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Information Aggregation in a Prediction Market for Climate Outcomes

Listed author(s):
  • Elmira Aliakbari

    (Fraser Institute, Vancouver BC, Canada)

  • Ross McKitrick

    ()

    (Department of Economics, University of Guelph, Guelph ON Canada)

Two forms of uncertainty in climate policy are the wide range of estimated marginal costs and uncertainty over credibility of rival information sources. We show how a recently-proposed solution to the first problem also addresses the second. The policy is an emissions tax tied to average temperatures, coupled with permits that exempt the emitter from paying the tax in a future year. It has been shown that the resulting tax path will be correlated with future marginal damages. It has been conjectured that the permit prices will yield unbiased forecasts of the climate, which, if true, would address the second uncertainty. We confirm the conjecture by showing a trading mechanism that converges on unbiased forecasts if traders are risk-neutral. Risk aversion slows down but does not prevent convergence. We also show that the forecasts are more likely to be sufficient statistics the stronger the consensus on climate science.

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File URL: http://www.uoguelph.ca/economics/repec/workingpapers/2017/2017-02.pdf
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Paper provided by University of Guelph, Department of Economics and Finance in its series Working Papers with number 1702.

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Length: 33 pages
Date of creation: 2017
Handle: RePEc:gue:guelph:2017-02
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Guelph, Ontario, N1G 2W1

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Web page: https://www.uoguelph.ca/economics/

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  1. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
  2. Nordhaus, William D, 1991. "To Slow or Not to Slow: The Economics of the Greenhouse Effect," Economic Journal, Royal Economic Society, vol. 101(407), pages 920-937, July.
  3. McKitrick, Ross, 2011. "A simple state-contingent pricing rule for complex intertemporal externalities," Energy Economics, Elsevier, vol. 33(1), pages 111-120, January.
  4. Tol, Richard S.J., 2014. "Quantifying the consensus on anthropogenic global warming in the literature: A re-analysis," Energy Policy, Elsevier, vol. 73(C), pages 701-705.
  5. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
  6. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
  7. Kelly, David L. & Kolstad, Charles D., 1999. "Bayesian learning, growth, and pollution," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 491-518, February.
  8. Berg, Joyce E. & Nelson, Forrest D. & Rietz, Thomas A., 2008. "Prediction market accuracy in the long run," International Journal of Forecasting, Elsevier, vol. 24(2), pages 285-300.
  9. H. Henry Cao., 1995. "Imperfect Competition in Securities Markets with Diversely Informed Traders," Research Program in Finance Working Papers RPF-258, University of California at Berkeley.
  10. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
  11. Nordhaus, William D., 1993. "Rolling the 'DICE': an optimal transition path for controlling greenhouse gases," Resource and Energy Economics, Elsevier, vol. 15(1), pages 27-50, March.
  12. Foster, F Douglas & Viswanathan, S, 1996. " Strategic Trading When Agents Forecast the Forecasts of Others," Journal of Finance, American Finance Association, vol. 51(4), pages 1437-1478, September.
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