Finite Horizon Learning

William Branch, George Evans and Bruce McGough ()

University of Oregon Economics Department Working Papers from University of Oregon Economics Department

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: Euler equation 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 determine 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; Expectations; Learning dynamics (search for similar items in EconPapers)
JEL-codes: C61 C62 D83 D84 E32 (search for similar items in EconPapers)
Pages: 23
Date: 2010-11-14
New Economics Papers: this item is included in nep-cba
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Working Paper: Finite Horizon Learning (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:ore:uoecwp:2010-15

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