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Generalized Stochastic Gradient Learning

  • George W. Evans
  • Seppo Honkapohja
  • Noah Williams

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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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
Date of revision:
Handle: RePEc:nbr:nberte:0317
Note: TWP
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  1. 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.
  2. Evans, G.W. & Honkapohja, S., 1998. "Stochastic Gradient Learning in the Cobweb Model," University of Helsinki, Department of Economics 438, Department of Economics.
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