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Erscheinungsjahr: 
2005
Schriftenreihe/Nr.: 
CESifo Working Paper No. 1576
Verlag: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Zusammenfassung: 
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.
Schlagwörter: 
adaptive learning
E-stability
recursive least squares
robust estimation
JEL: 
C65
C62
E17
E10
D83
Dokumentart: 
Working Paper
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