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Learning in a large square economy

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Abstract

Learning is introduced into a sequence of large square endowment economies indexed by n, in which agents live n periods. Young agents need to forecast n - 1 periods ahead in these models in order to make consumption decisions, and thus these models constitute multi-step ahead systems. Real time learning is introduced via least squares. The systems studied in this paper are sometimes locally convergent when n = 2,3 but are never locally convergent when . Because the economies studied are analogous, nonconvergence can be attributed solely to the multi-step ahead nature of the forecast problem faced by the agents. We interpret this result as suggesting that beliefs-outcomes interaction may be an important element in explaining actual dynamics in general equilibrium systems of this type.

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  • James B. Bullard & John Duffy, 1993. "Learning in a large square economy," Working Papers 1994-013, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:1994-013
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    Cited by:

    1. Elder, Erick, 1999. "Dynamic Fiscal Policy with Regime-Duration Uncertainty: The Tax-Cut Case," Journal of Macroeconomics, Elsevier, vol. 21(1), pages 29-55, January.

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