Finite Horizon Learning
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.
|Date of creation:||14 Nov 2010|
|Contact details of provider:|| Postal: 1285 University of Oregon, 435 PLC, Eugene, OR 97403-1285|
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- Bullard, James & Mitra, Kaushik, 2002.
"Learning about monetary policy rules,"
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- George W. Evans & Seppo Honkapohja & Kaushik Mitra, 2007.
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CDMA Working Paper Series
200717, Centre for Dynamic Macroeconomic Analysis.
- George W. Evans & Seppo Honkapohja & Kaushik Mitra, 2007. "Anticipated Fiscal Policy and Adaptive Learning," University of Oregon Economics Department Working Papers 2007-5, University of Oregon Economics Department, revised 13 Dec 2008.
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830, University Library of Munich, Germany.
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