Title: Inference on quantile processes in partially linear models with applications to the impact of unemployment benefits
Authors: Zhongjun Qu - Boston University (United States) [presenting]
Jungmo Yoon - Hanyang University (Korea, South)
Pierre Perron - Boston University (United States)
Abstract: Methods are proposed to estimate and conduct inference 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 helping to bridge the gender gap.