Finite Horizon Learning
William Branch (),
George Evans () and
Bruce McGough ()
No 201204, CDMA Working Paper Series from Centre for Dynamic Macroeconomic Analysis
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 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; adaptive learning; Ramsey model. (search for similar items in EconPapers)
JEL-codes: D83 D84 D91 E32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge and nep-for
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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:san:cdmawp:1204
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