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Finite Horizon Learning

Author

Listed:
  • 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)

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 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.

Suggested Citation

  • William A. Branch & George W. Evans & Bruce McGough, 2010. "Finite Horizon Learning," University of Oregon Economics Department Working Papers 2010-15, University of Oregon Economics Department.
  • Handle: RePEc:ore:uoecwp:2010-15
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    File URL: http://economics.uoregon.edu/papers/UO-2010-15_Branch_Finite_Horizon_learning.pdf
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    References listed on IDEAS

    as
    1. Bullard, James & Mitra, Kaushik, 2002. "Learning about monetary policy rules," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1105-1129, September.
    2. Evans, George W. & Honkapohja, Seppo & Mitra, Kaushik, 2009. "Anticipated fiscal policy and adaptive learning," Journal of Monetary Economics, Elsevier, vol. 56(7), pages 930-953, October.
    3. Roger Guesnerie, 2005. "Assessing Rational Expectations 2: "Eductive" Stability in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262072580, January.
    4. 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.
    5. Bray, Margaret M & Savin, Nathan E, 1986. "Rational Expectations Equilibria, Learning, and Model Specification," Econometrica, Econometric Society, vol. 54(5), pages 1129-1160, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Isabelle SALLE (GREThA, CNRS, UMR 5113) & Martin ZUMPE (GREThA, CNRS, UMR 5113) & Murat YILDIZOGLU (GREThA, CNRS, UMR 5113) & Marc-Alexandre SENEGAS (GREThA, CNRS, UMR 5113), 2012. "Modelling Social Learning in an Agent-Based New Keynesian Macroeconomic Model," Cahiers du GREThA 2012-20, Groupe de Recherche en Economie Théorique et Appliquée.
    2. Tesfaselassie, Mewael F., 2014. "Trend growth and learning about monetary policy rules," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 241-256.
    3. Hommes, Cars & Zhu, Mei, 2014. "Behavioral learning equilibria," Journal of Economic Theory, Elsevier, vol. 150(C), pages 778-814.
    4. Evans, George W. & Honkapohja, Seppo, 2011. "Learning as a Rational Foundation for Macroeconomics and Finance," CEPR Discussion Papers 8340, C.E.P.R. Discussion Papers.
    5. Liam Graham, 2011. "Individual rationality, model-consistent expectations and learning," CDMA Working Paper Series 201112, Centre for Dynamic Macroeconomic Analysis.

    More about this item

    Keywords

    Planning horizon; Expectations; Learning dynamics;

    JEL classification:

    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium

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