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Generalized Stochastic Gradient Learning

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Author Info
George W. Evans ()
Seppo Honkapohja ()
Noah Williams ()

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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.

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Publisher Info
Paper provided by CESifo GmbH in its series CESifo Working Paper Series with number CESifo Working Paper No. 1576.

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Date of creation: 2005
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Handle: RePEc:ces:ceswps:_1576

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Related research
Keywords: adaptive learning E-stability recursive least squares robust estimation

Other versions of this item:

Find related papers by JEL classification:
C62 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Existence and Stability Conditions of Equilibrium
C65 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Miscellaneous Mathematical Tools
D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. George W. Evans & Seppo Honkapohja, 2003. "Expectations and the Stability Problem for Optimal Monetary Policies," Review of Economic Studies, Blackwell Publishing, vol. 70(4), pages 807-824, October. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Seppo Honkapohja & Kaushik Mitra, 2006. "Learning Stability in Economies with Heterogeneous Agents," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 9(2), pages 284-309, April. [Downloadable!] (restricted)
    Other versions:
  2. Carceles-Poveda, Eva & Giannitsarou, Chryssi, 2006. "Adaptive Learning in Practice," CEPR Discussion Papers 5627, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  3. Emiliano Santoro & Damjan Pfajfar, 2006. "Heterogeneity and learning in inflation expectation formation: an empirical assessment," Department of Economics Working Papers 0607, Department of Economics, University of Trento, Italia. [Downloadable!]
  4. Sergey Slobodyan & Anna Bogomolova & Dmitri Kolyuzhnov, 2006. "Stochastic Gradient versus Recursive Least Squares Learning," Computing in Economics and Finance 2006 446, Society for Computational Economics. [Downloadable!]
  5. James B. Bullard, 2006. "The learnability criterion and monetary policy," Review, Federal Reserve Bank of St. Louis, issue May, pages 203-217. [Downloadable!]
  6. Sergey Slobodyan & Atanas Christev, 2006. "On learnability of E–stable equilibria," Computing in Economics and Finance 2006 451, Society for Computational Economics. [Downloadable!]
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This page was last updated on 2008-9-22.


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