[情報]自我偏誤:heckman與treatreg的不同(stata)
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,
誰可以解釋Heckman模型與treatreg的不同。他們看起來都在處理內生性偏誤，
且都運用hazard ration(Inverse Mills)運算。
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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.
來源:http://stata.com/statalist/archive/2008-08/msg01385.html
答:Stata提供兩種模式是有理由的:
在Heckman中，薪資(sargent註:似乎是開始討論自我偏誤的經典例子，待找)
有657個案例遺失，heckman可以censoring(以有限資料推估)該缺失的657個案例，
例如:婦女勞動投入的例子*ps。因此選擇方程式模式化了此問題...(sargent註:
不好意思，這句不太會翻譯)
而在treatreg裡面，顯示了內生性的影響，這裡並沒有缺失的情況，
但追求更高的學位衝擊了薪資。因為學生們更追求學位，因此
該行為的選擇即是內生性的展現，而且必須隱含在模型中。
===
相關資料
http://bbs.pinggu.org/thread-1087688-1-1.html
請更了解的人補充一下。謝謝
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