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Finite Horizon Learning

William Branch (), George Evans () and Bruce McGough ()

No 201204, CDMA Working Paper Series from Centre for Dynamic Macroeconomic Analysis

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 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)
Date: 2012-01-25
New Economics Papers: this item is included in nep-dge and nep-for
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Related works:
Working Paper: Finite Horizon Learning (2012) Downloads
Working Paper: Finite Horizon Learning (2010) Downloads
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