Testing Multiple Forecasters
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 I) 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. Copyright Copyright 2008 by The Econometric Society.
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Volume (Year): 76 (2008)
Issue (Month): 3 (05)
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