Bounding mean regressions when a binary regressor is mismeasured
Bounding mean regressions when a binary regressor is mismeasured
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DOI：
10.1016/s0304-4076(95)01730-5
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发表时间：
1996-08-01
影响因子：
6.3
通讯作者：
Bollinger, CR
中科院分区：
文献类型：
--
作者：
Bollinger, CR
在本文中，我研究了识别和估计的平均回归模型，当一个二元回归量是错测。我证明了模型参数的界是可识别的，并提供了一致和渐近正态的简单估计。当有关于错误分类概率的更强的先验信息时，边界可以变得更紧。同样，为这些情况提供了一个简单的估计器。所有结果适用于参数和非参数模型。最后给出了一个简短的实证例子。
In this paper I examine identification and estimation of mean regression models when a binary regressor is mismeasured. I prove that bounds for the model parameters are identified and provide simple estimators which are consistent and asymptotically normal. When stronger prior information about the probability of misclassification is available, the bounds can be made tighter. Again, a simple estimator for these cases is provided. All results apply to parametric and nonparametric models. The paper concludes with a short empirical example.