Generalized Stochastic Gradient Learning
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 di1er 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.
|Date of creation:||Oct 2005|
|Date of revision:|
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- Honkapohja, S. & Evans, G.W., 2000.
"Expectations and the Stability Problem for Optimal Monetary Policies,"
University of Helsinki, Department of Economics
481, Department of Economics.
- George W. Evans & Seppo Honkapohja, 2003. "Expectations and the Stability Problem for Optimal Monetary Policies," Review of Economic Studies, Oxford University Press, vol. 70(4), pages 807-824.
- Honkapohja, Seppo & Evans, George W., 2000. "Expectations and the stability problem for optimal monetary policies," Discussion Paper Series 1: Economic Studies 2000,10, Deutsche Bundesbank, Research Centre.
- George W. Evans & Seppo Honkapohja, 2001. "Expectations and the Stability Problem for Optimal Monetary Policies," University of Oregon Economics Department Working Papers 2001-6, University of Oregon Economics Department, revised 03 Aug 2001.
- Evans, George W. & Honkapohja, Seppo, 2001. "Expectations and the Stability Problem for Optimal Monetary Policies," CEPR Discussion Papers 2805, C.E.P.R. Discussion Papers.
- Evans, George W. & Honkapohja, S., 1998.
"Stochastic gradient learning in the cobweb model,"
Elsevier, vol. 61(3), pages 333-337, December.
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