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RE: st: Re: Endogenous right hand side variable


From   "McCullough, Kathleen" <kmccullough@cob.fsu.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Re: Endogenous right hand side variable
Date   Tue, 6 Jun 2006 19:02:52 -0400

Thank you to all who have helped with this.  I will try the suggestion from Mark that relates to the Wooldridge text.
 
The censored endogenous variable follows a distribution that seems consistent with a tobit specification.    If we were merely modeling the determinates of the use of this factor, I would use a Heckman model to control for selection bias, but in this case we are interested in this variables relation to another factor, so that approach will not with our research question.
 
Thank you all!
Kathleen
 
______________________________________________________________

________________________________

From: owner-statalist@hsphsun2.harvard.edu on behalf of Austin Nichols
Sent: Tue 6/6/2006 2:23 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Re: Endogenous right hand side variable



Rodrigo -
I assume Mark is referring to page 574 in
http://www.stata.com/bookstore/cspd.html

if (y1, y2) observed when y3>0,
run a tobit of y3 on Z,
save the residuals vhat, and
then
. ivreg2 y1 (y2 = vhat Z)

though it is not clear to me from the original post that any tobit is
required.  (A variable with a lot of zeros could mean a number of very
different things.)

On 6/6/06, Rodrigo A. Alfaro <ralfaro76@hotmail.com> wrote:
> Mark,
>
> I don't understand this point "Finally, estimate using IV with the predicted
> values of the endogenous regressor as the single excluded instrument." did
> you mean IV regression using the predicted values as instruments of the
> original endogenous?
>
> Rodrigo.
>
> ----- Original Message -----
> From: "Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>
> To: <statalist@hsphsun2.harvard.edu>
> Sent: Tuesday, June 06, 2006 12:54 PM
> Subject: RE: st: Re: Endogenous right hand side variable
>
>
> Kathleen, Rodrigo et al.,
>
> An alternative to Rodrigo's proposal of full system estimation is to use
> a trick that Wooldridge describes in his 2002 book.  The method takes
> advantage of the fact that simple IV gives consistent estimates.
>
> In a first step, estimate the Tobit-type relationship between the
> endogenous regressor and the full set of exogenous variables, including
> the excluded instruments.
>
> Then, get the predicted values from this Tobit.
>
> Finally, estimate using IV with the predicted values of the endogenous
> regressor as the single excluded instrument.
>
> No ex post adjustment of the var-cov matrix is necessary, since the last
> step is simple IV and therefore gets you consistent estimates of the
> var-cov matrix as well as the parameters.
>
> Very convenient, more efficient than IV on the original equation (which
> is also consistent, btw), but not as efficient as a full system
> estimation.
>
> Cheers,
> Mark
>
> > -----Original Message-----
> > From: owner-statalist@hsphsun2.harvard.edu
> > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of
> > Rodrigo A. Alfaro
> > Sent: 06 June 2006 17:44
> > To: statalist@hsphsun2.harvard.edu
> > Subject: Re: st: Re: Endogenous right hand side variable
> >
> > Deepankar,
> >
> > I think that Kathleen wants to use tobit because this
> > "endogenous right-hand side variable that is zero for over
> > half of the observations" let's say censored, not because the
> > variable is discrete. Also, I think that -heckman- is not the
> > solution (I mean using the command)... in that procedure
> > there is only one endogenous variable, in the problem we have
> > 2. A two-stage here involves do the tobit, makes the
> > predictions, put into the linear equation and estimate that.
> > It seems easy to do it, the problem is to adjust the standard errors.
> >
> > Rodrigo.
> >
> >
> > ----- Original Message -----
> > From: "Deepankar Basu" <basu.15@osu.edu>
> > To: <statalist@hsphsun2.harvard.edu>
> > Sent: Tuesday, June 06, 2006 12:08 PM
> > Subject: Re: st: Re: Endogenous right hand side variable
> >
> >
> > Kathleen,
> >
> > Some quick thoughts on your questions.
> >
> > 1. Why do you want the endogenous variable to be estimated
> > with a tobit
> > regression? The variable in question does not seem to  be a discrete
> > random variable. If it has a mixture distribution, you might
> > want to do
> > a joint likelihood estimation as Rodrigo suggests.
> >
> > 2. If you want a two-stage procedure with a tobit in the
> > first stage and
> > an OLS in the second, you could look at Heckman-type selection models.
> >
> > Deepankar
> >
> > On Tue, 2006-06-06 at 11:44 -0400, Rodrigo A. Alfaro wrote:
> > > Dear Kathleen,
> > >
> > > I don't know if there is procedure in Stata for this model.
> > The model
> > > sounds
> > > interesting, maybe there is some code in Gauss, Matlab or R.
> > >
> > > An alternative solution if to estimate the entire problem in a joint
> > > framework. You could write down the likelihood for this
> > problem and solve
> > > it
> > > using -ml- procedures in Stata. The book of Gould "MLE with
> > Stata" could
> > > help you in this matter.
> > >
> > > Rodrigo.
> > >
> > > ----- Original Message -----
> > > From: "McCullough, Kathleen" <kmccullough@cob.fsu.edu>
> > > To: <statalist@hsphsun2.harvard.edu>
> > > Sent: Tuesday, June 06, 2006 9:20 AM
> > > Subject: st: Endogenous right hand side variable
> > >
> > >
> > > I seem to be having some trouble posting today, so I am
> > going to send this
> > > message again.  I am sorry if it is a duplicate.
> > >
> > > I am estimating a model with a endogenous right-hand side
> > variable that is
> > > zero for over half of the observations.  Otherwise it is a
> > continuous
> > > variable. The dependent variable is continuous.  Is there a specific
> > > procedure to help control for this situation?  I was
> > concerned that using
> > > IVREG2 might not be effective as you cannot specify that
> > the endogenous
> > > variable should be estimated with a tobit model.  It does
> > not appear that
> > > there is a canned procedure with a first stage tobit and
> > second stage OLS
> > > model.
> > >
> > > I appreciate any suggestions that you might have.
> > >
> > > Regards,
> > > Kathleen
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