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Generalized Stochastic Gradient Learning

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  • Evans, G.W.
  • Honkapohja ,S.
  • Williams, N.

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 di1er 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.econ.cam.ac.uk/research/repec/cam/pdf/cwpe0545.pdf
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Bibliographic Info

Paper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0545.

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Length: 35
Date of creation: Oct 2005
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Handle: RePEc:cam:camdae:0545

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Web page: http://www.econ.cam.ac.uk/index.htm

Related research

Keywords: adaptive learning; E-stability; recursive least squares; robust estimation;

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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.
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