Hi, I was wondering if someone can explain the difference between how -heckman- and -treatreg are estimated. I understand that analysts usually prefer -heckman- for sample selection bias and -treatreg- for endogeneity bias. But I was not sure how the two models are different computationally because they both use hazard ratio (or inverse Mills). Is hazard ratio different from IMR? Can anyone direct me to an article that explains the computational and theoretical difference between the two models? Thank you, 谁可以解释Heckman模型与treatreg的不同。他们看起来都在处理内生性偏误， 且都运用hazard ration(Inverse Mills)运算。 ====== Well, start from the examples in -h heckman- and -h treatreg-, and do not be fooled by the similarity with respect to computation: There is a reason why Stata supplies two estimators. In - h heckman-, the wage that is supposed to be modelled is missing in 657 cases (-ta wage, m- to see that). Heckman allows one to take into account the mechanism that determines the censoring of 657 cases, i.e. the labor supply behavior of the women in the dataset. So the selection equation models this question with -possibly- different covariates from the outcome equation - the determination of the wage itself. -h treatreg-, on the other hand, shows the effect of the -enodgenous- choice of attending college on earnings. There are no missing cases here (-ta ww, m- to see that) but the choice of a higher degree impacts earnings. As more able students tend to choose this career track, the decision is endogenous and must be explicitly modelled. 来源:http://stata.com/statalist/archive/2008-08/msg01385.html 答:Stata提供两种模式是有理由的: 在Heckman中，薪资(sargent注:似乎是开始讨论自我偏误的经典例子，待找) 有657个案例遗失，heckman可以censoring(以有限资料推估)该缺失的657个案例， 例如:妇女劳动投入的例子*ps。因此选择方程式模式化了此问题...(sargent注: 不好意思，这句不太会翻译) 而在treatreg里面，显示了内生性的影响，这里并没有缺失的情况， 但追求更高的学位冲击了薪资。因为学生们更追求学位，因此 该行为的选择即是内生性的展现，而且必须隐含在模型中。 === 相关资料 http://bbs.pinggu.org/thread-1087688-1-1.html 请更了解的人补充一下。谢谢 --

※ 发信站: 批踢踢实业坊(ptt.cc) ◆ From: 114.33.28.107