论文标题
本地回归分布估计器
Local Regression Distribution Estimators
论文作者
论文摘要
本文研究了局部回归分布估计器的较大样本特性，其中包括一类边界自适应密度估计器作为一个很好的例子。首先，我们以统一的方式建立了一个侧面的高斯大型样品分布近似，同时允许边界和内部评估点。使用此结果，我们研究了估计器的渐近效率，并表明基于“冗余”回归器的精心制作的最小距离实现可以导致效率提高。其次，我们为估计器建立统一的线性化和强近似值，并采用这些结果来构建有效的置信带。第三，我们开发了具有估计权重的加权分布以及本地$ l^{2} $最小二乘估计的扩展。最后，我们通过在程序评估中使用两种应用来说明我们的方法：反事实密度测试以及IV规范和异质性密度分析。可以使用Stata和R中的伴随软件包。
This paper investigates the large sample properties of local regression distribution estimators, which include a class of boundary adaptive density estimators as a prime example. First, we establish a pointwise Gaussian large sample distributional approximation in a unified way, allowing for both boundary and interior evaluation points simultaneously. Using this result, we study the asymptotic efficiency of the estimators, and show that a carefully crafted minimum distance implementation based on "redundant" regressors can lead to efficiency gains. Second, we establish uniform linearizations and strong approximations for the estimators, and employ these results to construct valid confidence bands. Third, we develop extensions to weighted distributions with estimated weights and to local $L^{2}$ least squares estimation. Finally, we illustrate our methods with two applications in program evaluation: counterfactual density testing, and IV specification and heterogeneity density analysis. Companion software packages in Stata and R are available.
