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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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Working Paper: Generalized Stochastic Gradient Learning (2008) Downloads
Working Paper: Generalized Stochastic Gradient Learning (2005) Downloads
Working Paper: Generalized Stochastic Gradient Learning (2005) Downloads
Working Paper: Generalized Stochastic Gradient Learning (2005) Downloads
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