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Manipulability of Future-Independent Tests

Author

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  • Alvaro Sandroni

    () (Department of Economics, University of Pennsylvania)

  • Wojciech Olszewski

    () (Department of Economics, Northwestern University)

Abstract

The difficulties in properly anticipating key economic variables may encourage decision makers to rely on experts’ forecasts. Professional forecasters, however, may not be reliable and so their forecasts must be empirically tested. This may induce experts to forecast strategically in order to pass the test. A test can be ignorantly passed if a false expert, with no knowledge of the data generating process, can pass the test. Many tests that are unlikely to reject correct forecasts can be ignorantly passed. Tests that cannot be ignorantly passed do exist, but these tests must make use of predictions contingent on data not yet observed at the time the forecasts are rejected. Such tests cannot be run if forecasters report only the probability of the next period’s events on the basis of the actually observed data. This result shows that it is difficult to dismiss false, but strategic, experts who know how theories are tested. This result also shows an important role that can be played by predictions contingent on data not yet observed.

Suggested Citation

  • Alvaro Sandroni & Wojciech Olszewski, 2008. "Manipulability of Future-Independent Tests," PIER Working Paper Archive 08-014, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  • Handle: RePEc:pen:papers:08-014
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    References listed on IDEAS

    as
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    Citations

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    Cited by:

    1. DeMarzo, Peter M. & Kremer, Ilan & Mansour, Yishay, 2016. "Robust option pricing: Hannan and Blackwell meet Black and Scholes," Journal of Economic Theory, Elsevier, vol. 163(C), pages 410-434.
    2. Yossi Feinberg & Nicolas Lambert, 2015. "Mostly calibrated," International Journal of Game Theory, Springer;Game Theory Society, vol. 44(1), pages 153-163, February.
    3. Wojciech Olszewski & Alvaro Sandroni, 2006. "Strategic Manipulation of Empirical Tests," Discussion Papers 1425, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    4. Colin, Stewart, 2011. "Nonmanipulable Bayesian testing," Journal of Economic Theory, Elsevier, vol. 146(5), pages 2029-2041, September.
    5. Feinberg, Yossi & Lambert, Nicolas S., 2011. "Mostly Calibrated," Research Papers 2090, Stanford University, Graduate School of Business.
    6. Olszewski, Wojciech, 2015. "Calibration and Expert Testing," Handbook of Game Theory with Economic Applications, Elsevier.
    7. Lambert, Nicolas S. & Langford, John & Wortman Vaughan, Jennifer & Chen, Yiling & Reeves, Daniel M. & Shoham, Yoav & Pennock, David M., 2015. "An axiomatic characterization of wagering mechanisms," Journal of Economic Theory, Elsevier, vol. 156(C), pages 389-416.
    8. Alvaro Sandroni & Wojciech Olszewski, 2008. "Falsifiability," PIER Working Paper Archive 08-016, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    9. Al-Najjar, Nabil I. & Sandroni, Alvaro & Smorodinsky, Rann & Weinstein, Jonathan, 2010. "Testing theories with learnable and predictive representations," Journal of Economic Theory, Elsevier, vol. 145(6), pages 2203-2217, November.
    10. Freedman, David A., 2009. "Diagnostics cannot have much power against general alternatives," International Journal of Forecasting, Elsevier, vol. 25(4), pages 833-839, October.
    11. Boleslavsky, Raphael & Lewis, Tracy R., 2016. "Evolving influence: Mitigating extreme conflicts of interest in advisory relationships," Games and Economic Behavior, Elsevier, vol. 98(C), pages 110-134.

    More about this item

    Keywords

    Testing Strategic Experts;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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