摘要翻译：
在回归不连续(RD)设计中，现代经验工作通常采用局部多项式估计和推断，并具有均方误差(MSE)最优带宽选择。这个带宽产生了一个MSE最优的RD处理效果估计器，但通过构造对推断无效。当使用MSE最优带宽时，鲁棒偏差校正(RBC)推理方法是有效的，但我们发现它们在覆盖误差方面产生了次优置信区间。我们为RBC置信区间估计建立了有效的复盖误差展开，并利用这些结果提出了新的推断--形成这些区间的最优带宽选择。我们发现，当目标是构造具有最小覆盖误差的RBC置信区间时，RD点估计器的标准MSE最优带宽太大。我们进一步优化复盖误差背后的常数项，以导出RBC推断所需的辅助带宽的新的最优选择。我们的扩展也建立了RBC推论产生高阶精化（相对于传统的欠平滑）在RD设计的背景下。我们的主要结果涵盖了条件异方差下的sharp和sharp kink RD设计，并讨论了fuzzy和其他RD设计的扩展、聚类抽样和干预前协变量调整。理论结果用蒙特卡罗实验和经验应用加以说明，主要方法学结果可在\texttt{R}和\texttt{Stata}包中获得。
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英文标题：
《Optimal Bandwidth Choice for Robust Bias Corrected Inference in
  Regression Discontinuity Designs》
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作者：
Sebastian Calonico and Matias D. Cattaneo and Max H. Farrell
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最新提交年份：
2020
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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：
  Modern empirical work in Regression Discontinuity (RD) designs often employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This bandwidth yields an MSE-optimal RD treatment effect estimator, but is by construction invalid for inference. Robust bias corrected (RBC) inference methods are valid when using the MSE-optimal bandwidth, but we show they yield suboptimal confidence intervals in terms of coverage error. We establish valid coverage error expansions for RBC confidence interval estimators and use these results to propose new inference-optimal bandwidth choices for forming these intervals. We find that the standard MSE-optimal bandwidth for the RD point estimator is too large when the goal is to construct RBC confidence intervals with the smallest coverage error. We further optimize the constant terms behind the coverage error to derive new optimal choices for the auxiliary bandwidth required for RBC inference. Our expansions also establish that RBC inference yields higher-order refinements (relative to traditional undersmoothing) in the context of RD designs. Our main results cover sharp and sharp kink RD designs under conditional heteroskedasticity, and we discuss extensions to fuzzy and other RD designs, clustered sampling, and pre-intervention covariates adjustments. The theoretical findings are illustrated with a Monte Carlo experiment and an empirical application, and the main methodological results are available in \texttt{R} and \texttt{Stata} packages.
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PDF链接：
https://arxiv.org/pdf/1809.00236