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Testing multiple forecasters
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DOI：10.1111/j.1468-0262.2008.00847.x.png)
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Abstract
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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
Keywords:
calibration
probability forecasting
testing
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