论文标题
使用安慰区域的RDD和相关设置中的最佳模型选择
Optimal Model Selection in RDD and Related Settings Using Placebo Zones
论文作者
论文摘要
我们提出了一种用于回归不连续设计，回归扭结设计和相关IV估计器的新模型选择算法。在运行变量的“安慰区域”中评估候选模型，其中已知真实效果为零。该方法得出带宽，多项式和任何其他选择参数的最佳组合。它还可以为模型类别（例如RDD与队列-IV）与任何其他选择（例如协变量，内核或其他权重）之间的选择提供信息。我们概述了该方法渐近最佳的足够条件。在一系列蒙特卡洛模拟中，该方法在更一般的条件下也表现出色。我们在评估澳大利亚新南威尔士州最低监督驾驶时间的变化时证明了这种方法。我们还重新评估了有关头开始和最低法律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比较。
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.
