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Generalized Stochastic Gradient Learning

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  • George W. Evans
  • Seppo Honkapohja
  • Noah Williams

Abstract

We study the properties of generalized stochastic gradient (GSG) learning in forward-looking 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://www.nber.org/papers/t0317.pdf
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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0317.

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Date of creation: Oct 2005
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Handle: RePEc:nbr:nberte:0317

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  1. 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.
  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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