Inference on Conditional Quantile Processes in Partially Linear Models with Applications to the Impact of Unemployment Benefits
部分线性模型中条件分位数过程的推断及失业福利影响应用
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…