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Least-Squares Learning and the Stability of Equilibria with Externalities

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  • Bartholomew J. Moore

Abstract

This paper studies the stability of competitive equilibria in a model of aggregate employment when the representative agent uses a least-squares forecasting procedure. It is shown that the Pareto inferior low employment steady state is always unstable under least-squares learning, even if it is stable under perfect foresight. The high employment steady state is stable under learning if and only if it is saddle point stable under perfect foresight. This weakens multiple equilibrium theories of coordination failure that purport to explain persistently high unemployment. The Pareto superior high employment steady state will be the focal point of individual forecasting.

Suggested Citation

  • Bartholomew J. Moore, 1993. "Least-Squares Learning and the Stability of Equilibria with Externalities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(1), pages 197-208.
  • Handle: RePEc:oup:restud:v:60:y:1993:i:1:p:197-208.
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    File URL: http://hdl.handle.net/10.2307/2297818
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    Cited by:

    1. Kiminori Matsuyama & Takaaki Takahashi, 1998. "Self-Defeating Regional Concentration," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 211-234.
    2. Gauthier, Stephane, 2001. "Learning and the saddle point property," Economics Letters, Elsevier, vol. 73(3), pages 339-343, December.
    3. Walde, Klaus, 2002. "The economic determinants of technology shocks in a real business cycle model," Journal of Economic Dynamics and Control, Elsevier, vol. 27(1), pages 1-28, November.

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