On learnability of E–stable equilibria

Sergey Slobodyan and Atanas Christev

No 451, Computing in Economics and Finance 2006 from Society for Computational Economics

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 †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

Keywords: Adaptive learning; E–stability; stochastic gradient; learnability (search for similar items in EconPapers)
JEL-codes: C62 D83 E17 (search for similar items in EconPapers)
Date: 2006-07-04
New Economics Papers: this item is included in nep-evo and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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