Finite Horizon Learning
William Branch (),
George Evans () and
Bruce McGough ()
No 2012-16, SIRE Discussion Papers from Scottish Institute for Research in Economics (SIRE)
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)
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Working Paper: Finite Horizon Learning (2012)
Working Paper: Finite Horizon Learning (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:edn:sirdps:319
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