A simple ordered data estimator for inverse density weighted expectations
A simple ordered data estimator for inverse density weighted expectations
复制标题
用于逆密度加权期望的简单有序数据估计器
DOI：
10.1016/j.jeconom.2005.08.005
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发表时间：
2007
影响因子：
6.3
通讯作者：
Susanne M. Schennach
中科院分区：
文献类型：
--
作者：
Arthur Lewbel;Susanne M. Schennach
我们考虑估计的功能，是由一个未知的密度，或等价的，条件期望积分的平均值。我们提供的“有序数据”估计是根n一致的，渐近正态的，并且在数值上非常简单，只涉及对数据进行排序并对结果进行求和。不需要依赖于样本大小的平滑。一个类似的简单的估计提供了限制方差。证据包括新的极限分布结果的最近邻间距的功能。潜在的应用包括内生二元选择，支付意愿，选择和治疗模型。
We consider estimation of means of functions that are scaled by an unknown density, or equivalently, integrals of conditional expectations. The “ordered data” estimator we provide is root n consistent, asymptotically normal, and is numerically extremely simple, involving little more than ordering the data and summing the results. No sample-size-dependent smoothing is required. A similarly simple estimator is provided for the limiting variance. The proofs include new limiting distribution results for functions of nearest-neighbor spacings. Potential applications include endogenous binary choice, willingness to pay, selection, and treatment models.