  In this study, we develop a novel estimation method for quantile treatment
effects (QTE) under rank invariance and rank stationarity assumptions. Ishihara
(2020) explores identification of the nonseparable panel data model under these
assumptions and proposes a parametric estimation based on the minimum distance
method. However, when the dimensionality of the covariates is large, the
minimum distance estimation using this process is computationally demanding. To
overcome this problem, we propose a two-step estimation method based on the
quantile regression and minimum distance methods. We then show the uniform
asymptotic properties of our estimator and the validity of the nonparametric
bootstrap. The Monte Carlo studies indicate that our estimator performs well in
finite samples. Finally, we present two empirical illustrations, to estimate
the distributional effects of insurance provision on household production and
TV watching on child cognitive development.
