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Generalized Stochastic Gradient Learning

  • George W. Evans
  • Seppo Honkapohja
  • Noah Williams

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.

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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1468-2354.2009.00578.x
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Article provided by Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association in its journal International Economic Review.

Volume (Year): 51 (2010)
Issue (Month): 1 (02)
Pages: 237-262

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Handle: RePEc:ier:iecrev:v:51:y:2010:i:1:p:237-262
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  1. George W. Evans & Seppo Honkapohja, 2003. "Expectations and the Stability Problem for Optimal Monetary Policies," Review of Economic Studies, Wiley Blackwell, vol. 70(4), pages 807-824, October.
  2. Evans, George W. & Honkapohja, S., 1998. "Stochastic gradient learning in the cobweb model," Economics Letters, Elsevier, vol. 61(3), pages 333-337, December.
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