Identification and estimation of statistical functionals using incomplete data
Identification and estimation of statistical functionals using incomplete data
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DOI：
10.1016/j.jeconom.2005.02.007
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发表时间：
2006-06-01
影响因子：
6.3
通讯作者：
Manski, Charles F.
中科院分区：
文献类型：
--
作者：
Horowitz, Joel L.;Manski, Charles F.
不完整的数据，由于缺少观测或变量的区间测量，通常会导致应用程序中的感兴趣的参数无法识别，除非是在不可测试的，往往是有争议的假设。然而，通常可以确定参数的严格界限，而无需对数据变得不完整的过程做出不可测试的假设。边界包含参数的所有逻辑上可能的值，并且可以通过用经验分布替换数据的总体分布来一致地估计。这在某些情况下很简单，但在其他情况下计算负担很重。本文描述了一般问题，并提出了一个实证说明。(c)2005 Elsevier B.V.保留所有权利。
Incomplete data, due to missing observations or interval measurement of variables, usually cause parameters of interest in applications to be unidentified except under untestable and often controversial assumptions. However, it is often possible to identify sharp bounds on parameters without making untestable assumptions about the process through which data become incomplete. The bounds contain all logically possible values of the parameters and can be estimated consistently by replacing the population distribution of the data with the empirical distribution. This is straightforward in some circumstances but computationally burdensome in others. This paper describes the general problem and presents an empirical illustration. (c) 2005 Elsevier B.V. All rights reserved.