DP16822 The Unattractiveness of Indeterminate Dynamic Equilibria

Author(s):	Julian Ashwin, Paul Beaudry, Martin Ellison
Publication Date:	December 2021
Keyword(s):	Indeterminacy, Machine Learning, multiple equilibria, neural networks
JEL(s):
Programme Areas:	Monetary Economics and Fluctuations
Link to this Page:	cepr.org/active/publications/discussion_papers/dp.php?dpno=16822

Macroeconomic forces that generate multiple equilibria often support locally-indeterminate dynamic equilibria in which a continuum of perfect foresight paths converge towards the same steady state. The set of rational expectations equilibria (REE) in such environments can be very large, although the relevance of many of them has been questioned on the basis that they may not be learnable. In this paper we document the existence of a learnable REE in such situations. However, we show that the dynamics of this learnable REE do not resemble perturbations around any of the convergent perfect foresight paths. Instead, the learnable REE treats the locally-indeterminate steady state as unstable, in contrast to it resembling a stable attractor under perfect foresight.