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LEARNING DYNAMICS AND NONLINEAR MISSPECIFICATION IN AN ARTIFICIAL FINANCIAL MARKET

Published online by Cambridge University Press:  30 October 2009

Christophre Georges  and
John C. Wallace
Christophre Georges*
Affiliation:
Hamilton College
John C. Wallace
Affiliation:
Hamilton College
*
Address correspondence to: Christophre Georges, Department of Economics, Hamilton College, Clinton, NY 13323, USA; e-mail: cgeorges@hamilton.edu; URL: http://academics.hamilton.edu/economics/cgeorges/.
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Abstract

In this paper, we explore the consequence of learning to forecast in a very simple environment. Agents have bounded memory and incorrectly believe that there is nonlinear structure underlying the aggregate time series dynamics. Under social learning with finite memory, agents may be unable to learn the true structure of the economy and rather may chase spurious trends, destabilizing the actual aggregate dynamics. We explore the degree to which agents' forecasts are drawn toward a minimal state variable learning equilibrium as well as a weaker long-run consistency condition.

Keywords

LearningExpectationsAgent-Based Model

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Type
Articles
Information
Macroeconomic Dynamics , Volume 13 , Issue 5 , November 2009 , pp. 625 - 655
Copyright
Copyright © Cambridge University Press 2009

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