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From |
"Shehzad Ali" <sia500@york.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Different approcah to estimate treatment effect |

Date |
Wed, 28 May 2008 12:07:57 +0100 |

In the past I have used the first approach, i.e. using both imr as well as the endogenous variable in the final OLS. IMR corrects for the unobserved heterogeneity. HTH, Shehzad -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Gordon Sent: 27 May 2008 16:30 To: statalist@hsphsun2.harvard.edu Subject: st: Different approcah to estimate treatment effect Greetings! Suppose I want to estimate a treatment effect model, Y = b*X+a*D + e D is the treatment and endogenous, where D = 1 if g*Z>0, and 0 otherwise. If I understand correctly, treatreg in Stata does the following: 1. in the first stage using a probit model (regress D on probit(g*Z)) to estimate g. 2. In the second stage, add the inverse mills ratio to the equation Y = Xb+a*D + e and estimate using OLS. However, I have seen another approach to estimate the treatment effect: 1. in the first stage using a probit model (regress D on probit(g*Z)) to estimate g. 2. replacing D with the estimated probabilities from the first stage and then run the OLS. I am not clear how this second approach is derived. I read through Lee and Trost (1978 journal of econometrics) but there is not much details. Most important, which approach is the preferred one? Thanks for your attention. Gordon * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: Different approcah to estimate treatment effect***From:*"Austin Nichols" <austinnichols@gmail.com>

**References**:**st: Different approcah to estimate treatment effect***From:*Gordon <gaogstat@gmail.com>

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