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

Paul McNelis () and John Duffy

GE, Growth, Math methods from University Library of Munich, Germany

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.

JEL-codes: C6 C63 C68 (search for similar items in EconPapers)
Pages: 34 pages
Date: 1998-04-30, Revised 1998-05-14
New Economics Papers: this item is included in nep-dge, nep-ets and nep-tid
Note: Type of Document - MS Word 97; prepared on IBM PC; to print on HP; pages: 34 ; figures: included
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Citations: View citations in EconPapers (8)

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Related works:
Journal Article: Approximating and simulating the stochastic growth model: Parameterized expectations, neural networks, and the genetic algorithm (2001)
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