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Prediction Markets for Economic Forecasting

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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," CESifo Working Paper Series 3884, CESifo Group Munich.
  • Handle: RePEc:ces:ceswps:_3884
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    References listed on IDEAS

    as
    1. Erik Snowberg & Justin Wolfers & Eric Zitzewitz, 2007. "Partisan Impacts on the Economy: Evidence from Prediction Markets and Close Elections," The Quarterly Journal of Economics, Oxford University Press, vol. 122(2), pages 807-829.
    2. Göran Therborn & K.C. Ho, 2009. "Introduction," City, Taylor & Francis Journals, vol. 13(1), pages 53-62, March.
    3. Plott, Charles R & Sunder, Shyam, 1982. "Efficiency of Experimental Security Markets with Insider Information: An Application of Rational-Expectations Models," Journal of Political Economy, University of Chicago Press, vol. 90(4), pages 663-698, August.
    4. Refet Gürkaynak & Justin Wolfers, 2005. "Macroeconomic Derivatives: An Initial Analysis of Market-Based Macro Forecasts, Uncertainty, and Risk," NBER Chapters, in: NBER International Seminar on Macroeconomics 2005, pages 11-50, National Bureau of Economic Research, Inc.
    5. Erik Snowberg & Justin Wolfers, 2010. "Explaining the Favorite-Long Shot Bias: Is it Risk-Love or Misperceptions?," Journal of Political Economy, University of Chicago Press, vol. 118(4), pages 723-746, August.
    6. Plott, Charles R & Sunder, Shyam, 1988. "Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets," Econometrica, Econometric Society, vol. 56(5), pages 1085-1118, September.
    7. Robin Hanson & Ryan Oprea, 2009. "A Manipulator Can Aid Prediction Market Accuracy," Economica, London School of Economics and Political Science, vol. 76(302), pages 304-314, April.
    8. Erik Snowberg & Justin Wolfers & Eric Zitzewitz, 2011. "How Prediction Markets can Save Event Studies," CAMA Working Papers 2011-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Steven Gjerstad, 2004. "Risk Aversion, Beliefs, and Prediction Market Equilibrium," Microeconomics 0411002, University Library of Munich, Germany.
    10. Hanson, Robin & Oprea, Ryan & Porter, David, 2006. "Information aggregation and manipulation in an experimental market," Journal of Economic Behavior & Organization, Elsevier, vol. 60(4), pages 449-459, August.
    11. David Laster & Paul Bennett & In Sun Geoum, 1999. "Rational Bias in Macroeconomic Forecasts," The Quarterly Journal of Economics, Oxford University Press, vol. 114(1), pages 293-318.
    12. Colin Camerer, 1998. "Can asset markets be manipulated? A field experiment with racetrack betting," Natural Field Experiments 00222, The Field Experiments Website.
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    Citations

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    Cited by:

    1. von der Gracht, Heiko A. & Hommel, Ulrich & Prokesch, Tobias & Wohlenberg, Holger, 2016. "Testing weighting approaches for forecasting in a Group Wisdom Support System environment," Journal of Business Research, Elsevier, vol. 69(10), pages 4081-4094.
    2. 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.
    3. Refet S. Gürkaynak & Jonathan H. Wright, 2013. "Identification and Inference Using Event Studies," Manchester School, University of Manchester, vol. 81, pages 48-65, September.
    4. Linardi, Sera, 2017. "Accounting for noise in the microfoundations of information aggregation," Games and Economic Behavior, Elsevier, vol. 101(C), pages 334-353.
    5. 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.
    6. 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.
    7. Boleslavsky, Raphael & Kelly, David L. & Taylor, Curtis R., 2017. "Selloffs, bailouts, and feedback: Can asset markets inform policy?," Journal of Economic Theory, Elsevier, vol. 169(C), pages 294-343.
    8. Mueller-Frank, Manuel, 2014. "Does one Bayesian make a difference?," Journal of Economic Theory, Elsevier, vol. 154(C), pages 423-452.
    9. Lionel Page & Christoph Siemroth, 2018. "How much information is incorporated in financial asset prices? Experimental Evidence," QuBE Working Papers 054, QUT Business School.

    More about this item

    Keywords

    prediction markets; forecasting;

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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