Regression Discontinuity Designs Using Covariates
Regression Discontinuity Designs Using Covariates
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DOI：
10.1162/rest_a_00760
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发表时间：
2019-07-01
影响因子：
8
通讯作者：
Titiunik, Rocio
中科院分区：
文献类型：
--
作者：
Calonico, Sebastian;Cattaneo, Matias D.;Titiunik, Rocio
我们研究了当协变量包含在估计中时的回归不连续设计。我们研究了局部多项式估计，包括离散或连续协变量的添加剂可分离的方式，但没有施加任何参数限制的基本人口回归函数。我们推荐一种协变量调整方法，该方法在直观条件下保持一致性，并表征估计和推断改进的潜力。我们还提出了新的协变量调整的均方误差扩展和强大的偏差校正推理程序，异方差一致和集群稳健的标准误。我们提供了一个实证说明和广泛的模拟研究。所有方法都在R和Stata软件包中实现。
We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but without imposing any parametric restrictions on the underlying population regression functions. We recommend a covariate-adjustment approach that retains consistency under intuitive conditions and characterize the potential for estimation and inference improvements. We also present new covariate-adjusted mean-squared error expansions and robust bias-corrected inference procedures, with heteroskedasticity-consistent and cluster-robust standard errors. We provide an empirical illustration and an extensive simulation study. All methods are implemented in R and Stata software packages.