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CAREER: Robust Econometric Inference

职业：稳健的计量经济学推理

基本信息

批准号：
1654234
负责人：
Isaiah Andrews
金额：
$ 41.72万
依托单位：
National Bureau of Economic Research Inc
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1654234&HistoricalAwards=false
关键词：
CAREER Robust Econometric Inference

项目摘要

This project develops new econometric and statistical tools for drawing inferences about economic relationships in contexts where existing techniques are unreliable. In many economic contexts, the available data contain relatively little usable information about the relationships of economic interest, or are poorly described by available econometric models. In such cases, existing techniques for inference can be unreliable, and researchers using these techniques risk drawing spurious conclusions about relationships among economic variables and the likely effects of proposed policies. This project develops new techniques for reliable inference in such settings, allowing researchers to more accurately quantify uncertainty about economic relationships and policy proposals. To increase the impact of this research, the investigator organizes, records, and publicly posts a series of lectures discussing the proposed methods, and also posts code for the methods developed to publicly accessible software repositories. Further, the investigator participates in a mentoring program for graduate students from underrepresented minority backgrounds organized by the American Economic Association.The project consists of four components. The first component uses tools from differential geometry to study weak identification-robust inference in instrumental variables models with heterogeneous treatment effects, closing an important gap in the existing literature on identification-robust inference. The second component derives optimal tests and estimators for moment equality models with potential identification failure, using a novel family of integrated likelihoods which integrate out a prior on a functional nuisance parameter. The third component shows that by exploiting structure common to many partially-identified moment inequality applications one can obtain powerful, computationally simple inference for low-dimensional parameters of interest. The fourth and final component considers locally misspecified models, develops a connection between inference under local misspecification and inference under weak identification, and proposes techniques which allow misspecification-robust inference on structural or causal parameters of interest.

该项目开发了新的计量经济学和统计工具，用于在现有技术不可靠的情况下推断经济关系。在许多经济背景下，现有数据包含的关于经济利益关系的可用信息相对较少，或者现有计量经济学模型描述得很差。在这种情况下，现有的推理技术可能是不可靠的，使用这些技术的研究人员可能会对经济变量之间的关系和拟议政策的可能影响得出虚假的结论。该项目开发了在这种情况下进行可靠推断的新技术，使研究人员能够更准确地量化经济关系和政策建议的不确定性。为了增加这项研究的影响力，研究人员组织，记录，并公开发布了一系列讲座讨论所提出的方法，并公布代码的方法开发到公开访问的软件库。此外，调查员还参加了一个由美国经济协会组织的针对少数族裔背景的研究生的辅导项目。第一部分使用微分几何的工具来研究具有异质性处理效应的工具变量模型中的弱识别鲁棒推理，关闭了现有文献中关于识别鲁棒推理的重要空白。第二个组成部分推导出最佳的测试和估计矩平等模型与潜在的识别失败，使用一种新的家庭的综合似然集成了一个功能滋扰参数的先验。第三个组成部分表明，通过利用结构共同的许多部分确定的矩不等式的应用程序，可以获得强大的，计算简单的推理低维参数的利益。第四个和最后一个组件考虑局部误指定的模型，开发了一个连接的推理下的局部误指定和弱识别下的推理，并提出了技术，允许误指定鲁棒推理的结构或因果参数的利益。