The Devil is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable
The Devil is in the Tails: Regression Discontinuity Design with Measurement Error in the Assignment Variable
复制标题
魔鬼在尾部：赋值变量中存在测量误差的不连续性回归设计
DOI：
10.1108/s0731-905320170000038019
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Yi Shen
中科院分区：
文献类型：
--
作者：
Zhuan Pei;Yi Shen
当协变量（分配变量）超过已知阈值时，回归不连续性（RD）设计中的识别取决于治疗概率的不连续性。然而，如果分配变量的测量有误差，则治疗概率与观察到的误测量分配变量之间的第一阶段关系中的不连续性可能消失。因此，分配变量中存在的测量误差对治疗效果识别提出了挑战。本文给出了仅观察到误测的分配变量、治疗状态和结果变量时的识别充分条件。我们证明识别分别为离散和连续的分配变量，并研究各种估计程序的性质。我们说明了所提出的方法在实证应用中，我们估计医疗补助的占用和私人医疗保险覆盖面的挤出效应。
Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the first stage relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This paper provides sufficient conditions for identification when only the mismeasured assignment variable, the treatment status and the outcome variable are observed. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.