Generalized Stochastic Gradient Learning

Evans, George W.; Honkapohja, Seppo; Williams, Noah

doi:10.1111/j.1468-2354.2009.00578.x

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Generalized Stochastic Gradient Learning

International Economic Review, Vol. 51, Issue 1, pp. 237-262, February 2010

26 Pages Posted: 1 Mar 2010

See all articles by George W. Evans

George W. Evans

University of Oregon - Department of Economics; University of St. Andrews - School of Economics and Finance

Seppo Honkapohja

Centre for Economic Policy Research (CEPR); Aalto University School of Business; CESifo (Center for Economic Studies and Ifo Institute for Economic Research); Aalto University - School of Business

Noah Williams

Princeton University - Department of Economics; National Bureau of Economic Research (NBER)

There are 3 versions of this paper

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.

Suggested Citation: Suggested Citation

Evans, George W. and Honkapohja, Seppo and Honkapohja, Seppo and Williams, Noah, Generalized Stochastic Gradient Learning. International Economic Review, Vol. 51, Issue 1, pp. 237-262, February 2010, Available at SSRN: https://ssrn.com/abstract=1560178 or http://dx.doi.org/10.1111/j.1468-2354.2009.00578.x