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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: D84 D83 E32 C61 C62 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cba
Date: 2010-11-14
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Working Paper: Finite Horizon Learning (2012) Downloads
Working Paper: Finite Horizon Learning (2012) Downloads
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