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Approximating and simulating the stochastic growth model: Parameterized expectations, neural networks, and the genetic algorithm

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  • Duffy, John
  • McNelis, Paul D.

Abstract

This paper compares alternative methods for approximating and solving the stochastic growth model with parameterized expectations. We compare polynomial and neural netowork specifications for expectations, and we employ both genetic algorithm and gradient-descent methods for solving the alternative models of parameterized expectations. Many of the statistics generated by the neural network specification in combination with the genetic algorithm and gradient descent optimization methods approach the statistics generated by the exact solution with risk aversion coefficients close to unity and full depreciation of the capital stock. For the alternative specification, with no depreciation of capital, the neural network results approach those generated by computationally-intense methods. Our results suggest that the neural network specification and genetic algorithm solution methods should at least complement parameterized expectation solutions based on polynomial approximation and pure gradient-descent optimization.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 25 (2001)
Issue (Month): 9 (September)
Pages: 1273-1303

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Handle: RePEc:eee:dyncon:v:25:y:2001:i:9:p:1273-1303

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Cited by:
  1. Paul D. McNelis & Guay Lim, 2006. "Inflation Targeting, Learning and Q Volatility in Small Open Economies," Computing in Economics and Finance 2006 104, Society for Computational Economics.
  2. G.C. Lim & P.D. McNelis, 2002. "Central Bank Learning, Terms of Trade Shocks & Currency Risks: Should Only Inflation Matter for Monetary Policy?," Computing in Economics and Finance 2002 68, Society for Computational Economics.
  3. Richard Dennis, 2006. "The frequency of price adjustment and New Keynesian business cycle dynamics," Working Paper Series 2006-22, Federal Reserve Bank of San Francisco.
  4. Burkhard Heer & Alfred Maussner, 2004. "Computation of Business Cycle Models: A Comparison of Numerical Methods," CESifo Working Paper Series 1207, CESifo Group Munich.
  5. S. Sirakaya & Stephen Turnovsky & M. Alemdar, 2006. "Feedback Approximation of the Stochastic Growth Model by Genetic Neural Networks," Computational Economics, Society for Computational Economics, vol. 27(2), pages 185-206, May.
  6. Lim, G. C. & McNelis, Paul D., 2004. "Learning and the monetary policy strategy of the European Central Bank," Journal of International Money and Finance, Elsevier, Elsevier, vol. 23(7-8), pages 997-1010.
  7. G.C. Lim & Paul D. McNelis, 2001. "Central Bank Learning, Terms of Trade Shocks & Currency Risk: Should Exchange Rate Volatility Matter for Monetary Policy?," Boston College Working Papers in Economics 509, Boston College Department of Economics.
  8. McNelis, Paul & McAdam, Peter, 2004. "Forecasting inflation with thick models and neural networks," Working Paper Series 0352, European Central Bank.
  9. G. Lim & Paul Mcnelis, 2006. "Central Bank Learning and Taylor Rules with Sticky Import Prices," Computational Economics, Society for Computational Economics, vol. 28(2), pages 155-175, September.
  10. Javier J. Pérez, 2001. "A Log-linear Homotopy Approach to Initialize the Parameterized Expectations Algorithm," Economic Working Papers at Centro de Estudios Andaluces E2001/02, Centro de Estudios Andaluces.
  11. Richard Dennis, 2004. "Specifying and estimating New Keynesian models with instrument rules and optimal monetary policies," Working Paper Series 2004-17, Federal Reserve Bank of San Francisco.
  12. Floortje Alkemade & Han Poutré & Hans Amman, 2006. "Robust Evolutionary Algorithm Design for Socio-economic Simulation," Computational Economics, Society for Computational Economics, vol. 28(4), pages 355-370, November.
  13. Hull, Isaiah, 2013. "Approximate dynamic programming with postdecision states as a solution method for dynamic economic models," Working Paper Series 276, Sveriges Riksbank (Central Bank of Sweden).
  14. Roumasset, James A. & Wada, Christopher A., 2012. "Ordering the extraction of renewable resources: The case of multiple aquifers," Resource and Energy Economics, Elsevier, vol. 34(1), pages 112-128.
  15. Lim, G.C. & McNelis, Paul D., 2007. "Central bank learning, terms of trade shocks and currency risk: Should only inflation matter for monetary policy?," Journal of International Money and Finance, Elsevier, Elsevier, vol. 26(6), pages 865-886, October.

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