Generalized Stochastic Gradient Learning
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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Volume (Year): 51 (2010)
Issue (Month): 1 (02)
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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- George W. Evans & Seppo Honkapohja, 2003.
"Expectations and the Stability Problem for Optimal Monetary Policies,"
Review of Economic Studies,
Oxford University Press, vol. 70(4), pages 807-824.
- Honkapohja, Seppo & Evans, George W., 2000. "Expectations and the stability problem for optimal monetary policies," Discussion Paper Series 1: Economic Studies 2000,10, Deutsche Bundesbank, Research Centre.
- Evans, George W. & Honkapohja, Seppo, 2001. "Expectations and the Stability Problem for Optimal Monetary Policies," CEPR Discussion Papers 2805, C.E.P.R. Discussion Papers.
- Honkapohja, S. & Evans, G.W., 2000. "Expectations and the Stability Problem for Optimal Monetary Policies," University of Helsinki, Department of Economics 481, Department of Economics.
- George W. Evans & Seppo Honkapohja, 2001. "Expectations and the Stability Problem for Optimal Monetary Policies," University of Oregon Economics Department Working Papers 2001-6, University of Oregon Economics Department, revised 03 Aug 2001.
- 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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