Some recent developments in modeling quantile treatment effects
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
