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首页> 外文期刊>The econometrics journal >Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs
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Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs

机译：回归不连续设计中强大的偏置推断的最佳带宽选择

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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 that 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 smaller coverage error rate. 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 un-dersmoothing) 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 R and Stata packages.
机译：消退中的现代实证工作不连续（RD）设计通常采用局部多项式估计和推断，均线误差（MSE）最佳带宽选择。该带宽产生了MSE最佳RD处理效果估计器，但是通过施工无效。稳健的偏置（RBC）推理方法在使用MSE最佳带宽时有效，但我们表明它们在覆盖误差方面产生次优置信区间。我们为RBC置信区间估算器建立了有效的覆盖误差扩展，并使用这些结果提出了用于形成这些间隔的新推理最佳带宽选择。我们发现，当目标是以较小的覆盖误差率构造RBC置信区间时，RD点估计器的标准MSE最佳带宽太大。我们进一步优化了覆盖率误差背后的常量术语，以导出RBC推理所需的辅助带宽的新的最佳选择。我们的扩展还建立了RBC推理在RD设计的背景下产生了高阶的细化（相对于传统的Un-Dersmooth）。我们的主要结果涵盖了条件异性娱乐性下的夏普和尖锐的扭结Rd设计，我们讨论了模糊和其他RD设计，聚类采样和预干预性协变量调整的延伸。理论发现用蒙特卡罗实验和经验应用说明，r和stata包装中有主要方法结果。

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