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Finite Horizon Learning

  • William A. Branch

    ()

    (University of California, Irvine)

  • George W. Evans

    ()

    (University of Oregon Economics Department and University of St. Andrews)

  • Bruce McGough

    ()

    (Oregon State University)

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.

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Paper provided by University of Oregon Economics Department in its series University of Oregon Economics Department Working Papers with number 2010-15.

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Length: 23
Date of creation: 14 Nov 2010
Date of revision:
Handle: RePEc:ore:uoecwp:2010-15
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  1. Kaushik Mitra & James Bullard, . "Learning About Monetary Policy Rules," Discussion Papers 00/41, Department of Economics, University of York.
  2. Preston, Bruce, 2005. "Learning about Monetary Policy Rules when Long-Horizon Expectations Matter," MPRA Paper 830, University Library of Munich, Germany.
  3. 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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