Generalized Stochastic Gradient Learning

George Evans, Seppo Honkapohja and Noah Williams

University of Oregon Economics Department Working Papers from University of Oregon Economics Department

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 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)
Pages: 35
Date: 2005-09-19, Revised 2008-05-18
New Economics Papers: this item is included in nep-mac
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://economics.uoregon.edu/papers/UO-2005-17_Evans_Gradient.pdf (application/pdf)

Related works:
Journal Article: GENERALIZED STOCHASTIC GRADIENT LEARNING (2010)
Working Paper: Generalized Stochastic Gradient Learning (2005)
Working Paper: Generalized Stochastic Gradient Learning (2005)
Working Paper: Generalized Stochastic Gradient Learning (2005)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ore:uoecwp:2005-17

Access Statistics for this paper

More papers in University of Oregon Economics Department Working Papers from University of Oregon Economics Department Contact information at EDIRC.
Bibliographic data for series maintained by Bill Harbaugh ().