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Expectation formation in finance and macroeconomics: A review of new experimental evidence

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  • Bao, Te
  • Hommes, Cars
  • Pei, Jiaoying

Abstract

This paper reviews the recent development and new findings of the literature on learning-to-forecast experiments (LtFEs). In general, the stylized finding in the typical LtFEs, namely the rapid convergence to the rational expectations equilibrium in negative feedback markets and persistent bubbles and crashes in positive feedback markets, is a robust result against several deviations from the baseline design (e.g., number of subjects in each market, price prediction versus quantity decision, short term versus long term predictions, predicting price or returns). Recent studies also find a high level of consistency between findings from forecasting data from the laboratory and the field, and forecasting accuracy crucially depends on the complexity of the task.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:beexfi:v:32:y:2021:i:c:s2214635021001350
    DOI: 10.1016/j.jbef.2021.100591
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    2. Bao, Te & Corgnet, Brice & Hanaki, Nobuyuki & Riyanto, Yohanes E. & Zhu, Jiahua, 2023. "Predicting the unpredictable: New experimental evidence on forecasting random walks," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    3. Mignot, Sarah & Tramontana, Fabio & Westerhoff, Frank H., 2023. "Complex dynamics in a nonlinear duopoly model with heuristic expectation formation and learning behavior," BERG Working Paper Series 187, Bamberg University, Bamberg Economic Research Group.
    4. Leonid Serkov & Sergey Krasnykh, 2023. "The Specific Behavior of Economic Agents with Heterogeneous Expectations in the New Keynesian Model with Rigid Prices and Wages," Mathematics, MDPI, vol. 11(4), pages 1-17, February.
    5. de Jong, Johan & Sonnemans, Joep & Tuinstra, Jan, 2022. "The effect of futures markets on the stability of commodity prices," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 176-211.
    6. Alfarano, Simone & Camacho-Cuena, Eva & Colasante, Annarita & Ruiz-Buforn, Alba, 2022. "The effect of time-varying fundamentals in Learning-to-Forecast Experiments," MPRA Paper 113086, University Library of Munich, Germany.
    7. Filippo Gusella & Giorgio Ricchiuti, 2022. "A State-Space Approach for Time-Series Prediction of an Heterogeneous Agent Model," Working Papers - Economics wp2022_20.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.

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    19. Kryvtsov, Oleksiy & Petersen, Luba, 2021. "Central bank communication that works: Lessons from lab experiments," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 760-780.
    20. Cars Hommes & Domenico Massaro & Isabelle Salle, 2019. "Monetary And Fiscal Policy Design At The Zero Lower Bound: Evidence From The Lab," Economic Inquiry, Western Economic Association International, vol. 57(2), pages 1120-1140, April.

    More about this item

    Keywords

    Learning to forecast experiment; Experimental finance; Rational expectations; Bubbles and crashes; Behavioral finance;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G40 - Financial Economics - - Behavioral Finance - - - General

    Statistics

    Access and download statistics

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