Thank you Le, I think you are right.
Alejandro
In message <[email protected]>
[email protected] writes:
> I think you can always bootstrap the standard errors. Write an ado
> file that does the whole procedure (including generating regressor,
> and instrumenting it). Correct me if I am wrong.
>
> Le
>
> On 12/1/06, Alejandro Delafuente
> <[email protected]> wrote:
> > Dear Philipp, many thanks for your response. I've checked the journal
reference
> > and you are right all the discussion is framed within nested models. I
guess I
> > should be more specific about my query:
> > My data is a household panel survey out of which I estimate my main
dependent
> > variable (my regressor of interest). Then I take this generated regressor
into
> > a probit model where my intuition tells me that since the sample is large
> > enough (around 17,000 observations) there should not be an important
problem of
> > efficiency, but I still wanted to check.
> > In a second type of model, the same predicted regressor is instrumented and
> > then taken into a probit, in some sort of two-stage fashion. Given that my
> > generated regressor is actually the dependent variable in the IV regression
> > (and not an instrument in itself) I wonder if it should not cause any
problem
> > for further inference as all source of variation is left for the residuals
in
> > the equation and hence I can proceed with the probit afterwards. If
you/anyone
> > think this is not really the case then I wonder if carrying out a Maximum
> > Likelihood IV Probit (ie, run both the IV regression and the probit all in
one
> > step to achieve efficiency) could solve part of the problem, but still need
to
> > find a solution for correcting the Standard Errors.
> > Again any clarification on this would be very helpful.
> > Alejandro
> > In message <[email protected]> [email protected] writes:
> > > Aljandrop,
> > >
> > > off-list, since I am not really sure.
> > >
> > > > Am trying to estimate a Probit Model where my main regressor is based on
> > > > predictions from another regression. Is there anything I should do to
> > adjust
> > > > for the standard errors given that am using an estimated dependent
variable?
> > >
> > > There is a recent issue of Political Analysis (Volume 13, Number 4,
> > > Autumn 2005), a "Special Issue on Multilevel Modeling for Large Clusters."
> > >
> > > If I remember correctly, one of the things almost all authors argue is
> > > that you do not need to adjust for standard errors. But, as the title of
> > > the special issue indicates, this advice is tailored to situations in
> > > which you have large clusters (lots of level-1 observation per level-2
> > > unit).
> > >
> > > Also, you may want to check out GLLAMM, in case you want to fit the
> > > model differently.
> > >
> > > HTH,
> > > Philipp
> > >
> > > *
> > > * For searches and help try:
> > > * http://www.stata.com/support/faqs/res/findit.html
> > > * http://www.stata.com/support/statalist/faq
> > > * http://www.ats.ucla.edu/stat/stata/
> > >
> >
> > --
> > Alejandro de la Fuente
> > Department of International Development/QEH
> > University of Oxford, Mansfield Road, Oxford OX1 3TB
> > Tel: 01865 281836
> >
> > *
> > * For searches and help try:
> > * http://www.stata.com/support/faqs/res/findit.html
> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
> >
>
>
> --
> ~~~~~~~~~~~~~~~~~~~~~~~~~~~
> Le Wang, Ph.D.
> Minnesota Population Center
> University of Minnesota
> (o) 612-624-5818
> *
> * For searches and help try:
> * http://www.stata.com/support/faqs/res/findit.html
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
--
Alejandro de la Fuente
Department of International Development/QEH
University of Oxford, Mansfield Road, Oxford OX1 3TB
Tel: 01865 281836
*
* For searches and help try:
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