论文标题
回归不连续性的局部复合分位数回归
Local Composite Quantile Regression for Regression Discontinuity
论文作者
论文摘要
我们将局部复合分位回归（LCQR）引入回归不连续性（RD）设计的因果推断。 Kai等。 （2010年）研究了LCQR的效率属性，而我们表明其良好的边界性能转化为在各种数据生成过程中对RD治疗效应的准确估算。此外，我们提出了一个偏置校正和标准错误调整的t检验的推理，这导致具有良好覆盖概率的置信区间。还讨论了带宽选择器。为了说明，我们进行了仿真研究，并重新审视了Lee（2008）的经典示例。开发了一个伴随R软件包RDCQR。
We introduce the local composite quantile regression (LCQR) to causal inference in regression discontinuity (RD) designs. Kai et al. (2010) study the efficiency property of LCQR, while we show that its nice boundary performance translates to accurate estimation of treatment effects in RD under a variety of data generating processes. Moreover, we propose a bias-corrected and standard error-adjusted t-test for inference, which leads to confidence intervals with good coverage probabilities. A bandwidth selector is also discussed. For illustration, we conduct a simulation study and revisit a classic example from Lee (2008). A companion R package rdcqr is developed.
