@techreport{Evans2005Generalized,
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.},
author = {George W. Evans and Seppo Honkapohja and Noah Williams},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {C65; C62; E17; E10; D83; 330; adaptive learning; E-stability; recursive least squares; robust estimation; Rationale Erwartung; Lernprozess; Prognoseverfahren; Gleichgewichtsstabilit\"{a}t; Theorie},
language = {eng},
number = {1576},
title = {Generalized stochastic gradient learning},
type = {CESifo working papers},
url = {http://hdl.handle.net/10419/19040},
year = {2005}
}
