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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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    2. Bao, Te & Hommes, Cars & Pei, Jiaoying, 2021. "Expectation formation in finance and macroeconomics: A review of new experimental evidence," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    3. Evans, George W. & Hommes, Cars & McGough, Bruce & Salle, Isabelle, 2022. "Are long-horizon expectations (de-)stabilizing? Theory and experiments," Journal of Monetary Economics, Elsevier, vol. 132(C), pages 44-63.
    4. Isabelle Salle & Murat Yildizoglu & Martin Zumpe & Marc-Alexandre Sénégas, 2012. "Modelling social learning in an Agent-Based new keynesian macroeconomic model," Post-Print hal-00779045, HAL.
    5. Hommes, Cars, 2018. "Behavioral & experimental macroeconomics and policy analysis: a complex systems approach," Working Paper Series 2201, European Central Bank.
    6. Michael Woodford, 2019. "Monetary Policy Analysis When Planning Horizons Are Finite," NBER Macroeconomics Annual, University of Chicago Press, vol. 33(1), pages 1-50.
    7. Chatterjee, Pratiti & Milani, Fabio, 2020. "Perceived uncertainty shocks, excess optimism-pessimism, and learning in the business cycle," Journal of Economic Behavior & Organization, Elsevier, vol. 179(C), pages 342-360.
    8. 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.
    9. Goy, Gavin & Hommes, Cars & Mavromatis, Kostas, 2022. "Forward guidance and the role of central bank credibility under heterogeneous beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 1240-1274.
    10. Hommes, Cars & Zhu, Mei, 2014. "Behavioral learning equilibria," Journal of Economic Theory, Elsevier, vol. 150(C), pages 778-814.
    11. Gavin Goy & Cars Homme & Kostas Mavromatis, 2018. "Forward Guidance and the Role of Central Bank Credibility," DNB Working Papers 614, Netherlands Central Bank, Research Department.

    More about this item

    Keywords

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    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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