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EconStor >
ifo Institut – Leibniz-Institut für Wirtschaftsforschung an der Universität München >
CESifo Working Papers, CESifo Group Munich >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10419/19040
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Full metadata record
| DC Field | | Value | | Language |
| dc.contributor.author | | Evans, George W. | | en_US |
| dc.contributor.author | | Honkapohja, Seppo | | en_US |
| dc.contributor.author | | Williams, Noah | | en_US |
| dc.date.accessioned | | 2009-01-28T15:54:38Z | | - |
| dc.date.available | | 2009-01-28T15:54:38Z | | - |
| dc.date.issued | | 2005 | | en_US |
| dc.identifier.uri | | http://hdl.handle.net/10419/19040 | | - |
| dc.description.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. | | en_US |
| dc.language.iso | | eng | | en_US |
| dc.publisher | | | | en_US |
| dc.relation.ispartofseries | | CESifo working papers 1576 | | en_US |
| dc.subject.jel | | C65 | | en_US |
| dc.subject.jel | | C62 | | en_US |
| dc.subject.jel | | E17 | | en_US |
| dc.subject.jel | | E10 | | en_US |
| dc.subject.jel | | D83 | | en_US |
| dc.subject.ddc | | 330 | | en_US |
| dc.subject.keyword | | adaptive learning | | en_US |
| dc.subject.keyword | | E-stability | | en_US |
| dc.subject.keyword | | recursive least squares | | en_US |
| dc.subject.keyword | | robust estimation | | en_US |
| dc.subject.stw | | Rationale Erwartung | | en_US |
| dc.subject.stw | | Lernprozess | | en_US |
| dc.subject.stw | | Prognoseverfahren | | en_US |
| dc.subject.stw | | Gleichgewichtsstabilität | | en_US |
| dc.subject.stw | | Theorie | | en_US |
| dc.title | | Generalized stochastic gradient learning | | en_US |
| dc.type | | Working Paper | | en_US |
| dc.identifier.ppn | | 503712469 | | en_US |
| dc.rights | | http://www.econstor.eu/dspace/Nutzungsbedingungen | | - |
| Appears in Collections: | | CESifo Working Papers, CESifo Group Munich
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