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Generalized Stochastic Gradient Learning

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  • George W. Evans

    () (University of Oregon Economics Department)

  • Seppo Honkapohja

    () (University of Cambridge)

  • Noah Williams

    (Princeton University and NBER)

Abstract

We study the properties of generalized stochastic gradient (GSG) learning in forwardlooking models. We examine how the conditions for stability of standard stochastic gradient (SG) learning both differ from and are related to E-stability, which governs stability under least squares learning. SG algorithms are sensitive to units of measurement and we show that there is a transformation of variables for which E-stability governs SG stability. GSG algorithms with constant gain have a deeper justification in terms of parameter drift, robustness and risk sensitivity.

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File URL: http://economics.uoregon.edu/papers/UO-2005-17_Evans_Gradient.pdf
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Bibliographic Info

Paper provided by University of Oregon Economics Department in its series University of Oregon Economics Department Working Papers with number 2005-17.

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Length: 35
Date of creation: 19 Sep 2005
Date of revision: 18 May 2008
Handle: RePEc:ore:uoecwp:2005-17

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Postal: 1285 University of Oregon, 435 PLC, Eugene, OR 97403-1285
Phone: (541) 346-4661
Fax: (541) 346-1243
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Web page: http://economics.uoregon.edu/
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Keywords: adaptive learning; E-stability; recursive least squares; robust estimation;

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  1. 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.
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