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GENERALIZED STOCHASTIC GRADIENT LEARNING*
Authors:George W Evans  Seppo Honkapohja  Noah Williams
Institution:1. University of Oregon, U.S.A., University of St. Andrews, UK;2. Bank of Finland, Finland;3. University of Wisconsin–Madison, U.S.A.;4. The authors thank Chryssi Giannitsarou, Larry Samuelson, Felix Kubler, and two anonymous referees for useful comments. Financial support from National Science Foundation Grant No. SES‐0617859 and ESRC grant RES‐000‐23‐1152 is gratefully acknowledged. Please address correspondence to: Noah Williams, Department of Economics, University of Wisconsin–Madison, William H. Sewell Social Science Building, Room 7434, 1180 Observatory Drive, Madison, WI 53706‐1393. Phone: (608) 263‐3864. E‐mail: .
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.
Keywords:
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