Nonlinear Rational Expectations Models: A Stochastic Equilibrium Model of Interest Rates," Econometrica, Econometric Society, vol. 58(1), pages 93-111, January.
  8. Christiano, Lawrence J, 1990. "Linear-Quadratic Approximation and Value-Function Iteration: A Comparison," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 99-113, January.
  9. 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.
  10. Brock, William A. & Mirman, Leonard J., 1972. "Optimal economic growth and uncertainty: The discounted case," Journal of Economic Theory, Elsevier, vol. 4(3), pages 479-513, June.
  11. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  12. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
  13. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-70, November.
  14. Miller, Robert A, 1984. "Job Matching and Occupational Choice," Journal of Political Economy, University of Chicago Press, vol. 92(6), pages 1086-120, December.
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