Semiparametric Estimation and Inference in Partially Identified Econometric Models

部分识别计量经济模型中的半参数估计和推理

• 批准号: 1357653
• 负责人: Hiroaki Kaido
• 金额: $ 19.9万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-15 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1357653&HistoricalAwards=false
• 关键词: Semiparametric; Estimation; Inference; Partially; Identified

This award funds research in statistical methods for the analysis of economic data. The goal is to expand the scope of the existing statistical theory that explains the properties of estimation and inference methods for a partially identified model with set identified parameters.The research clarifies the main challenges to expanding the scope of the existing theory of semiparametric inference, develops new approaches to overcome these challenges, and develops practical suggestions for constructing asymptotically efficient estimators within semiparametric models. The research develops new methods that are robust against lack of identification, robust against misspecification, and asymptotically efficient within semiparametric models. Broader impacts will come from the use of these methods to evaluate the results of a variety of government and business decisions. The PI will make software code available to other researchers who want to use these new methods.

该奖项资助经济数据分析统计方法的研究。目的是扩展现有统计理论的范围，解释具有已确定参数的部分可识别模型的估计和推断方法的性质。研究阐明了扩展现有半参数推断理论范围的主要挑战，开发了克服这些挑战的新方法，并提出了在半参数模型中构造渐近有效估计量的实用建议。 该研究开发了新的方法，是强大的缺乏识别，强大的对误设定，和渐近有效的半参数模型。 更广泛的影响将来自于使用这些方法来评估各种政府和企业决策的结果。 PI将向其他希望使用这些新方法的研究人员提供软件代码。