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Long-run expectations in a learning-to-forecast experiment

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  • Annarita Colasante
  • Simone Alfarano
  • Eva Camacho
  • Mauro Gallegati

Abstract

We conduct a Learning to Forecast Experiment using a novel setting in which we elicit subjects’ short- and long-run expectations on the future price of an asset. We find that: (i) the rational expectations equilibrium is not a meaningful description for the whole time spectrum of subjects’ expectations; (ii) they are, instead, better described by an anchor-and-adjustment learning scheme; (iii) subjects exhibit a higher degree of heterogeneity in their long-run expectations vis-à-vis short-run expectations.

Suggested Citation

  • Annarita Colasante & Simone Alfarano & Eva Camacho & Mauro Gallegati, 2018. "Long-run expectations in a learning-to-forecast experiment," Applied Economics Letters, Taylor & Francis Journals, vol. 25(10), pages 681-687, June.
    • Handle: RePEc:taf:apeclt:v:25:y:2018:i:10:p:681-687
    DOI: 10.1080/13504851.2017.1355537
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    1. N. Gregory Mankiw & Ricardo Reis, 2002. "Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 117(4), pages 1295-1328.
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    3. Michael Woodford, 2007. "The Case for Forecast Targeting as a Monetary Policy Strategy," Journal of Economic Perspectives, American Economic Association, vol. 21(4), pages 3-24, Fall.
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    6. Heemeijer, Peter & Hommes, Cars & Sonnemans, Joep & Tuinstra, Jan, 2009. "Price stability and volatility in markets with positive and negative expectations feedback: An experimental investigation," Journal of Economic Dynamics and Control, Elsevier, vol. 33(5), pages 1052-1072, May.
    7. Stephen Morris & Hyun Song Shin, 2002. "Social Value of Public Information," American Economic Review, American Economic Association, vol. 92(5), pages 1521-1534, December.
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    Cited by:

    1. Anita Kopányi-Peuker & Matthias Weber & Lauren Cohen, 2021. "Experience Does Not Eliminate Bubbles: Experimental Evidence," The Review of Financial Studies, Society for Financial Studies, vol. 34(9), pages 4450-4485.
    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. Makarewicz, Tomasz, 2021. "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 626-673.
    4. 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.
    5. Annarita Colasante & Simone Alfarano & Eva Camacho-Cuena, 2019. "The term structure of cross-sectional dispersion of expectations in a Learning-to-Forecast Experiment," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 491-520, September.
    6. Morone, Andrea & Caferra, Rocco, 2020. "Inequalities in financial markets: Evidences from a laboratory experiment," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 88(C).
    7. Annarita Colasante & Simone Alfarano & Eva Camacho-Cuena & Mauro Gallegati, 2020. "Long-run expectations in a learning-to-forecast experiment: a simulation approach," Journal of Evolutionary Economics, Springer, vol. 30(1), pages 75-116, January.
    8. 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.
    9. 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.
    10. Biondo, Alessio Emanuele, 2018. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-21.
    11. Makarewicz, Tomasz, 2019. "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," BERG Working Paper Series 141, Bamberg University, Bamberg Economic Research Group.
    12. Zhou Lu & Te Bao & Xiaohua Yu, 2021. "Gender and Bubbles in Experimental Markets with Positive and Negative Expectation Feedback," Computational Economics, Springer;Society for Computational Economics, vol. 57(4), pages 1307-1326, April.
    13. 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.

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    More about this item

    JEL classification:

    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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