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Solving the Stochastic Growth Model by Deterministic Extended Path

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Author Info
Gagnon, Joseph E

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Abstract

This article describes the use of the deterministic version of the extended-path algorithm to solve the simple stochastic growth model. The article also discusses the two sources of approximation error inherent in this method. It is demonstrated that the error due to numerical iterations is small. No general conclusion can be reached on the error that arises from the algorithm's treatment of expectations. In at least two specific cases, however, this error appears to be small.

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Publisher Info
Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 8 (1990)
Issue (Month): 1 (January)
Pages: 35-36
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Handle: RePEc:bes:jnlbes:v:8:y:1990:i:1:p:35-36

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  1. S. Sirakaya & Stephen Turnovsky & M. Alemdar, 2006. "Feedback Approximation of the Stochastic Growth Model by Genetic Neural Networks," Computational Economics, Springer, vol. 27(2), pages 185-206, May. [Downloadable!] (restricted)
    Other versions:
  2. Jeffrey C. Fuhrer & C. Hoyt Bleakley, 1996. "Computationally efficient solution and maximum likelihood estimation of nonlinear rational expectation models," Working Papers 96-2, Federal Reserve Bank of Boston. [Downloadable!]
    Other versions:
  3. David R.F. Love, 2008. "A Note on the Accuracy of Extended-Path Solution Methods for Dynamic General Equilibrium Economies," Working Papers 0801, Brock University, Department of Economics, revised Apr 2008. [Downloadable!]
  4. David R.F. Love & Jean-Francois Lamarche, 2004. "Anticipation and Real Business Cycles," Working Papers 0703, Brock University, Department of Economics, revised Sep 2007. [Downloadable!]
  5. David R.F. Love, 2007. "Aggregate Comovements, Anticipation, and Business Cycles," Working Papers 0704, Brock University, Department of Economics, revised Jun 2007. [Downloadable!]
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This page was last updated on 2009-11-22.


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