> could anyone offer any advice about estimating a model in which the
> dependent variable is censored and which has a binary endogenous
> variable? The dependent variable takes both positive and negative
> values, but has a mass at 0. I also need to use pweights and estimate
> clustered standard errors (I'm using survey data).
This is what I would do.
. set imho on :)
It looks like you have three equations here.
The first one takes care of the binary endogeneous variable. Let's say
this is probit.
The second one takes care of the point mass at zero, so it is a probit
again, where the dependent variable is an indicator of whether your
variable of interest hits zero or not. This is a standard way -- see for
instance -zip- in Stata.
The third one is the equation for the regression if the dependent variable
is not zero.
You can try to get hold of this stage by stage. You would need to extract
the inverse Mills' ratio from the first equation in the spirit of Heckman
/ treatreg model (so all concerns regarding identification in such models
apply). Then you plug it into the second and the third equations. You can
get cluster-corrected standard errors with probit and regress without much
problem, but it is going to be tough to ensure that you have the same
coefficients across the second and the third equations, if that's a
concern to you.
Your ultimate choice would be to write a maximum likelihood estimator with
the suite of -ml- commands. As far as this is a relatively straightforward
(although rather involved) observation-by-observation likelihood, Stata
can handle the -[pw=] , cluster(psu)- automatically with its -lf- form of
the -ml- routine. I think it suffices to specify something like -svy-
somewhere in the header of your maximum likelihood program, but I am not
100% sure about that.
--- Stas Kolenikov
-- Ph.D. student in Statistics at UNC-Chapel Hill
- http://www.komkon.org/~tacik/ -- [email protected]
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