Finite Horizon Learning

William Branch, George Evans and Bruce McGough ()

No 2012-16, SIRE Discussion Papers from Scottish Institute for Research in Economics (SIRE)

Abstract: Incorporating adaptive learning into macroeconomics requires assumptions about how agents incorporate their forecasts into their decision-making. We develop a theory of bounded rationality that we call finite-horizon learning. This approach generalizes the two existing benchmarks in the literature: Eulerequation learning, which assumes that consumption decisions are made to satisfy the one-step-ahead perceived Euler equation; and infinite-horizon learning, in which consumption today is determined optimally from an infinite-horizon optimization problem with given beliefs. In our approach, agents hold a finite forecasting/planning horizon. We find for the Ramsey model that the unique rational expectations equilibrium is E-stable at all horizons. However, transitional dynamics can differ significantly depending upon the horizon.

Keywords: Planning horizon; bounded rationality; dynamic optimization; adpative learning; Ramsey Model (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://hdl.handle.net/10943/319
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
Working Paper: Finite Horizon Learning (2010)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:edn:sirdps:319

Access Statistics for this paper

More papers in SIRE Discussion Papers from Scottish Institute for Research in Economics (SIRE) 31 Buccleuch Place, EH8 9JT, Edinburgh. Contact information at EDIRC.
Bibliographic data for series maintained by Research Office ().