Learnability of e-stable equilibria

Atanas Christev, Sergey Slobodyan

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)


    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 recursuve least-squares speed
    of convergence is slow. The authors propose a refinement of E-stability conditions that 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.
    Original languageEnglish
    Pages (from-to)959-984
    Number of pages26
    JournalMacroeconomic Dynamics
    Issue number5
    Publication statusPublished - Jul 2014


    • Adaptive Learning
    • Expectational Stability
    • Speed of Convergence
    • Stochastic Gradient

    ASJC Scopus subject areas

    • Economics and Econometrics


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