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On learnability of E–stable equilibria

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  • Sergey Slobodyan

    (CERGE-EI, Czech Republic)

  • Atanas Christev

    (Heriot Watt University, UK)

Abstract

While under recursive least squares learning the dynamics of the economy converges to rational expectations equilibria (REE) which are E–stable, some recent examples propose that E–stability is not a sufficient condition for learnability. In this paper, we provide some further evidence on the conditions under which E–stability of a particular equilibrium might fail to imply its stochastic gradient (SG) or generalized SG learnability. We also claim that the requirement on the speed of convergence of the learning process imposed by [4] also implies that E–stable equilibria are likely to be GSG learnable. We show this in a simple â€New Keneysian†model of optimal monetary policy design in which the stability of REE under SG learning. In this case, the paper gives the conditions which are necessary for reversal of learnability

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File URL: http://repec.org/sce2006/up.30186.1141164474.pdf
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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 451.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:451

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Keywords: Adaptive learning; E–stability; stochastic gradient; learnability;

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  1. Giuseppe Ferrero, 2004. "Monetary Policy and the Transition to Rational Expectations," Econometric Society 2004 North American Summer Meetings 101, Econometric Society.
  2. George W. Evans & Seppo Honkapohja & Noah Williams, 2005. "Generalized Stochastic Gradient Learning," NBER Technical Working Papers 0317, National Bureau of Economic Research, Inc.
  3. Barucci, Emilio & Landi, Leonardo, 1997. "Least mean squares learning in self-referential linear stochastic models," Economics Letters, Elsevier, vol. 57(3), pages 313-317, December.
  4. Giannitsarou, Chryssi, 2005. "E-Stability Does Not Imply Learnability," Macroeconomic Dynamics, Cambridge University Press, vol. 9(02), pages 276-287, April.
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