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.
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.:
- Evans, George W. & Honkapohja, S., 1998.
"Stochastic gradient learning in the cobweb model,"
Elsevier, vol. 61(3), pages 333-337, December.
- 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.
- 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, 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.
- 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.
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