Stability Analysis of Heterogeneous Learning in Self-Referential Linear Stochastic Models

Chryssi Giannitsarou

No 46, Computing in Economics and Finance 2001 from Society for Computational Economics

Abstract: There is by now a large literature characterizing conditions under which learning schemes converge to rational expectations equilibria (REEs). A number of authors have claimed that these results are dependent on the assumption of homogeneous agents and homogeneous learning. We study the local stability of REEs under heterogeneous adaptive learning, for the broad class of self-referential linear stochastic models. We introduce three types of heterogeneity related to the way agents learn: different expectations, different degrees of inertia in updating, and different learning algorithms. We provide general conditions for local stability of an REE. Even though in general, hetereogeneity may lead to different stability conditions, we provide applications to various economic models where the stability conditions are identical to the conditions required under aggregation. This suggests that heterogeneity may affect the local stability of the learning scheme but that in most models aggregation works locally.

Keywords: adaptive learning; stability; heterogeneity (search for similar items in EconPapers)
JEL-codes: C82 D83 (search for similar items in EconPapers)
Date: 2001-04-01
References: Add references at CitEc
Citations: View citations in EconPapers (2)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf1:46

Access Statistics for this paper

More papers in Computing in Economics and Finance 2001 from Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().