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From |
"Austin Nichols" <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Wed, 28 May 2008 09:32:11 -0400 |

Gordon et al.-- The second approach you outline arises from a misunderstanding, and is called the "forbidden regression" by Wooldridge (2002; http://www.stata.com/bookstore/cspd.html). With respect to the first approach, note that the other exogenous vars are not included by default in the first stage by -treatreg- (unlike in -ivregress- etc.) and the example below showing calculations "by hand" clarifies how this can matter. With a binary endogenous variable, you can use -ivreg2- (or even -ivreg- I guess), or a control function approach, as well--i.e. that fact that the endogenous variable is binary does not affect consistency of the IV estimator. The -treatreg- estimator offers improved efficiency, when its assumptions are justified, and can show big differences in small samples. Another alternative appears in 18.4.1 of Wooldridge (2002) as Procedure 18.1, shown in the examples below as iv18. Note that the example given in -help treatreg- and the manual entry on -treatreg- probably violates the assumptions (on p.512 of the manual). The dep var is "wife's wage" but is zero for women with zero hours, rather than missing, making it more like "Wife's earnings" (made explicit in my revision of the -treatreg- example below) and so the errors in the "second stage" regression of wage on observables are not normally distributed (nor bivariate normal with the unobserved first-stage error in the latent var regression). A better model would use log earnings as the dep var, but then there is selection not only on who attends college, but who is in the labor force. Another method is to assume that expected earnings are a function exp(Xb) of X (see -help ivpois- for discussion if installed), where some vars in X may be endogenous (using -ivpois- one could also include the zero-earnings obs in the estimation of that model). ---cut and paste from here to Command window in Stata--- * first update user-written ados required for examples ssc inst estout, replace ssc inst ivreg2, replace ssc inst ivpois, replace webuse labor, clear g byte wc=(we>12) if we<. la var wc "wife went to college" g earn=(whrs*ww) replace ww=ln(earn) la var ww "log earnings" keep if ww<. treatreg ww wa cit, treat(wc=wmed wfed) twostep est sto ts1 probit wc wmed wfed set type double predict xb, xb g res=cond(wc==1,normalden(xb)/normal(xb),-normalden(xb)/(1-normal(xb))) drop xb reg ww wa cit wc res est sto byhand1 treatreg ww wa cit, treat(wc=wmed wfed wa cit) twostep est sto ts2 probit wc wmed wfed wa cit predict xb, xb g r2=cond(wc==1,normalden(xb)/normal(xb),-normalden(xb)/(1-normal(xb))) reg ww wa cit wc r2 est sto byhand2 esttab ts1 byhand1 ts2 byhand2, nogaps mti ivreg2 ww wa cit (wc=wmed wfed) est sto iv1 reg wc wmed wfed wa cit predict wchat ivreg2 ww wa cit (wc=wchat) est sto iv18 ivpois earn wa cit, endog(wc) exog(wmed wfed) est sto ivpois esttab ts1 ts2 iv1 iv18 ivpois, nogaps mti scalar(idstat widstat) Note above how the -ivreg2- results are the same, except that using the predicted value as the excluded instrument artificially pumps up the weak-ID stat. See also "Causal inference with observational data" in Stata Journal 7(4): 507-541 (http://www.stata-journal.com/sj7-4.html). On Wed, May 28, 2008 at 7:07 AM, Shehzad Ali <sia500@york.ac.uk> wrote: > 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. > > -----Original Message----- > From: Gordon <gaogstat@gmail.com> > Sent: 27 May 2008 16:30 > > 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/

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

**st: RE: Different approcah to estimate treatment effect***From:*"Shehzad Ali" <sia500@york.ac.uk>

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