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Generalized Stochastic Gradient Learning

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Author Info
George W. Evans () (University of Oregon Economics Department)
Seppo Honkapohja () (University of Cambridge)
Noah Williams (Princeton University and NBER)

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Abstract

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 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 University of Oregon Economics Department in its series University of Oregon Economics Department Working Papers with number 2005-17.

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Length: 35
Date of creation: 19 Sep 2005
Date of revision: 18 May 2008
Handle: RePEc:ore:uoecwp:2005-17

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

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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
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  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. James B. Bullard, 2006. "The learnability criterion and monetary policy," Review, Federal Reserve Bank of St. Louis, issue May, pages 203-217. [Downloadable!]
  3. Evans , George W & Honkapohja, Seppo, 2007. "Robust learning stability with operational monetary policy rules," Research Discussion Papers 31/2007, Bank of Finland. [Downloadable!]
    Other versions:
  4. Sergey Slobodyan & Atanas Christev, 2006. "On learnability of E–stable equilibria," Computing in Economics and Finance 2006 451, Society for Computational Economics. [Downloadable!]
  5. 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!]
  6. Gaetano Gaballo, 2008. "Interactive Learning and Behavioral Sunspots," Department of Economic Policy, Finance and Development (DEPFID) University of Siena 1008, Department of Economic Policy, Finance and Development (DEPFID), University of Siena. [Downloadable!]
  7. 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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  8. 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!]
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