Some recent developments in modeling quantile treatment effects  

作　　者：TANG Sheng-fang 

机构地区：[1]Department of Statistics,School of Economics,Xiamen University,Xiamen 361005,China

出　　处：《Applied Mathematics(A Journal of Chinese Universities)》2020年第2期220-243,共24页高校应用数学学报（英文版）（B辑）

基　　金：Supported by the National Natural Science Foundation of China#71631004(Key Project);the National Science Fund for Distinguished Young Scholars#71625001;the scholarship from China Scholarship Council(CSC)under the Grant CSC N201806310088.

摘　　要：This paper provides a selective review of the recent developments on econometric/statistical modeling in quantile treatment effects under both selection on observables and on unobservables.First,we discuss identification,estimation and inference of quantile treatment effects under the framework of selection on observables.Then,we consider the case where the treatment variable is endogenous or self-selected,for which an instrumental variable method provides a powerful tool to tackle this problem.Finally,some extensions are discussed to the data-rich environments,to the regression discontinuity design,and some other approaches to identify quantile treatment effects are also discussed.In particular,some future research works in this area are addressed.

关 键 词：average treatment effect ENDOGENEITY quantile treatment effect regression discontinuity design 

分 类 号：C81[社会学—统计学]