Generalized Stochastic Gradient Learning

George William Evans (), Seppo Mikko Sakari Honkapohja () and Noah Williams ()

No 317, NBER Technical Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C62 C65 D83 E10 E17 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-evo and nep-mac
Date: Written
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Related works:
Working Paper: Generalized Stochastic Gradient Learning (2005)
Working Paper: Generalized Stochastic Gradient Learning (2005)
Working Paper: Generalized Stochastic Gradient Learning (2008)
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