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Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods

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John B. Taylor
Harald Uhlig

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Abstract

The purpose of this paper is to report on a comparison of several alternative numerical solution techniques for nonlinear rational expectations models. The comparison was made by asking individual researchers to apply their different solution techniques to a simple representative agent, optimal, stochastic growth model. Decision rules as well as simulated time series are compared. The differences among the methods turned ou t to be quite substantial for certain aspects of the growth model. Therefore, researchers might want to be careful not to rely blindly on the results of any chosen numerical solution method in applied work.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 3117.

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Date of creation: Sep 1990
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Handle: RePEc:nbr:nberwo:3117

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  1. Quantitative Macroeconomics and Real Business Cycles (QM&RBC)
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Ingram, Beth Fisher, 1990. "Equilibrium Modeling of Asset Prices: Rationality versus Rules of Thumb," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 115-25, January.
  2. McGrattan, Ellen R, 1990. "Solving the Stochastic Growth Model by Linear-Quadratic Approximation," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 41-44, January.
  3. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May. [Downloadable!] (restricted)
  4. Sims, Christopher A, 1990. "Solving the Stochastic Growth Model by Backsolving with a Particular Nonlinear Form for the Decision Rule," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 45-47, January.
  5. Coleman, Wilbur John, II, 1990. "Solving the Stochastic Growth Model by Policy-Function Iteration," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 27-29, January.
  6. Baxter, Marianne & Crucini, Mario J & Rouwenhorst, K Geert, 1990. "Solving the Stochastic Growth Model by a Discrete-State-Space, Euler-Equation Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 19-21, January.
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