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Learning Dynamics And Nonlinear Misspecification In An Artificial Financial Market

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  • Georges, Christophre
  • Wallace, John C.

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.

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  • Georges, Christophre & Wallace, John C., 2009. "Learning Dynamics And Nonlinear Misspecification In An Artificial Financial Market," Macroeconomic Dynamics, Cambridge University Press, vol. 13(5), pages 625-655, November.
    • Handle: RePEc:cup:macdyn:v:13:y:2009:i:05:p:625-655_08
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    Cited by:

    1. Thomas Holtfort, 2019. "From standard to evolutionary finance: a literature survey," Management Review Quarterly, Springer, vol. 69(2), pages 207-232, June.
    2. Georges, Christophre, 2008. "Staggered updating in an artificial financial market," Journal of Economic Dynamics and Control, Elsevier, vol. 32(9), pages 2809-2825, September.
    3. Yong Shi & Bo Li & Guangle Du, 2021. "Pyramid scheme in stock market: a kind of financial market simulation," Papers 2102.02179, arXiv.org, revised Feb 2021.
    4. Georges, Christophre, 2008. "Bounded memory, overparameterized forecast rules, and instability," Economics Letters, Elsevier, vol. 98(2), pages 129-135, February.

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