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Testing Multiple Forecasters

  • Feinberg, Yossi

    (Stanford U)

  • Stewart, Colin

    (Yale U)

We consider a cross-calibration test of predictions by multiple potential experts in a stochastic environment. This test checks whether each expert is calibrated conditional on the predictions made by other experts. We show that this test is good in the sense that a true expert--one informed of the true distribution of the process--is guaranteed to pass the test no matter what the other potential experts do, and false experts will fail the test on all but a small (category one) set of true distributions. Furthermore, even when there is no true expert present, a test similar to cross-calibration cannot be simultaneously manipulated by multiple false experts, but at the cost of failing some true experts. In contrast, tests that allow false experts to make precise predictions can be jointly manipulated.

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File URL: http://gsbapps.stanford.edu/researchpapers/library/RP1957.pdf
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Paper provided by Stanford University, Graduate School of Business in its series Research Papers with number 1957.

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Date of creation: Jan 2007
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Handle: RePEc:ecl:stabus:1957
Contact details of provider: Postal: Stanford University, Stanford, CA 94305-5015
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  1. Drew Fudenberg & David K. Levine, 1997. "Conditional Universal Consistency," Levine's Working Paper Archive 471, David K. Levine.
  2. Vladimir Vovk & Glenn Shafer, 2005. "Good randomized sequential probability forecasting is always possible," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 747-763.
  3. Alvaro Sandroni, 2003. "The reproducible properties of correct forecasts," International Journal of Game Theory, Springer, vol. 32(1), pages 151-159, December.
  4. Nabil I. Al-Najjar & Jonathan Weinstein, 2008. "Comparative Testing of Experts," Econometrica, Econometric Society, vol. 76(3), pages 541-559, 05.
  5. Alvaro Sandroni & Wojciech Olszewski, 2008. "Strategic Manipulation of Empirical Tests," PIER Working Paper Archive 08-015, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  6. Ehud Kalai, 1995. "Calibrated Forecasting and Merging," Discussion Papers 1144R, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  7. Eddie Dekel & Yossi Feinberg, 2006. "Non-Bayesian Testing of a Stochastic Prediction," Review of Economic Studies, Oxford University Press, vol. 73(4), pages 893-906.
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