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Manipulability of Future-Independent Tests

Author

Listed:
  • Wojciech Olszewski
  • Alvaro Sandroni

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. Copyright 2008 The Econometric Society.

Suggested Citation

  • Wojciech Olszewski & Alvaro Sandroni, 2008. "Manipulability of Future-Independent Tests," Econometrica, Econometric Society, vol. 76(6), pages 1437-1466, November.
  • Handle: RePEc:ecm:emetrp:v:76:y:2008:i:6:p:1437-1466
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    File URL: http://hdl.handle.net/10.3982/ECTA7428
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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. Colin, Stewart, 2011. "Nonmanipulable Bayesian testing," Journal of Economic Theory, Elsevier, vol. 146(5), pages 2029-2041, September.
    4. Olszewski, Wojciech, 2015. "Calibration and Expert Testing," Handbook of Game Theory with Economic Applications,, Elsevier.
    5. Dean P. Foster & H. Peyton Young, 2012. "A strategy-proof test of portfolio returns," Quantitative Finance, Taylor & Francis Journals, vol. 12(5), pages 671-683, March.
    6. Kavaler, Itay & Smorodinsky, Rann, 2019. "On comparison of experts," Games and Economic Behavior, Elsevier, vol. 118(C), pages 94-109.
    7. Mark Whitmeyer & Kun Zhang, 2023. "Redeeming Falsifiability?," Papers 2303.15723, arXiv.org, revised Jan 2026.
    8. 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.
    9. Itay Kavaler & Rann Smorodinsky, 2019. "A Cardinal Comparison of Experts," Papers 1908.10649, arXiv.org, revised Feb 2020.
    10. Yaron Azrieli & Christopher Chambers & Paul Healy & Nicolas Lambert, 2025. "Elicitability," Papers 2510.00879, arXiv.org.
    11. Wojciech Olszewski & Alvaro Sandroni, 2006. "Strategic Manipulation of Empirical Tests," Discussion Papers 1425, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    12. David Lagziel & Ehud Lehrer, 2021. "Transferable deposits as a screening mechanism," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 71(2), pages 483-504, March.
    13. Wojciech Olszewski & Alvaro Sandroni, 2009. "Strategic Manipulation of Empirical Tests," Mathematics of Operations Research, INFORMS, vol. 34(1), pages 57-70, February.
    14. 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.
    15. Alvaro Sandroni & Wojciech Olszewski, 2008. "Falsifiability," PIER Working Paper Archive 08-016, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    16. Freedman, David A., 2009. "Diagnostics cannot have much power against general alternatives," International Journal of Forecasting, Elsevier, vol. 25(4), pages 833-839, October.
    17. 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

    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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