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Re: st: RE: Tobit coefficients

From   <>
Subject   Re: st: RE: Tobit coefficients
Date   Thu, 26 Jul 2007 20:27:40 -0300 (ART)

Hi all,

I have a problem estimating predicted values that is
somehow related to the messages fro Lisa and May

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

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?!..

Thanks in advance,

----- Original Message -----
From: May Boggess <>
Subject: Re: st: RE: Tobit coefficients
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:

 sysuse auto 
 replace mpg=mpg-20
 tobit mpg len, ll(0)
 predict xb,xb
 predict y, ystar(0,.)  /*  Unconditional Expected
Value */
 predict e, e(0,.)      /*  Conditional on being
Uncensored */
 predict p, pr(0,.)     /*  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

 mfx, predict(ystar(0,.))


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