Generalized Stochastic Gradient Learning

George Evans, Seppo Honkapohja and Noah Williams

No 1576, CESifo Working Paper Series from CESifo

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.

Keywords: adaptive learning; E-stability; recursive least squares; robust estimation (search for similar items in EconPapers)
JEL-codes: C62 C65 D83 E10 E17 (search for similar items in EconPapers)
Date: 2005
New Economics Papers: this item is included in nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Related works:
Journal Article: GENERALIZED STOCHASTIC GRADIENT LEARNING (2010)
Working Paper: Generalized Stochastic Gradient Learning (2008)
Working Paper: Generalized Stochastic Gradient Learning (2005)
Working Paper: Generalized Stochastic Gradient Learning (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_1576

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