Testing Multiple Forecasters

34 Pages Posted: 7 Feb 2007

See all articles by Yossi Feinberg

Yossi Feinberg

Stanford Graduate School of Business

Colin Stewart

Yale University

Date Written: January 2007

Abstract

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 one) 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. In contrast, tests that allow false experts to make precise predictions can be jointly manipulated.

Keywords: microeconomics

JEL Classification: C12, C44, C53, D82, D83

Suggested Citation

Feinberg, Yossi and Stewart, Colin, Testing Multiple Forecasters (January 2007). Stanford University Graduate School of Business Research Paper No. 1957, Available at SSRN: https://ssrn.com/abstract=961856 or http://dx.doi.org/10.2139/ssrn.961856

Yossi Feinberg (Contact Author)

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Colin Stewart

Yale University ( email )

New Haven, CT 06520
United States

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