Collaborative Research: Honest Inference and Efficiency Bounds for Nonparametric Regression and Approximate Moment Condition Models

协作研究：非参数回归和近似矩条件模型的诚实推理和效率界限

• 批准号: 1628939
• 负责人: Timothy Armstrong
• 金额: $ 20.22万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1628939&HistoricalAwards=false
• 关键词: Collaborative; Research; Honest; Inference; Efficiency

In analyzing economic data, researchers use models and assumptions that are typically best thought of as approximations of reality. This project will develop statistical methods that are valid when these models are only approximately correct, rather than exactly correct. The methods developed in this project can also be used to provide simple ways of assessing the sensitivity of the conclusions of an empirical study to its underlying assumptions. These methods can be applied to numerous commonly studied problems that are relevant for policy and for understanding the economy.This project will develop confidence intervals in approximate moment condition models with convex parameter spaces, as well as sharp efficiency bounds showing that they are as tight as possible in a certain precise sense. The setup covers inference on a linear functional of a nonparametric regression function, such as its value at a point, the regression discontinuity parameter, or an average treatment effect under unconfoundedness. The setup also covers parameter constraints in the linear regression model as well as moment condition models such as generalized method of moments (GMM) or minimum distance models in which the moment condition is locally misspecified. The confidence intervals are simple to construct, and valid in an "honest" or uniform sense. As special cases of the results, the project obtains optimal kernels for inference in nonparametric regression models, and optimal weights for GMM under misspecification.

在分析经济数据时，研究人员使用的模型和假设通常被认为是最接近现实的。这个项目将开发统计方法，当这些模型只是近似正确，而不是完全正确时，这些方法是有效的。本项目中开发的方法也可用于提供评估实证研究结论对其基本假设的敏感性的简单方法。这些方法可以应用于许多与政策和理解经济相关的普遍研究问题。该项目将开发具有凸参数空间的近似矩条件模型的置信区间，以及在一定精确意义上显示它们尽可能紧密的尖锐效率界限。该设置涵盖了对非参数回归函数的线性函数的推断，例如其在某一点的值、回归不连续参数或在无混杂情况下的平均处理效果。该设置还涵盖了线性回归模型中的参数约束以及矩条件模型，如广义矩方法（GMM）或最小距离模型，其中矩条件在局部指定错误。置信区间很容易构建，并且在“诚实”或统一的意义上有效。作为结果的特例，获得了非参数回归模型推理的最优核，以及错规范下GMM的最优权值。