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Learnability Of E–Stable Equilibria

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  • Christev, Atanas
  • Slobodyan, Sergey

Abstract

If private sector agents update their beliefs with a learning algorithm other than recursive least squares, expectational stability or learnability of rational expectations equilibria (REE) is not guaranteed. Monetary policy under commitment, with a determinate and E-stable REE, may not imply robust learning stability of such equilibria if the RLS speed of convergence is slow. In this paper, we propose a refinement of E-stability conditions that allows us to select equilibria more robust to specification of the learning algorithm within the RLS/SG/GSG class. E-stable equilibria characterized by faster speed of convergence under RLS learning are learnable with SG or generalized SG algorithms as well.

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  • Christev, Atanas & Slobodyan, Sergey, 2014. "Learnability Of E–Stable Equilibria," Macroeconomic Dynamics, Cambridge University Press, vol. 18(5), pages 959-984, July.
    • Handle: RePEc:cup:macdyn:v:18:y:2014:i:05:p:959-984_00
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    Cited by:

    1. Araújo, Eurilton, 2016. "Determinacy and learnability of equilibrium in a small-open economy with sticky wages and prices," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 16-32.
    2. Berardi, Michele, 2015. "Learning and coordination with dispersed information," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 19-33.
    3. Berardi, Michele & Galimberti, Jaqueson K., 2019. "Smoothing-Based Initialization For Learning-To-Forecast Algorithms," Macroeconomic Dynamics, Cambridge University Press, vol. 23(3), pages 1008-1023, April.

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