Minimum Contrast Empirical Likelihood Manipulation Testing for Regression Discontinuity Design
Minimum Contrast Empirical Likelihood Manipulation Testing for Regression Discontinuity Design
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DOI：
10.2139/ssrn.2925682
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发表时间：
2017-03
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通讯作者：
Jun Ma;Hugo Jales;Zhengfei Yu
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文献类型：
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作者：
Jun Ma;Hugo Jales;Zhengfei Yu
本文提出了一种简单的基于概率似然的密度不连续性推理方法。在回归不连续性设计（RDD）中，分配变量在阈值处的密度的连续性被认为是“无操纵”行为假设，这是局部处理效应（LATE）的识别条件的可检验含义。我们的方法基于从最小对比度（MC）问题获得的一阶条件，并补充了大津等人（2013）的方法。我们的推理过程有三个主要优点。首先，它只需要一个调谐参数;其次，它不需要集中任何多余的参数，因此是非常容易实现的;第三，其微妙的二阶属性导致一个简单的覆盖误差最优（CE最优）带宽选择规则。我们提出了一个数据驱动的CE最优带宽选择器，用于实践。Monte Carlo模拟的结果。我们的方法的有效性说明了经验的例子。
This paper proposes a simple empirical-likelihood-based inference method for discontinuity in density. In a regression discontinuity design (RDD), the continuity of the density of the assignment variable at the threshold is considered as a “nomanipulation” behavioral assumption, which is a testable implication of an identifying condition for the local treatment effect (LATE). Our approach is based on the first-order conditions obtained from a minimum contrast (MC) problem and complements Otsu et al. (2013)’s method. Our inference procedure has three main advantages. Firstly, it requires only one tuning parameter; secondly, it does not require concentrating out any nuisance parameter and therefore is very easily implementable; thirdly, its delicate second-order properties lead to a simple coverage-error-optimal (CE-optimal) bandwidth selection rule. We propose a data-driven CE-optimal bandwidth selector for use in practice. Results from Monte Carlo simulations are presented. Usefulness of our method is illustrated by empirical examples.