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{Spam?}{Score: 0} RE: st: Re: ivreg etc.
Paolo,
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of
> Paolo Caruso
> Sent: 23 March 2006 21:56
> To: [email protected]; [email protected]
> Subject: Re: st: Re:
>
> I have used the usual ivreg however, I am not sure about the
> validity of doing so as it is including these interaction
> terms in the first stage which are in fact the exactly value
> of the instrumented variable simply multiplied by a dummy
> variable. Therefore, because the interaction terms are based
> on the variable I am instrumenting I believed that I would
> have to estimate the two stages separately not including the
> interaction terms.
>
> If you believe that I should simply use ivreg then I shall.
The problem is not that ivreg isn't valid; it's the specification you
are providing to ivreg. You need to make sure that you don't fall into
the "forbidden regression" trap - see my earlier posting and the
reference to Jeff Wooldridge's textbook. I suggest following Austin's
suggestion and use ivreg (or ivreg2) with an extended set of
instruments.
Cheers,
Mark
> Thank you all for your time.
>
> Regards,
>
> Paolo
>
> >>> [email protected] 03/23/06 18:44 PM >>>
> I agree that the -ivreg- approach is the best one.
>
> (The omission of s in the first stage regression was an oversight).
>
> Cheers,
> Arne
>
> On 23/03/06, Austin Nichols <[email protected]> wrote:
> > The code Arne supplies is an example of how to get the answer
> > requested by Paolo, but that answer is still incorrect.
> Just run that
> > code (including the variable s in both first and second
> stages, since
> > I think its omission was an oversight) to get:
> >
> > . qui use
> http://fmwww.bc.edu/ec-p/data/hayashi/griliches76.dta, clear
> > . qui regress iq med kww s expr tenure rns smsa . qui
> predict double
> > iq_hat . gen double intact = iq_hat*expr . qui regress lw iq_hat
> > intact s expr tenure rns smsa . replace iq_hat = iq . qui replace
> > intact = iq*expr . predict double res, residual . gen double res2 =
> > res^2 . qui sum res2 . scalar iv_mse = r(mean)*r(N)/e(df_r)
> . matrix b
> > = e(b) . matrix V = e(V)*(iv_mse/e(rmse)^2) . ereturn post b V .
> > ereturn display
> >
> --------------------------------------------------------------
> ----------------
> > | Coef. Std. Err. z P>|z|
> [95% Conf. Interval]
> >
> -------------+--------------------------------------------------------
> > -------------+--------
> > iq_hat | .0151927 .0061657 2.46 0.014
> .0031081 .0272772
> > intact | -.0005875 .0008488 -0.69 0.489
> -.0022512 .0010762
> > s | .0595681 .0188307 3.16 0.002
> .0226605 .0964757
> > expr | .1018824 .0848968 1.20 0.230
> -.0645121 .268277
> > tenure | .0295226 .0085698 3.44 0.001
> .0127262 .046319
> > rns | -.0440594 .0349568 -1.26 0.208
> -.1125736 .0244547
> > smsa | .1269817 .0301617 4.21 0.000
> .0678657 .1860976
> > _cons | 3.104997 .4183089 7.42 0.000
> 2.285127 3.924868
> >
> ----------------------------------------------------------------------
> > --------
> >
> > and then compare to the IV estimate:
> > . ivreg lw s expr tenure rns smsa (iq inta=kww med)
> >
> --------------------------------------------------------------
> ----------------
> > lw | Coef. Std. Err. t P>|t|
> [95% Conf. Interval]
> >
> -------------+--------------------------------------------------------
> > -------------+--------
> > iq | .1019604 .7498967 0.14 0.892
> -1.370186 1.574107
> > intact | -.031272 .2663122 -0.12 0.907
> -.554078 .4915341
> > s | -.0457328 .9088715 -0.05 0.960
> -1.829968 1.738502
> > expr | 3.17964 26.70891 0.12 0.905
> -49.25347 55.61275
> > tenure | .0273913 .0324433 0.84 0.399
> -.0362991 .0910818
> > rns | -.0274002 .1739247 -0.16 0.875
> -.3688374 .314037
> > smsa | -.0344371 1.379771 -0.02 0.980
> -2.743109 2.674235
> > _cons | -4.338579 64.363 -0.07 0.946
> -130.6916 122.0145
> >
> ----------------------------------------------------------------------
> > --------
> >
> > Note particularly the SEs and p-values on the two endog vars.
> >
> > I believe this impulse to "plug in" y2hat in various incorrect ways
> > comes from conceiving of the IV estimator as a two-step estimator,
> > which it is not. The better two-step estimator analogy uses the
> > control function approach, IMHO, since folks are less
> likely to apply
> > it incorrectly in a nonlinear second stage, or to square or
> interact
> > y2hat.
> >
> > Arne, do you concur?
> >
> > On 3/23/06, Arne Risa Hole <[email protected]> wrote:
> > > I suppose an alternative approach would be to do
> something like this:
> > >
> > > use
> http://fmwww.bc.edu/ec-p/data/hayashi/griliches76.dta, clear qui
> > > regress iq med kww expr tenure rns smsa predict double iq_hat gen
> > > double intact = iq_hat*expr qui regress lw iq_hat intact s expr
> > > tenure rns smsa replace iq_hat = iq replace intact =
> iq*expr predict
> > > double res, residual gen double res2 = res^2 qui sum res2 scalar
> > > iv_mse = r(mean)*r(N)/e(df_r) matrix b = e(b) matrix V =
> > > e(V)*(iv_mse/e(rmse)^2) ereturn post b V ereturn display
> > >
> > > i.e. get the forecast y2hat (iq_hat in the example) from the
> > > first-stage regression and interact this with the
> exogenous RHS var
> > > x1 (expr in the example). Then you replace the interaction with
> > > y2*x1 before calculating the residuals/MSE.
> > >
> > > Austin's suggestion is probably the better one though, but this
> > > seems to me to be ok.
> > >
> > > Cheers
> > > Arne
> > >
> > > On 23/03/06, Austin Nichols <[email protected]> wrote:
> > > > If your endog RHS var y2 is interacted with an exog RHS var x1,
> > > > then you have a "new" endog RHS var y2x1, and you may need
> > > > additional excluded instruments. Use -ivreg- as suggested.
> >
> > *
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> > * http://www.ats.ucla.edu/stat/stata/
> >
>
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