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: 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.
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- Bruce Preston, 2005.
"Learning about Monetary Policy Rules when Long-Horizon Expectations Matter,"
International Journal of Central Banking,
International Journal of Central Banking, vol. 1(2), September.
- Preston, Bruce, 2005. "Learning about Monetary Policy Rules when Long-Horizon Expectations Matter," MPRA Paper 830, University Library of Munich, Germany.
- Bruce Preston, 2003. "Learning about monetary policy rules when long-horizon expectations matter," Working Paper 2003-18, Federal Reserve Bank of Atlanta.
- James Bullard & Kaushik Mitra, 2002.
"Learning about monetary policy rules,"
2000-001, Federal Reserve Bank of St. Louis.
- Roger Guesnerie, 2005. "Assessing Rational Expectations 2: "Eductive" Stability in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262072580, June.
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