Optimal Data-Driven Regression Discontinuity Plots
Optimal Data-Driven Regression Discontinuity Plots
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DOI：
10.1080/01621459.2015.1017578
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发表时间：
2015-12-01
影响因子：
3.7
通讯作者：
Titiunik, Rocio
中科院分区：
文献类型：
--
作者：
Calonico, Sebastian;Cattaneo, Matias D.;Titiunik, Rocio
探索性数据分析在应用统计学和计量经济学中发挥着核心作用。在流行的回归不连续（RD）设计中，强烈提倡使用图形分析，因为它提供了简单的表示和透明的设计验证。 RD 图如今在应用中广泛使用，尽管其形式属性未知：这些图通常采用调整参数的临时选择来呈现，这使得这些过程不太自动化且更主观。在本文中，我们正式研究了基于均匀间隔的数据分箱的最常见的 RD 图，并根据研究人员的目标提出了几种（最佳）数据驱动的分箱数量选择。这些 RD 图的构建要么是为了近似潜在的未知回归函数，而不在估计器中施加平滑度，要么是为了近似原始数据的潜在可变性，同时平滑数据的其他无信息散点图。此外，我们引入了一种基于分位数间隔分箱的替代 RD 图，研究其形式属性，并针对分箱数量提出类似（最佳）数据驱动的选择。主要提出的数据驱动选择器采用间距估计器，这种估计器简单且易于在应用中实现，因为它们不需要额外选择调整参数。总而言之，我们的结果提供了一系列替代 RD 图，这些图在实施时是客观且自动的，为 RD 设计中的图形分析提供了可靠的基准。我们使用几个经验示例和蒙特卡罗研究来说明自动 RD 图的性能。使用 Calonico、Cattaneo 和 Titiunik 中描述的软件包，所有结果都可以在 R 和 STATA 中轻松获得。本文的补充材料可在线获取。
Exploratory data analysis plays a central role in applied statistics and econometrics. In the popular regression-discontinuity (RD) design, the use of graphical analysis has been strongly advocated because it provides both easy presentation and transparent validation of the design. RD plots are nowadays widely used in applications, despite its formal properties being unknown: these plots are typically presented employing ad hoc choices of tuning parameters, which makes these procedures less automatic and more subjective. In this article, we formally study the most common RD plot based on an evenly spaced binning of the data, and propose several (optimal) data-driven choices for the number of bins depending on the goal of the researcher. These RD plots are constructed either to approximate the underlying unknown regression functions without imposing smoothness in the estimator, or to approximate the underlying variability of the raw data while smoothing out the otherwise uninformative scatterplot of the data. In addition, we introduce an alternative RD plot based on quantile spaced binning, study its formal properties, and propose similar (optimal) data-driven choices for the number of bins. The main proposed data-driven selectors employ spacings estimators, which are simple and easy to implement in applications because they do not require additional choices of tuning parameters. Altogether, our results offer an array of alternative RD plots that are objective and automatic when implemented, providing a reliable benchmark for graphical analysis in RD designs. We illustrate the performance of our automatic RD plots using several empirical examples and a Monte Carlo study. All results are readily available in R and STATA using the software packages described in Calonico, Cattaneo, and Titiunik. Supplementary materials for this article are available online.