CAREER: Robust Econometric Inference

该项目开发新的计量和统计工具，以便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，现有的数据包含相对较少的关于经济利益关系的可用信息，或者现有的计量经济模型描述得很差。在这种情况下，现有的推断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，研究者组织、记录并公开发布了一系列讨论建议方法的讲座，并将开发的方法的代码发布到可公开访问的软件存储库中。此外，研究者还参加了由美国经济协会组织的针对少数族裔背景的研究生的指导计划。该项目由四个部分组成。第一部分使用微分几何工具研究具有异质处理效应的工具变量模型中的弱识别鲁棒推断，填补了现有文献中识别鲁棒推断的重要空白。第二部分为具有潜在识别失败的力矩相等模型导出了最优测试和估计，使用了一组新的集成似然，在函数干扰参数上集成出先验。第三部分表明，通过利用许多部分识别矩不等式应用的共同结构，可以获得对低维参数感兴趣的强大的，计算简单的推断。第四部分也是最后一部分考虑了局部错定模型，建立了局部错定推理和弱识别推理之间的联系，并提出了允许对感兴趣的结构或因果参数进行错定鲁棒推理的技术。

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.