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From |
<vfgate-demog@yahoo.com.br> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: Tobit coefficients |

Date |
Fri, 27 Jul 2007 01:01:58 -0300 (ART) |

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... (total of 8 possibilities) While keeping the set controlling variables (sex, education, etc.) constant at some values. I want the Standard Errors because I want to check if the estimated E(Y|Y>0) are significant. With this estimated values, I will fill the cell of a triple-diference table. Like the one on page 30, here (if you want to look at): CARVALHO (2004) http://wwwtest.aup.edu/lacea2005/system/step2_php/papers/decarvalhofilho_idec.pdf I suppose the -predict- strategy you proposed could work. But in fact I need predicted values for a certain vector of X, not at every observed value of X. In any case, it would be good to have a faster way, because I will estimate for every each one of the 8 possibilities listed above. Besied, I need to do this for 10 different years! Even if I was going to do that manually by -predict-, it would be very time consuming, and I most certainly would make a typing error. The -predict- algorithmon you sent is great. Thank you very much! Already helped a lot to clear the situation to me. But if you or anyone else has an alternative faster/eaiser solution, it would be also great. Thank you for your help, Vitor --- Austin Nichols <austinnichols@gmail.com> wrote: > > Vitor > -prvalue- is some user-written code, I guess > (http://www.indiana.edu/~jslsoc/spost.htm)? > I'm not quite sure what standard error you want--but > it seems plain > that -mfx- has nothing to do with it. You can use > the formulas in > May's email mechanically to plug into -nlcom- and > get a point estimate > and SE on anything, I suppose. However, the > estimate of y conditional > on y>0 (and its SE) and X equal to the sample mean > of X need not > *mean* anything in particular--the mean of X could > even be an > infeasible value for X, so not a particularly > interesting point at > which to calculate predictions. More interesting is > to calculate > predictions at every observed value of X. > That said, try this on for size [aside: can anyone > give an easier method?]: > > clear > sysuse auto > replace mpg=mpg-20 > version 7 > tobit mpg len, ll(0) > predict xb,xb > predict y, ystar(0,.) > la var y "E(y|x) unconditional expected y" > predict e, e(0,.) > la var e "E(y|x,y>0) conditional on y being > uncensored, from predict" > predict p, pr(0,.) > la var p "E(y>0) probability uncensored" > gen > m_e=xb+_b[_se]*normden((0-xb)/_b[_se])/norm(-((0-xb)/_b[_se])) > la var m_e "E(y) conditional on y being uncensored, > manual calc" > su e m_e > su len, meanonly > loc v=r(mean) > local xb="(`v'*_b[len]+_b[_cons])" > di "Estimate of E(y|x=E(X),y>0) and its SE" > nlcom > `xb'+_b[_se]*normden((-`xb')/_b[_se])/norm((`xb')/_b[_se]) > di "Now get SE for two types of predictions for each > obs:" > predict stdp, stdp > g hixb=xb+1.96*stdp > g loxb=xb-1.96*stdp > g se=. > loc n=_N > qui { > forv i=1/`n' { > su len in `i', meanonly > loc v=r(mean) > local xb="(`v'*_b[len]+_b[_cons])" > nlcom > `xb'+_b[_se]*normden((-`xb')/_b[_se])/norm((`xb')/_b[_se]) > mat v=r(V) > replace se=sqrt(v[1,1]) in `i' > } > } > g hie=e+1.96*se > g loe=e-1.96*se > version 8.2 > sort len > tw rarea hixb loxb len || scatter xb len, yli(0) > c(l) name(xb) > tw rarea hie loe len || scatter e len, yli(0) c(l) > name(e) > gr combine xb e, ycommon > > > On 7/26/07, vfgate-demog@yahoo.com.br > <vfgate-demog@yahoo.com.br> wrote: > I have a problem estimating predicted values that is > somehow related to the messages fro Lisa and May > below. > I estimated I tobit model and I want E(y|x,y>0) > conditional on vector x on its mean values. After > reading the messages below I understand that the > following command gives me what I want: > mfx compute, predict(e(0,.)) > (It also gives me the marginal effects, but I don't > need them) > My problem is that mfx does not give me the Standard > Error of E(y|x,y>0). And I need this. > I have tried the prvalue command. Simply typing > "prvalue", with no options", it gives me the > predicted > value and a confidence interval, which would be > enough. But what prvalue estimate is E(y|x), and not > E(y|x,y>0). > Does anyone know how to make prvalue estimate > E(y|x,y>0) after a Tobit model, rather than E(y|x)? > Or is there another way to do what I want? > Or am I getting it all wrong from the beginning and > my > question makes no sense at all?!.. > > ----- Original Message ----- > From: May Boggess <mboggess@stata.com> > Date: 03 Jan 2005 16:57:24 -0600 > > On Monday, Lisa wrote: > For the following command: > tobit facil4p $demo5, ll(0); > mfx compute, predict(e(0,.)); > does this give marginal effects in terms of > 1) E(y|x,y>0) OR 2) E(y|x) = P(y>0) * E(y|x,y>0) > If it is (1) (which I am pretty sure that it is) > could someone please tell me how to specify (2) in > STATA. > > It gives (1). Here is an example that shows how the > various > predictions available after -tobit- are calculated: > > clear > sysuse auto > replace mpg=mpg-20 > version 7 > tobit mpg len, ll(0) > predict xb,xb > predict y, ystar(0,.) > la var y "Unconditional Expected Value" > predict e, e(0,.) > la var e "Conditional on being Uncensored" > predict p, pr(0,.) > la var p "Probability Uncensored" > gen myy = p*e > sum y myy > gen mye = xb + _b[_se]*normden((0-xb)/_b[_se])/p > sum e mye > gen myp = norm(-((0-xb)/_b[_se])) > sum p myp > > This shows that ystar is the function Lisa is > looking for in (2). > That means she can calculate the marginal effect, > and its standard > error, as follows: > > mfx, predict(ystar(0,.)) > > --May > mmb@stata.com > Alertas do Yahoo! Mail em seu celular. Saiba mais em http://br.mobile.yahoo.com/mailalertas/ * * 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:*"Austin Nichols" <austinnichols@gmail.com>

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

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