Nonmanipulable Bayesian testing

• 成果类型: Article
• 署名作者: Stewart, Colin
• 署名单位: University of Toronto
• 刊物名称: JOURNAL OF ECONOMIC THEORY
• ISSN/ISSBN: 0022-0531
• DOI: 10.1016/j.jet.2011.06.015
• 发表日期: 2011
• 页码: 2029-2041
• 关键词: Probability forecasts; testing; experts

摘要

This paper considers the problem of testing an expert who makes probabilistic forecasts about the outcomes of a stochastic process. I show that, as long as uninformed experts do not learn the correct forecasts too quickly, a likelihood test can distinguish informed from uninformed experts with high prior probability. The test rejects informed experts on some data-generating processes; however, the set of such processes is topologically small. These results contrast sharply with many negative results in the literature. (C) 2011 Elsevier Inc. All rights reserved.