This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Approximating and Simulating the Stochastic Growth Model: Parameterized Expectations, Neural Networks, and the Genetic Algorithm

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Paul McNelis (Georgetown University)
John Duffy

Additional information is available for the following registered author(s):

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.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help file. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://129.3.20.41/eps/ge/papers/9804/9804004.html
File Format: text/html
File Function:
Download Restriction: no
File URL: http://129.3.20.41/eps/ge/papers/9804/9804004.pdf
File Format: application/pdf
File Function:
Download Restriction: no
File URL: http://129.3.20.41/eps/ge/papers/9804/9804004.doc.gz
File Format: application/msword
File Function:
Download Restriction: no
File URL: http://129.3.20.41/eps/ge/papers/9804/9804004.ps.gz
File Format: application/postscript
File Function:
Download Restriction: no

Publisher Info
Paper provided by EconWPA in its series GE, Growth, Math methods with number 9804004.

Download reference. The following formats are available: HTML, plain text, BibTeX, RIS (EndNote), ReDIF
Length: 34 pages
Date of creation: 30 Apr 1998
Date of revision: 04 May 1998
Handle: RePEc:wpa:wuwpge:9804004

Note: Type of Document - MS Word 97; prepared on IBM PC; to print on HP; pages: 34 ; figures: included
Contact details of provider:
Web page: http://129.3.20.41

For technical questions regarding this item, or to correct its listing, contact: (EconWPA).

Related research
Keywords:

Other versions of this item:

Find related papers by JEL classification:
C6 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques
C68 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computable General Equilibrium Models

This paper has been announced in the following NEP Reports:

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. 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.
    Other versions:
  2. 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.
    Other versions:
  3. Albert Marcet, 1991. "Simulation Analysis of Dynamic Stochastic Models: Applications to Theory and Estimation," Economics Working Papers 6, Department of Economics and Business, Universitat Pompeu Fabra.
  4. Beaumont, Paul M & Bradshaw, Patrick T, 1995. "A Distributed Parallel Genetic Algorithm for Solving Optimal Growth Models," Computational Economics, Springer, vol. 8(3), pages 159-79, August.
  5. Cooley, Thomas F & Hansen, Gary D, 1989. "The Inflation Tax in a Real Business Cycle Model," American Economic Review, American Economic Association, vol. 79(4), pages 733-48, September. [Downloadable!] (restricted)
    Other versions:
  6. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January. [Downloadable!] (restricted)
  7. Den Haan, Wouter J & Marcet, Albert, 1994. "Accuracy in Simulations," Review of Economic Studies, Blackwell Publishing, vol. 61(1), pages 3-17, January. [Downloadable!] (restricted)
    Other versions:
  8. Dorsey, Robert E & Mayer, Walter J, 1995. "Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 53-66, January.
    Other versions:
  9. 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.
Full references

Cited by:
(explanations, 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. 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. [Downloadable!]
  2. Richard Dennis, 2004. "Specifying and estimating New Keynesian models with instrument rules and optimal monetary policies," Working Papers in Applied Economic Theory 2004-17, Federal Reserve Bank of San Francisco. [Downloadable!]
  3. 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. [Downloadable!]
  4. 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. [Downloadable!]
    Other versions:
  5. Paul McNelis & Peter McAdam, 2004. "Forecasting inflation with thick models and neural networks," Working Paper Series 352, European Central Bank. [Downloadable!]
    Other versions:
  6. G. C. LIM & PAUL D. McNELIS, 2002. "Central Bank Learning, Terms Of Trade Shocks & Currency Risks: Should Only Inflation Matter For Monetary Policy?," Department of Economics - Working Papers Series 831, The University of Melbourne. [Downloadable!]
    Other versions:
Statistics
Access and download statistics

Did you know? All full texts are decentralized with the publishers, none reside on this server, thus making it possible to offer this service for free to all parties.

This page was last updated on 2008-7-6.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.