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Individual Expectations And Aggregate Behavior In Learning-To-Forecast Experiments

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  • Hommes, Cars
  • Lux, Thomas

Abstract

Models with heterogeneous interacting agents explain macro phenomena through interactions at the micro level. We propose genetic algorithms as a model for individual expectations to explain aggregate market phenomena. The model explains all stylized facts observed in aggregate price fluctuations and individual forecasting behaviour in recent learning to forecast laboratory experiments with human subjects (Hommes et al. 2007), simultaneously and across different treatments.
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  • Hommes, Cars & Lux, Thomas, 2013. "Individual Expectations And Aggregate Behavior In Learning-To-Forecast Experiments," Macroeconomic Dynamics, Cambridge University Press, vol. 17(02), pages 373-401, March.
  • Handle: RePEc:cup:macdyn:v:17:y:2013:i:02:p:373-401_00
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    References listed on IDEAS

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    1. Klaus Adam, 2007. "Experimental Evidence on the Persistence of Output and Inflation," Economic Journal, Royal Economic Society, vol. 117(520), pages 603-636, April.
    2. Gary A. Zarkin, 1985. "Occupational Choice: An Application to the Market for Public School Teachers," The Quarterly Journal of Economics, Oxford University Press, vol. 100(2), pages 409-446.
    3. Arifovic, Jasmina, 1994. "Genetic algorithm learning and the cobweb model," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 3-28, January.
    4. Marimon, Ramon & Sunder, Shyam, 1994. "Expectations and Learning under Alternative Monetary Regimes: An Experimental Approach," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 4(1), pages 131-162, January.
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    10. Cars Hommes & Joep Sonnemans & Jan Tuinstra & Henk van de Velden, 2005. "Coordination of Expectations in Asset Pricing Experiments," Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 955-980.
    11. Lux, Thomas & Schornstein, Sascha, 2005. "Genetic learning as an explanation of stylized facts of foreign exchange markets," Journal of Mathematical Economics, Elsevier, vol. 41(1-2), pages 169-196, February.
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    14. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics,in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186 Elsevier.
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    16. Marco Casari, 2004. "Can Genetic Algorithms Explain Experimental Anomalies?," Computational Economics, Springer;Society for Computational Economics, vol. 24(3), pages 257-275, March.
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    Cited by:

    1. Arifovic, Jasmina & Karaivanov, Alexander, 2010. "Learning by doing vs. learning from others in a principal-agent model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 1967-1992, October.
    2. Colasante, Annarita & Alfarano, Simone & Camacho Cuena, Eva & Gallegati, Mauro, 2017. "Long-run expectations in a Learning-to-Forecast-Experiment: a simulation approach," MPRA Paper 77618, University Library of Munich, Germany.
    3. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    4. repec:spr:joevec:v:27:y:2017:i:5:d:10.1007_s00191-017-0511-y is not listed on IDEAS
    5. Biondo, Alessio Emanuele, 2017. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics Discussion Papers 2017-104, Kiel Institute for the World Economy (IfW).
    6. repec:zbw:ifweej:201820 is not listed on IDEAS
    7. Cars Hommes & Tomasz Makarewicz & Domenico Massaro & Tom Smits, 2017. "Genetic algorithm learning in a New Keynesian macroeconomic setup," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1133-1155, November.
    8. Tae-Seok Jang & Stephen Sacht, 2016. "Animal Spirits and the Business Cycle: Empirical Evidence from Moment Matching," Metroeconomica, Wiley Blackwell, vol. 67(1), pages 76-113, February.
    9. Antonio Doria, Francisco, 2011. "J.B. Rosser Jr. , Handbook of Research on Complexity, Edward Elgar, Cheltenham, UK--Northampton, MA, USA (2009) 436 + viii pp., index, ISBN 978 1 84542 089 5 (cased)," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 196-204, April.
    10. Guerci, E. & Kirman, A. & Moulet, S., 2014. "Learning to bid in sequential Dutch auctions," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 374-393.

    More about this item

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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