The Principal-Agent Approach to Testing Experts
AbstractRecent literature on testing experts shows that it is often impossible to determine whether an expert knows the stochastic process that generates data. Despite this negative result, we show that there often exist contracts that allow a decision maker to attain the first-best payoff without learning the expert's type. This kind of full-surplus extraction is always possible in infinite-horizon models in which future payoffs are not discounted. If future payoffs are discounted (but the discount factor tends to 1), the possibility of full-surplus extraction depends on a constraint involving the forecasting technology. (JEL D82)
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Bibliographic InfoArticle provided by American Economic Association in its journal American Economic Journal: Microeconomics.
Volume (Year): 3 (2011)
Issue (Month): 2 (May)
Find related papers by JEL classification:
- D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
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- Drew Fudenberg & David K. Levine, 1996.
"An Easier Way to Calibrate,"
Levine's Working Paper Archive
2059, David K. Levine.
- Alvaro Sandroni, 2003. "The reproducible properties of correct forecasts," International Journal of Game Theory, Springer, vol. 32(1), pages 151-159, December.
- 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.
- Eddie Dekel & Yossi Feinberg, 2006.
"Non-Bayesian Testing of a Stochastic Prediction,"
1418, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Colin Stewart, 2009.
"Nonmanipulable Bayesian Testing,"
tecipa-360, University of Toronto, Department of Economics.
- Irene Valsecchi, 2013. "The expert problem: a survey," Economics of Governance, Springer, vol. 14(4), pages 303-331, November.
- Alvaro Sandroni, 2014. "At Least Do No Harm: The Use of Scarce Data," American Economic Journal: Microeconomics, American Economic Association, vol. 6(1), pages 1-3, February.
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