Regression Discontinuity Design with Covariates
Regression Discontinuity Design with Covariates
复制标题
协变量的不连续性回归设计
DOI：
10.1920/wp.cem.2007.2707
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
M. Frölich
中科院分区：
文献类型：
--
作者：
M. Frölich
在本文中，断点回归设计（RDD）被推广，以完全非参数的方式解释观察到的协变量 X 的差异。结果表明，无论 X 的维数如何，治疗效果都可以以一维非参数回归的速率进行估计。因此，它扩展了 Hahn、Todd 和 van der Klaauw (2001) 以及 Porter (2003) 的分析，他们检查了没有协变量的识别和估计，需要的假设在应用中通常可能过于强烈。在许多应用中，阈值左侧和右侧的个体观察到的特征不同。房屋可以跨学区边界以不同的方式建造。公司可能会在某个阈值附近存在差异，这意味着某些法律变更等。考虑到协变量的这些差异对于减少偏差非常重要。此外，考虑协变量也可以减少方差。最后，还考虑了分位数治疗效果（QTE）的估计。
In this paper, the regression discontinuity design (RDD) is generalized to account for differences in observed covariates X in a fully nonparametric way. It is shown that the treatment effect can be estimated at the rate for one-dimensional nonparametric regression irrespective of the dimension of X. It thus extends the analysis of Hahn, Todd and van der Klaauw (2001) and Porter (2003), who examined identification and estimation without covariates, requiring assumptions that may often be too strong in applications. In many applications, individuals to the left and right of the threshold differ in observed characteristics. Houses may be constructed in different ways across school attendance district boundaries. Firms may differ around a threshold that implies certain legal changes, etc. Accounting for these differences in covariates is important to reduce bias. In addition, accounting for covariates may also reduces variance. Finally, estimation of quantile treatment effects (QTE) is also considered.