New Developments in Regression Discontinuity Designs: Covariates Adjustment and Coverage Optimal Inference

不连续性回归设计的新进展：协变量调整和覆盖最优推理

• 批准号: 21K01419
• 负责人: YU ZHENGFEI
• 金额: $ 2万
• 依托单位: University of Tsukuba
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2021
• 资助国家: 日本
• 起止时间: 2021-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K01419/
• 关键词: Regression discontinuity; Empirical likelihood; Coverage error; Local misspecification; Covariate adjustment; Moment restrictions; Efficiency gain; Uniform in bandwidth; Covariates; Bandwidth; Treatment effect

This project proposes a novel approach to incorporate covariates in regression discontinuity (RD) designs. It represents the covariate balance condition as over-identifying moment restrictions. The empirical likelihood (EL) RD estimator efficiently incorporates the information from covariate balance and has an asymptotic variance no larger than that of the standard estimator without covariates. This project then proposes a robust corrected EL confidence set which achieves a faster coverage error decay rate. The coverage accuracy of the proposed confidence interval is automatically robust against slight perturbation to the covariate balance condition, which may happen in cases such as data contamination and misspecified “unaffected” outcomes used as covariates. This project also propose a way to conduct sensitivity analysis in the bandwidth choice when the researcher wants to use regression discontinuity with covariate adjustment. This project conducts Monte Carlo simulations to assess the finite-sample performance of the proposed inference method and then applies it to a real dataset.

该项目提出了一种将协变量纳入回归不连续性 (RD) 设计的新方法，它将协变量平衡条件表示为过度识别矩限制。经验似然 (EL) RD 估计器有效地合并了协变量平衡的信息，并具有渐近方差。不大于没有协变量的标准估计量，然后该项目提出了一个稳健的校正 EL 置信度集，该置信度集实现了更快的覆盖误差衰减率。所提出的置信区间的覆盖精度自动稳健。对协变量平衡条件的扰动，这可能发生在数据污染和用作协变量的错误指定的“未受影响”结果等情况下。当研究人员想要使用协变量的回归不连续性时，该项目还提出了一种在带宽选择中进行敏感性分析的方法。该项目进行蒙特卡洛模拟来评估所提出的推理方法的有限样本性能，然后将其应用于实际数据集。

项目成果

Estimation and inference on treatment effects under treatment-based sampling designs

基于治疗的抽样设计对治疗效果的估计和推断

• DOI: 10.1093/ectj/utac008
• 发表时间: 2022
• 期刊: The Econometrics Journal
• 作者: Song Kyungchul;Yu Zhengfei
• 通讯作者: Yu Zhengfei

Emory university(米国)

埃默里大学（美国）

Renmin University of China(中国)

中国人民大学（中国）

Empirical Likelihood Covariate Adjustment for Regression Discontinuity Designs

• 发表时间: 2020-08
• 作者: Jun Ma;Zhengfei Yu

SIMPLE SEMIPARAMETRIC ESTIMATION OF ORDERED RESPONSE MODELS

有序响应模型的简单半参数估计

• DOI: 10.1017/s0266466622000317
• 发表时间: 2022
• 期刊: Econometric Theory
• 影响因子: 0.8
• 作者: Liu Ruixuan;Yu Zhengfei
• 通讯作者: Yu Zhengfei