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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Tobit coefficients |

Date |
Fri, 27 Jul 2007 11:34:55 -0400 |

Vitor <vfgate-demog@yahoo.com.br>: As I have pointed out before on this list, e.g. http://www.stata.com/statalist/archive/2007-04/msg00549.html http://www.stata.com/statalist/archive/2006-12/msg00466.html -tobit- is inappropriate if the zeros are not censored values (representing a negative y* that is observed as y=0). If the zeros are simply a point mass in the distribution of a nonnegative dep var, then -poisson- or -glm- are better options. In your case, the fact that many people receive no money from parents does not mean that their parents would like to take money from them (make a negative transfer), but are prevented by law from doing so. I would use -poisson- to estimate the response of ln(y) including zeros of y in the estimation, and then predict as usual. Why do you need a prediction conditional on y>0 anyway? Just to ensure predictions are nonnegative? Bad reason! Though -poisson- is designed for count variables, it works well for any model where E(y|x)=exp(xb). See Wooldridge (http://www.stata.com/bookstore/cspd.html) p.651 and surrounding text: "A nice property of the Poisson QMLE is that it retains some efficiency for certain departures from the Poisson assumption." In any case, the better way to get the predictions is to predict over the whole estimation sample, but first replace 1) AFTER=1, TREAT=1, TREAT*AFTER=1, then 2) AFTER=1, TREAT=0, TREAT*AFTER=0, etc. to generate predictions *for each observation* under different counterfactuals, then compare the means (the use of -poisson- is illustrative--any estimation command can be used, even -tobit-): sysuse auto, clear replace mpg=max(0,mpg-20) g treat=rep78>3 if !mi(rep78) la var treat "Fake Treatment Var" ren for after la var after "Fake After Var" g ta=treat*after poisson mpg len treat after ta predict m if e(sample) replace treat =1 replace after=1 replace ta=treat*after predict m11 if e(sample) replace treat =0 replace ta=treat*after predict m01 if e(sample) replace after=0 replace ta=treat*after predict m00 if e(sample) replace treat =1 replace ta=treat*after predict m10 if e(sample) su m01 local m01=r(mean) su m11 local m11=r(mean) di "Effect of treat given after=1 is " `m11'-`m01' su m00 local m00=r(mean) su m10 local m10=r(mean) di "DD of treat, after switching 0->1, is " `m11'-`m01'-`m10'+`m00' and so on. Then you could wrap all that in a rclass -program- and -bootstrap- it for standard errors on the estimated treatment effects, not that the diff-in-diff will really identify any true treatment effects. On 7/27/07, vfgate-demog@yahoo.com.br <vfgate-demog@yahoo.com.br> wrote: > Austin, thank you very much for your help. > > Yes, I was talking about -prvalue- from spost package. > > Let me try to explain my situation more clearly. I am > estimating a classic triple-difference model. So I > have three dummies: TREAT, AFTER and AFFECTED. I also > have TREAT*AFTER, TREAT*AFFECTED, AFTER*AFFECTED and > TREAT*AFTER*AFFECTED. And I also have a vector of > variables (sex, education, etc.) > > My dependent variable (Y) is the amount of money > received from parents. (many people receive zero). I > want to have predicted values E(Y|Y>0), and respective > Standard Errors, for every combination of the three > dummies: > > 1) TREAT=1, AFTER=1, TREAT=1, TREAT*AFTER=1, ... > 2) TRATE=0, AFTER=1, TREAT=0, TREAT*AFTER=0, ... > 3) etc... * * 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: Tobit coefficients***From:*Richard Williams <Richard.A.Williams.5@ND.edu>

**Re: st: RE: Tobit coefficients***From:*Vitor Felipe <vfgate-demog@yahoo.com.br>

**References**:**Re: st: RE: Tobit coefficients***From:*"Austin Nichols" <austinnichols@gmail.com>

**Re: st: RE: Tobit coefficients***From:*<vfgate-demog@yahoo.com.br>

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