ADAPTIVE LEARNING IN REGIME-SWITCHING MODELS

We study adaptive learning in economic environments subject to recurring structural change. Stochastically evolving institutional and policymaking features can be described by regime-switching models with parameters that evolve according to finite state Markov processes. We demonstrate that in nonli...

Full description

Saved in:
Bibliographic Details
Published inMacroeconomic dynamics Vol. 17; no. 5; pp. 998 - 1022
Main Authors Branch, William A., Davig, Troy, McGough, Bruce
Format Journal Article
LanguageEnglish
Published New York, USA Cambridge University Press 01.07.2013
Cambridge Univ. Press
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:We study adaptive learning in economic environments subject to recurring structural change. Stochastically evolving institutional and policymaking features can be described by regime-switching models with parameters that evolve according to finite state Markov processes. We demonstrate that in nonlinear models of this form, the presence of sunspot equilibria implies two natural schemes for learning the conditional means of endogenous variables: under mean value learning, agents condition on a sunspot variable that captures the self-fulfilling serial correlation in the equilibrium, whereas under vector autoregression learning (VAR learning), the self-fulfilling serial correlation must be learned. We show that an intuitive condition ensures convergence to a regime-switching rational expectations equilibrium. However, the stability of sunspot equilibria, when they exist, depends on whether agents adopt mean value or VAR learning: coordinating on sunspot equilibria via a VAR learning rule is not possible. To illustrate these phenomena, we develop results for an overlapping-generations model and a New Keynesian model.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:1365-1005
1469-8056
DOI:10.1017/S1365100511000800