Finite Horizon Learning
William Branch (),
George Evans () and
Bruce McGough ()
University of Oregon Economics Department Working Papers from University of Oregon Economics Department
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)
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Working Paper: Finite Horizon Learning (2012)
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