Inference on Conditional Quantile Processes in Partially Linear Models with Applications to the Impact of Unemployment Benefits
提出一种估计和推断部分线性模型中条件分位数过程的方法，并应用于研究失业救济金延长对收入分布尾部异质性的影响，发现其降低组内不平等但可能加剧组间不平等。
Abstract We propose methods to estimate and make inferences on conditional quantile processes for models with both nonparametric and (locally or globally) linear components. We derive their asymptotic properties, optimal bandwidths, and uniform confidence bands over quantiles allowing for robust bias correction. Our framework covers the sharp regression discontinuity design, which is used to study the effects of unemployment insurance benefits extensions, focusing on heterogeneity over quantiles and covariates. We show economically strong effects in the tails of the outcome distribution. They reduce the within-group inequality, but can be viewed as enhancing between-group inequality, although they help to bridge the gender gap.