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: [email protected]
[mailto:[email protected]] On Behalf Of Martin Weiss
Sent: 04 March 2009 11:04
To: [email protected]
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: [email protected]
[mailto:[email protected]] Im Auftrag von [email protected]
Gesendet: Mittwoch, 4. März 2009 11:46
An: [email protected]
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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