Testing multiple forecasters

• 成果类型: Article
• 署名作者: Feinberg, Yossi; Stewart, Colin
• 署名单位: Stanford University; University of Toronto
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.1111/j.1468-0262.2008.00847.x
• 发表日期: 2008
• 页码: 561-582
• 关键词: Calibration

摘要

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