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Feedback Approximation of the Stochastic Growth Model by Genetic Neural Networks

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  • S. Sirakaya
  • Stephen Turnovsky

    ()

  • M. Alemdar

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File URL: http://hdl.handle.net/10.1007/s10614-006-9024-8
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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 27 (2006)
Issue (Month): 2 (May)
Pages: 185-206

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Handle: RePEc:kap:compec:v:27:y:2006:i:2:p:185-206

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Web page: http://www.springerlink.com/link.asp?id=100248
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Keywords: stochastic growth model; genetic algorithms; neural networks;

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References

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  1. Fair, Ray C & Taylor, John B, 1983. "Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectations Models," Econometrica, Econometric Society, vol. 51(4), pages 1169-85, July.
  2. Schmitt-Grohé, Stephanie & Uribe, Martín, 2001. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," CEPR Discussion Papers 2963, C.E.P.R. Discussion Papers.
  3. Kim, Jinill & Kim, Sunghyun Henry, 2003. "Spurious welfare reversals in international business cycle models," Journal of International Economics, Elsevier, vol. 60(2), pages 471-500, August.
  4. Tauchen, George, 1990. "Solving the Stochastic Growth Model by Using Quadrature Methods and Value-Function Iterations," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 49-51, January.
  5. Ingram, Beth Fisher, 1990. "Solving the Stochastic Growth Model by Backsolving with an Expanded Shock Space," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 37-38, January.
  6. 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.
  7. Christiano, Lawrence J. & Fisher, Jonas D. M., 2000. "Algorithms for solving dynamic models with occasionally binding constraints," Journal of Economic Dynamics and Control, Elsevier, vol. 24(8), pages 1179-1232, July.
  8. Christiano, Lawrence J, 1990. "Solving the Stochastic Growth Model by Linear-Quadratic Approximation and by Value-Function Iteration," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 23-26, January.
  9. Paul McNelis & John Duffy, 1998. "Approximating and Simulating the Stochastic Growth Model: Parameterized Expectations, Neural Networks, and the Genetic Algorithm," GE, Growth, Math methods 9804004, EconWPA, revised 04 May 1998.
  10. 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.
  11. Jones, Charles I, 1995. "Time Series Tests of Endogenous Growth Models," The Quarterly Journal of Economics, MIT Press, vol. 110(2), pages 495-525, May.
  12. Ozyildirim, Suheyla & Alemdar, Nedim M., 2000. "Learning the optimum as a Nash equilibrium," Journal of Economic Dynamics and Control, Elsevier, vol. 24(4), pages 483-499, April.
  13. 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.
  14. Gagnon, Joseph E, 1990. "Solving the Stochastic Growth Model by Deterministic Extended Path," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 35-36, January.
  15. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
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Cited by:
  1. G. C. Lim & Paul D. McNelis, 2006. "Fiscal and Current Account Balances in a Model with Sticky Prices and Distortionary Taxes," Melbourne Institute Working Paper Series wp2006n21, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.

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