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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 Paul McNelis (Georgetown University)
John Duffy
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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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Paper provided by EconWPA in its series GE, Growth, Math methods with number
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Length: 34 pages
Date of creation: 30 Apr 1998Date of revision:
04 May 1998Handle: RePEc:wpa:wuwpge:9804004Note: Type of Document - MS Word 97; prepared on IBM PC; to print on HP; pages: 34 ; figures: includedContact details of provider: Web page: http://129.3.20.41
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Article Duffy, John & McNelis, Paul D., 2001.
"Approximating and simulating the stochastic growth model: Parameterized expectations, neural networks, and the genetic algorithm ,"
Journal of Economic Dynamics and Control ,
Elsevier, vol. 25(9), pages 1273-1303, September.
[Downloadable!] (restricted) 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
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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.)
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Paul D. McNelis & Guay Lim, 2006.
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Other versions: Javier J. Pérez, 2001.
"A Log-linear Homotopy Approach to Initialize the Parameterized Expectations Algorithm ,"
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Richard Dennis, 2004.
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