This is more an econometrics question than a Stata question
but I hope you don�t mind.
Austin Nichols said:
> 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.
...
> 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."
Nevertheless, in the same book by Wooldridge (p.518 and
other parts of chapter 16) tobit is used to model what is
called �corner solutions outcomes�, which seems to be
exactly the case that Austin is warning against. So my
question is, are there any references you can recommend
about the superiority of poisson or glm over tobit when the
data comes from a corner solution?
Thanks,
Alejandro
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