Working Paper
Learning in a large square economy
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.
https://doi.org/10.20955/wp.1994.013
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Provider: Federal Reserve Bank of St. Louis
Part of Series: Working Papers
Publication Date: 1993
Number: 1994-013