Estimation and Inference of Distributional Partial Effects: Theory and Application
提出非参数和半参数方法估计协变量对响应变量条件分布的影响，并给出均匀检验。蒙特卡洛实验显示小样本表现良好，实证发现美国最低工资对家庭收入有异质性影响，小幅提高更有效改善收入分布。
This article considers nonparametric and semiparametric estimation and inference of the effects of a covariate, either discrete or continuous, on the conditional distribution of a response outcome. It also proposes various uniform tests following estimation. This type of analysis is useful in situations where the econometrician or policy-maker is interested in knowing the effect of a variable or policy on the whole distribution of the response outcome conditional on covariates and is not willing to make parametric functional form assumptions. Monte Carlo experiments show that the proposed estimators and tests are well-behaved in small samples. The empirical section studies the effect of minimum wage hikes on household labor earnings. It is found that the minimum wage has a heterogenous impact on household earnings in the U.S. and that small hikes in the minimum wage are more effective in improving the household earnings distribution.