.png){kind=icon}
返回
.png){kind=icon}
.png){kind=icon}
摘要
En 中文
It is well understood that classical sample selection models are not semiparametrically identified without exclusion restrictions. Lee (2009) developed bounds for the parameters in a model that nests the semiparametric sample selection model. These bounds can be wide. In this paper, we investigate bounds that impose the full structure of a sample selection model with errors that are independent of the explanatory variables but have unknown distribution. The additional structure can significantly reduce the identified set for the parameters of interest. Specifically, we construct the identified set for the parameter vector of interest. It is a one-dimensional line segment in the parameter space, and we demonstrate that this line segment can be short in practice. We show that the identified set is sharp when the model is correct and empty when there exist no parameter values that make the sample selection model consistent with the data. We also provide non-sharp bounds under the assumption that the model is correct. These are easier to compute and associated with lower statistical uncertainty than the sharp bounds. Throughout the paper, we illustrate our approach by estimating a standard sample selection model for wages.
Keyword:
Sample selection
exclusion restrictions
bounds
partial identification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.1
论文数:
3.0K
被引数:
4.3W
机构
引用论文
Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects培训，工资和样本选择: 估计治疗效果的界限.png)
OAAI
OAAI.png){kind=icon}
分享.png){kind=icon}
收藏分享收藏NONPARAMETRIC ANALYSIS OF A GENERALIZED REGRESSION-MODEL - THE MAXIMUM RANK CORRELATION ESTIMATOR
PREAI
分享收藏Changes in the distribution of male and female wages accounting for employment composition using bounds.png)
ECONOMETRICAOAAI
ECONOMETRICAIF7.1
OAAI分享收藏
查看更多内容