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Adaptive Learning with Nonlinear Dynamics Driven by Dependent Processes

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  • Kuan, Chung-Ming
  • White, Halbert

Abstract

The authors provide a convergence theory for adaptive learning algorithms useful for the study of learning by economic agents. Their results extend the framework of L. Ljung previously utilized by A. Marcet-T. J. Sargent and M. Woodford by permitting nonlinear laws of motion driven by stochastic processes that may exhibit moderate dependence, such as mixing and mixingale processes. The authors draw on previous work by H. J. Kushner and D. S. Clark to provide readily verifiable and/or interpretable conditions ensuring algorithm convergence, chosen for their suitability in the context of adaptive learning. Copyright 1994 by The Econometric Society.

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Bibliographic Info

Article provided by Econometric Society in its journal Econometrica.

Volume (Year): 62 (1994)
Issue (Month): 5 (September)
Pages: 1087-1114

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Handle: RePEc:ecm:emetrp:v:62:y:1994:i:5:p:1087-1114

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Cited by:
  1. Atanas Christev, 2007. "Learning Hyperinflations," Money Macro and Finance (MMF) Research Group Conference 2006 126, Money Macro and Finance Research Group.
  2. Alan Beggs, 2002. "On the Convergence of Reinforcement Learning," Economics Series Working Papers 96, University of Oxford, Department of Economics.
  3. Evans, George W. & Honkapohja, Seppo, 1998. "Convergence of learning algorithms without a projection facility," Journal of Mathematical Economics, Elsevier, vol. 30(1), pages 59-86, August.
  4. Heinemann, Maik & Lange, Carsten, 1997. "Modellierung von Preiserwartungen durch neuronale Netze," Hannover Economic Papers (HEP) dp-203, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  5. Timothy Kam, 2004. "Two-sided Learning and Optimal Monetary Policy in an Open Economy Model," Economics Discussion / Working Papers 04-07, The University of Western Australia, Department of Economics.
  6. Guidolin, Massimo & Timmermann, Allan, 2007. "Properties of equilibrium asset prices under alternative learning schemes," Journal of Economic Dynamics and Control, Elsevier, vol. 31(1), pages 161-217, January.
  7. Sergio Pastorello & Valentin Patilea & Éric Renault, 2003. "Iterative and Recursive Estimation in Structural Non-Adaptive Models," CIRANO Working Papers 2003s-08, CIRANO.
  8. Evans, George W. & Honkapohja, S., 1998. "Stochastic gradient learning in the cobweb model," Economics Letters, Elsevier, vol. 61(3), pages 333-337, December.
  9. Peter Woehrmann & Willi Semmler & Martin Lettau, . "Nonparametric Estimation of the Time-varying Sharpe Ratio in Dynamic Asset Pricing Models," IEW - Working Papers 225, Institute for Empirical Research in Economics - University of Zurich.
  10. Flam, Sjur Didrik, 1996. "Approaches to economic equilibrium," Journal of Economic Dynamics and Control, Elsevier, vol. 20(9-10), pages 1505-1522.
  11. Myles Callan & Eric Ghysels & Norman R. Swanson, 1998. "Monetary Policy Rules with Model and Data Uncertainty," CIRANO Working Papers 98s-40, CIRANO.
  12. Chen, Xiaohong & White, Halbert, 1998. "Nonparametric Adaptive Learning with Feedback," Journal of Economic Theory, Elsevier, vol. 82(1), pages 190-222, September.
  13. Gregoir, Stephane & Weill, Pierre-Olivier, 2007. "Restricted perception equilibria and rational expectation equilibrium," Journal of Economic Dynamics and Control, Elsevier, vol. 31(1), pages 81-109, January.
  14. Barucci, Emilio & Landi, Leonardo, 1996. "Speculative dynamics with bounded rationality learning," European Journal of Operational Research, Elsevier, vol. 91(2), pages 284-300, June.
  15. Heinemann, Maik, 2000. "Adaptive learning of rational expectations using neural networks," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 1007-1026, June.

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