论文标题
带有因变量的分位数回归和协变量
Quantile regression with generated dependent variable and covariates
论文作者
论文摘要
我们研究线性分位数回归模型当回归器和/或因变量未直接观察到，而是在初始第一步中进行估计，并在第二步中用于估计分位数参数时使用。这种生成的分位数回归（GQR）类别涵盖了各种统计应用，例如，内源分位数回归模型和三角形结构方程模型的估计，并讨论了一些新的相关应用。我们研究了两步估计量的渐近分布，这是由于在非平滑分位数回归估计器中存在产生的协变量和/或因变量而具有挑战性的。我们采用经验过程理论的技术来找到两个步骤估计量的统一的巴哈杜尔扩展，该估计量用于建立渐近结果。我们通过模拟和基于拍卖的经验应用来说明GQR估计器的性能。
We study linear quantile regression models when regressors and/or dependent variable are not directly observed but estimated in an initial first step and used in the second step quantile regression for estimating the quantile parameters. This general class of generated quantile regression (GQR) covers various statistical applications, for instance, estimation of endogenous quantile regression models and triangular structural equation models, and some new relevant applications are discussed. We study the asymptotic distribution of the two-step estimator, which is challenging because of the presence of generated covariates and/or dependent variable in the non-smooth quantile regression estimator. We employ techniques from empirical process theory to find uniform Bahadur expansion for the two step estimator, which is used to establish the asymptotic results. We illustrate the performance of the GQR estimator through simulations and an empirical application based on auctions.
