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LEARNABILITY OF E–STABLE EQUILIBRIA

Published online by Cambridge University Press:  03 April 2013

Atanas Christev  and
Sergey Slobodyan
Atanas Christev*
Affiliation:
Heriot-Watt University and IZA
Sergey Slobodyan
Affiliation:
CERGE-EI
*
Address correspondence to: Atanas Christev, Department of Economics, Mary Burton Building, Room 1.4, Heriot-Watt University, Riccarton, Edinburgh EH14 4AS, UK; e-mail: A.Christev@hw.ac.uk.
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Abstract

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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.

Keywords

Adaptive LearningExpectational StabilityStochastic GradientSpeed of Convergence

Information

Type
Articles
Information
Macroeconomic Dynamics , Volume 18 , Issue 5 , July 2014 , pp. 959 - 984
Copyright
Copyright © Cambridge University Press 2013 

References

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