当前位置: X-MOL 学术The Review of Economics and Statistics › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Quantile Treatment Effects in the Presence of Covariates
The Review of Economics and Statistics ( IF 6.481 ) Pub Date : 2020-12-01 , DOI: 10.1162/rest_a_00858
David Powell 1
Affiliation  

This paper proposes a method to estimate unconditional quantile treatment effects (QTEs) given one or more treatment variables, which may be discrete or continuous, even when it is necessary to condition on covariates. The estimator, generalized quantile regression (GQR), is developed in an instrumental variable framework for generality to permit estimation of unconditional QTEs for endogenous policy variables, but it is also applicable in the conditionally exogenous case. The framework includes simultaneous equations models with nonadditive disturbances, which are functions of both unobserved and observed factors. Quantile regression and instrumental variable quantile regression are special cases of GQR and available in this framework.

中文翻译:

存在协变量时的分位数处理效果

本文提出了一种在给定一个或多个治疗变量的情况下估计无条件分位数治疗效果 (QTE) 的方法,这些变量可能是离散的或连续的,即使需要以协变量为条件。估计量,广义分位数回归 (GQR),是在工具变量框架中开发的,以允许对内生政策变量的无条件 QTE 进行估计,但它也适用于有条件的外生情况。该框架包括具有非可加扰动的联立方程模型,它们是未观察到的和已观察到的因素的函数。分位数回归和工具变量分位数回归是 GQR 的特例,可在此框架中使用。
更新日期:2020-12-01
down
wechat
bug