Placebo zones in discontinuity‐based designs: Estimation, inference, and implementation
提出一种基于安慰剂区域的模型选择算法，用于断点回归设计，通过蒙特卡洛模拟验证其有效性，并配套Stata命令实现。
Abstract We propose a new model‐selection algorithm for regression discontinuity design and related estimators. The performance of candidate models is assessed within a “placebo zone” of the running variable. Candidate models can differ by bandwidth and other choice parameters. We outline (restrictive) sufficient conditions under which the approach is asymptotically optimal, and then show the approach also performs favorably under more general conditions in Monte Carlo simulations, including simulations calibrated to well‐known real‐world applications. We also propose a new randomization inference procedure which draws on the placebo estimates. Our Stata commands implement the procedure and compare its performance to other approaches.