Learning in a large square economy
James Bullard and
John Duffy ()
No 1994-013, Working Papers from Federal Reserve Bank of St. Louis
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.
Keywords: Consumption; (Economics) (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:fip:fedlwp:1994-013
Ordering information: This working paper can be ordered from
Access Statistics for this paper
More papers in Working Papers from Federal Reserve Bank of St. Louis
Contact information at EDIRC.
Series data maintained by Anna Xiao ().