A0350
Title: Panel data quantile regression for treatment effect models
Authors: Takuya Ishihara - Tohoku University (Japan) [presenting]
Abstract: A novel estimation method is developed for quantile treatment effects (QTE) under rank invariance and rank stationarity assumptions. The identification of the nonseparable panel data model under these assumptions has been recently explored, and a parametric estimation based on the minimum distance method has been proposed. 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.
