Robust Confidence Regions for Incomplete Models

• 成果类型: Article
• 署名作者: Epstein, Larry G.; Kaido, Hiroaki; Seo, Kyoungwon
• 署名单位: Boston University; Seoul National University (SNU)
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA13394
• 发表日期: 2016
• 页码: 1799-1838
• 关键词: moment inequalities; LOWER PROBABILITIES; large numbers; inference; identification; intervals; entry; LAWS

摘要

Call an economic model incomplete if it does not generate a probabilistic prediction even given knowledge of all parameter values. We propose a method of inference about unknown parameters for such models that is robust to heterogeneity and dependence of unknown form. The key is a Central Limit Theorem for belief functions; robust confidence regions are then constructed in a fashion paralleling the classical approach. Monte Carlo simulations support tractability of the method and demonstrate its enhanced robustness relative to existing methods.