Bounded memory, overparameterized forecast rules, and instability

Authored by Christophre Georges

Date Published: 2008-02

DOI: 10.1016/i.econlet.2007.04.023

Sponsors: United States National Science Foundation (NSF)

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

We consider an environment in which traders with finite memory update their forecast rules at random intervals by OLS. In this context, overparameterization of the forecast rules can destabilize the learning dynamics. This instability tends to be attenuated by greater memory and less frequent updating. (c) 2007 Elsevier B.V. All rights reserved.

Tags

Agent-based modeling Learning Expectations