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The term structure of cross-sectional dispersion of expectations in a Learning-to-Forecast Experiment

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  • Colasante, Annarita
  • Alfarano, Simone
  • Camacho-Cuena, Eva

Abstract

In this paper, we present the results of a Learning-to-Forecast Experiment (LtFE) eliciting short- as well as long-run expectations about the future price dynamics in markets with positive and negative expectations feedback. Comparing our results on short-run expectations to the LtFE literature, we prove that eliciting long-run expectations neither has an impact on the price dynamics nor on short-run expectations formation. In particular, we confirm that the Rational Expectation Equilibrium (REE) is a good benchmark only for the markets with negative feedback. Interestingly, our data show that the term structure of the cross-sectional dispersion of expectations is convex in positive feedback markets and concave in negative feedback markets. Differences in the slope of the term structure stem from diverse degrees of uncertainty on the evolution of prices in the two feedback systems: (i) in the negative feedback system, the convergence of the price to the REE mirrors into a tendency for coordination of long-run expectations around the fundamental value; (ii) conversely, the instability of the REE in the positive feedback system and the resulting oscillatory price dynamics are responsible for the diverging pattern of long-run expectations. Finally, we propose a new measure of heterogeneity of expectations based on the scaling of the dispersion of expectations over the forecasting horizon.

Suggested Citation

  • Colasante, Annarita & Alfarano, Simone & Camacho-Cuena, Eva, 2018. "The term structure of cross-sectional dispersion of expectations in a Learning-to-Forecast Experiment," MPRA Paper 84835, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:84835
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    References listed on IDEAS

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    Cited by:

    1. Annarita Colasante & Simone Alfarano & Eva Camacho-Cuena, 2020. "Heuristic Switching Model and Exploration-Exploitation Algorithm to Describe Long-Run Expectations in LtFEs: a Comparison," Computational Economics, Springer;Society for Computational Economics, vol. 56(3), pages 623-658, October.
    2. Simone Alfarano & Eva Camacho-Cuena & Annarita Colasante & Alba Ruiz-Buforn, 2024. "The effect of time-varying fundamentals in learning-to-forecast experiments," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 19(4), pages 619-647, October.
    3. Domenico Delli Gatti & Filippo Gusella & Giorgio Ricchiuti, 2024. "Endogenous vs Exogenous Instability: An Out-of-Sample Comparison," Working Papers - Economics wp2024_05.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    4. Pignataro, Giuseppe & Raggi, Davide & Pancotto, Francesca, 2024. "On the role of fundamentals, private signals, and beauty contests to predict exchange rates," International Journal of Forecasting, Elsevier, vol. 40(2), pages 687-705.
    5. Toshiaki Akinaga & Takanori Kudo & Kenju Akai, 2023. "Interaction between price and expectations in the jar-guessing experimental market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(3), pages 491-532, July.

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    More about this item

    Keywords

    Long-Run Expectations; Heterogeneous Expectations; Experiment; Coordination; Convergence; Learning-to-Forecast Experiment;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D30 - Microeconomics - - Distribution - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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