Feedback approximation of the stochastic growth model by genetic neural networks

DOI10.1007/s10614-006-9024-8zbMath1154.91568OpenAlexW2014920738MaRDI QIDQ853580

M. Nedim Alemdar, Stephen J. Turnovsky, Sibel Sirakaya

Publication date: 17 November 2006

Published in: Computational Economics (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/11693/23801

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Stochastic models in economics (91B70) Economic growth models (91B62)

Uses Software

• CompEcon

Cites Work

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• A genetic game of trade, growth and externalities
• Projection methods for solving aggregate growth models
• Algorithms for solving dynamic models with occasionally binding constraints
• Learning the optimum as a Nash equilibrium
• Multilayer feedforward networks are universal approximators
• Genetic neural networks to approximate feedback Nash equilibria in dynamic games
• Solution and Maximum Likelihood Estimation of Dynamic Nonlinear Rational Expectations Models
• Accuracy in Simulations
• Time Series Tests of Endogenous Growth Models
• Approximating and simulating the stochastic growth model: Parameterized expectations, neural networks, and the genetic algorithm

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