论文标题
处理效果模型的面板数据分数回归
Panel Data Quantile Regression for Treatment Effect Models
论文作者
论文摘要
在这项研究中，我们在等级不变性和等级平稳性假设下开发了一种新颖的估计方法（QTE）。 Ishihara（2020）在这些假设下探索了对不可分割的面板数据模型的识别，并提出了基于最小距离方法的参数估计。但是，当协变量的维度很大时，使用此过程的最小距离估计是计算要求的。为了克服这个问题，我们提出了一种基于分位数回归和最小距离方法的两步估计方法。然后，我们显示估计量的均匀特性以及非参数bootstrap的有效性。蒙特卡洛研究表明，我们的估计器在有限样品中的性能很好。最后，我们提出了两个经验插图，以估算保险提供对家庭生产和电视观看儿童认知发展的分配影响。
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.
