Before revision: In this study, we explore the identification and estimation of the quantile treatment effects (QTE) using panel data. We generalize the change-in-changes (CIC) model proposed by Athey and Imbens (2006) and propose a tractable estimator of the QTE. The CIC model allows for the estimation of the potential outcomes distribution and captures the heterogeneous effects of the treatment on the outcomes .
After revision: In this study, we explore the identification and estimation of the quantile treatment effects (QTE) under the rank invariance and rank stationarity assumptions. Ishihara (2020) explores identification of the nonseparable panel data model under these assumptions and propose a parametric estimation based on the minimum distance method .