Statalist The Stata Listserver


[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

Re: st: Re:


From   "Austin Nichols" <austinnichols@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Re:
Date   Thu, 23 Mar 2006 12:54:31 -0500

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 <arnehole@gmail.com> 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 <austinnichols@gmail.com> 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.

*
*   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/



© Copyright 1996–2020 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index