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st: RE: AW: mfx with tobit


From   "Carlo Fezzi" <c.fezzi@uea.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: AW: mfx with tobit
Date   Wed, 4 Mar 2009 11:18:54 -0000

Phil,

I would not use intreg to fit a Tobit on heteroskedastic data, the estimates
would be biased.

See the related post:

http://www.stata.com/statalist/archive/2006-12/msg00093.html


You might be better off using CLAD or writing your own likelihood function,
on this see:

http://www.stata.com/statalist/archive/2007-12/msg00600.html


Cheers,

Carlo


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Martin Weiss
Sent: 04 March 2009 11:04
To: statalist@hsphsun2.harvard.edu
Subject: st: AW: mfx with tobit


<> 

Phil,

-mfx- uses the conventions of -predict- after estimation commands, so you
can obtain information about the options for -mfx- after -tobit- from 


*************
help tobit postestimation
*************

Look for "Syntax for predict".





HTH
Martin


-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Phil1899@gmx.de
Gesendet: Mittwoch, 4. März 2009 11:46
An: statalist@hsphsun2.harvard.edu
Betreff: st: mfx with tobit

Hello,

I am running a regression model where the observations are censored from the
left (at zero). Therefore, I use a Tobit model. Due to heteroskedasticity
present in my data, I use the intreg command to obtain robust standard
errors. However, I have two questions in this regard. 

First, the coefficients obtained from Tobit estimation cannot be directly
interpreted. I found out that it is possible to use the mfx command to
obtain the marginal effects and that different options exist to compute
these effects; e.g. mfx, predict(e(0,1000)) or mfx predict(ystar(0,1000)).
However, these commands lead to quite different results and I am not able to
figure out which command is preferable in which situation?

Second, I have another dataset where the observations also pile up at zero,
however, in this dataset the dependent variable does not only take on
positive values, but sometimes also negative values. I am not sure which
estimation technique is preferable in this case? 

I would appreciate any help,

best regards

Phil

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