Forecasting Conditional Probabilities of Binary Outcomes under Misspecification

• 成果类型: Article
• 署名作者: Elliott, Graham; Ghanem, Dalia; Krueger, Fabian
• 署名单位: University of California System; University of California San Diego; University of California System; University of California Davis; Heidelberg Institute for Theoretical Studies
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00564
• 发表日期: 2016-10
• 页码: 742-755
• 关键词: models; prediction

摘要

We consider constructing probability forecasts from a parametric binary choice model under a large family of loss functions (scoring rules). Scoring rules are weighted averages over the utilities that heterogeneous decision makers derive from a publicly announced forecast (Schervish, 1989). Using analytical and numerical examples, we illustrate howdifferent scoring rules yield asymptotically identical results if the model is correctly specified. Under misspecification, the choice of scoring rule may be inconsequential under restrictive symmetry conditions on the data-generating process. If these conditions are violated, typically the choice of a scoring rule favors some decision makers over others.