摘要翻译：
研究了估计中包含协变量时的回归间断设计。我们研究了局部多项式估计，其中包括离散或连续协变量在一个加性可分的方式，但不强加任何参数限制的基本总体回归函数。我们推荐了一种在直觉条件下保持一致性的协变量调整方法，并描述了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展开式和稳健的偏差校正推理过程，具有异方差一致性和聚类稳健的标准误差。给出了一个经验说明和一个广泛的模拟研究。所有方法都在\texttt{R}和\texttt{Stata}软件包中实现。
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英文标题：
《Regression Discontinuity Designs Using Covariates》
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作者：
Sebastian Calonico and Matias D. Cattaneo and Max H. Farrell and Rocio
  Titiunik
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最新提交年份：
2018
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  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. An empirical illustration and an extensive simulation study is presented. All methods are implemented in \texttt{R} and \texttt{Stata} software packages.
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PDF链接：
https://arxiv.org/pdf/1809.03904