使用安慰剂区在RDD和相关设置中进行最佳模型选择
Optimal Model Selection in RDD and Related Settings Using Placebo Zones
摘要：
我们提出了一个新的模型选择算法的断点回归设计，回归折点设计，和相关的IV估计。候选模型在运行变量的“安慰剂区域”内进行评估，其中真实效应已知为零。该方法产生带宽，多项式和任何其他选择参数的最佳组合。它还可以告知模型类别之间的选择（例如RDD与cohort-IV）以及任何其他选择，例如协变量，内核或其他权重。我们概述的充分条件下，该方法是渐近最优的。在一系列蒙特卡罗模拟中，该方法在更一般的条件下也表现良好。我们证明了在澳大利亚新南威尔士州的最低监督驾驶时间的变化的评估方法。我们还重新评估了关于领先和最低法定饮酒年龄影响的证据。我们的Stata命令实现了该过程，并将其性能与其他方法进行了比较。
英文摘要：
We propose a new model-selection algorithm for Regression Discontinuity Design, Regression Kink Design, and related IV estimators. Candidate models are assessed within a 'placebo zone' of the running variable, where the true effects are known to be zero. The approach yields an optimal combination of bandwidth, polynomial, and any other choice parameters. It can also inform choices between classes of models (e.g. RDD versus cohort-IV) and any other choices, such as covariates, kernel, or other weights. We outline sufficient conditions under which the approach is asymptotically optimal. The approach also performs favorably under more general conditions in a series of Monte Carlo simulations. We demonstrate the approach in an evaluation of changes to Minimum Supervised Driving Hours in the Australian state of New South Wales. We also re-evaluate evidence on the effects of Head Start and Minimum Legal Drinking Age. Our Stata commands implement the procedure and compare its performance to other approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
