GENERALIZED STOCHASTIC GRADIENT LEARNING
George Evans,
Seppo Honkapohja and
Noah Williams
International Economic Review, 2010, vol. 51, issue 1, 237-262
Abstract:
We study the properties of the generalized stochastic gradient (GSG) learning in forward-looking models. GSG algorithms are a natural and convenient way to model learning when agents allow for parameter drift or robustness to parameter uncertainty in their beliefs. The conditions for convergence of GSG learning to a rational expectations equilibrium are distinct from but related to the well-known stability conditions for least squares learning. Copyright (2010) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
Date: 2010
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Related works:
Working Paper: Generalized Stochastic Gradient Learning (2008) 
Working Paper: Generalized Stochastic Gradient Learning (2005) 
Working Paper: Generalized Stochastic Gradient Learning (2005) 
Working Paper: Generalized Stochastic Gradient Learning (2005) 
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Persistent link: https://EconPapers.repec.org/RePEc:ier:iecrev:v:51:y:2010:i:1:p:237-262
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