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Prediction Markets for Economic Forecasting

Author

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  • Erik Snowberg
  • Justin Wolfers
  • Eric Zitzewitz
Abstract
Prediction markets--markets used to forecast future events--have been used to accurately forecast the outcome of political contests, sporting events, and, occasionally, economic outcomes. This chapter summarizes the latest research on prediction markets in order to further their utilization by economic forecasters. We show that prediction markets have a number of attractive features: they quickly incorporate new information, are largely efficient, and impervious to manipulation. Moreover, markets generally exhibit lower statistical errors than professional forecasters and polls. Finally, we show how markets can be used to both uncover the economic model behind forecasts, as well as test existing economic models.

Suggested Citation

  • Erik Snowberg & Justin Wolfers & Eric Zitzewitz, 2012. "Prediction Markets for Economic Forecasting," NBER Working Papers 18222, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:18222
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    2. Mueller-Frank, Manuel, 2014. "Does one Bayesian make a difference?," Journal of Economic Theory, Elsevier, vol. 154(C), pages 423-452.
    3. Heraud, Florian & Page, Lionel, 2024. "Does the left-digit bias affect prices in financial markets?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 20-29.
    4. Refet S. Gürkaynak & Jonathan H. Wright, 2013. "Identification and Inference Using Event Studies," Manchester School, University of Manchester, vol. 81, pages 48-65, September.
    5. Mikuláš Gangur & Miroslav Plevný, 2014. "Tools for Consumer Rights Protection in the Prediction of Electronic Virtual Market and Technological Changes," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 16(36), pages 578-578, May.
    6. Olkhov, Victor, 2023. "Economic complexity limits accuracy of price probability predictions by gaussian distributions," MPRA Paper 118373, University Library of Munich, Germany.
    7. Bo Cowgill & Eric Zitzewitz, 2015. "Corporate Prediction Markets: Evidence from Google, Ford, and Firm X," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1309-1341.
    8. Michael P. Clements, 2015. "Are Professional Macroeconomic Forecasters Able To Do Better Than Forecasting Trends?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(2-3), pages 349-382, March.
    9. Georg Graetz, 2019. "Labor Demand in the Past, Present, and Future," European Economy - Discussion Papers 114, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    10. Gabriela Victoria ANGHELACHE & Prof. Vladimir MODRAK & Madalina Gabriela ANGHEL & Marius POPOVICI, 2016. "Portfolio Management and Predictability," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(1), pages 59-63, January.
    11. Thomas Ferguson & Paul Jorgensen & Jie Chen, 2016. "How Money Drives US Congressional Elections," Working Papers Series 48, Institute for New Economic Thinking.
    12. Zhao, Yang & Yu, Min-Teh, 2020. "Predicting catastrophe risk: Evidence from catastrophe bond markets," Journal of Banking & Finance, Elsevier, vol. 121(C).
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    15. Constantin ANGHELACHE & Ioan Constantin DIMA & Mãdãlina-Gabriela ANGHEL, 2016. "Using the Autoregressive Model for the Economic Forecast during the Period 2014- 2018," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(1), pages 21-31, January.
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    19. Polson Nicholas G. & Stern Hal S., 2015. "The implied volatility of a sports game," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(3), pages 145-153, September.
    20. Lionel Page & Christoph Siemroth, 2021. "How Much Information Is Incorporated into Financial Asset Prices? Experimental Evidence," Review of Financial Studies, Society for Financial Studies, vol. 34(9), pages 4412-4449.
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    22. Linardi, Sera, 2017. "Accounting for noise in the microfoundations of information aggregation," Games and Economic Behavior, Elsevier, vol. 101(C), pages 334-353.

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    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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