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Re: st: re: overid error


From   JOHN ANTONAKIS <john.antonakis@unil.ch>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: re: overid error
Date   Tue, 24 Nov 2009 08:34:56 +0100

Hi Kit:

Thanks for your note.

In fact, reg3 with the 2sls option provides precisely the same estimates as does ivreg/ivreg2. Whether using the 2sls or default 3sls, the cross-equation disturbance is estimated both in reg3 and ivreg2 (in an exchange awhile back when discussing the endog option in ivreg2 I establish that the cross-equation distrubance is estimated, remember?). reg3 does the job of a limited information 2sls estimator when 2sls is invoked in precisely the same way as does ivreg2, so I sincerely hope you don't disable the option to use overid with other estimators, like 2sls in reg3. In fact, the old overid provided precisely the same overid statistic (and of course, estimates) as does the ivreg2 routine. I think that you may have changed something in that regard in the new overid code. For example:


. use http://fmwww.bc.edu/ec-p/data/hayashi/griliches76.dta
. xi i.year
. ivreg2 lw s expr tenure rns smsa _I* (iq=med kww age mrt)

IV (2SLS) estimation
--------------------

Estimates efficient for homoskedasticity only
Statistics consistent for homoskedasticity only

Number of obs = 758 F( 12, 745) = 45.91 Prob > F = 0.0000 Total (centered) SS = 139.2861498 Centered R2 = 0.4255 Total (uncentered) SS = 24652.24662 Uncentered R2 = 0.9968 Residual SS = 80.0182337 Root MSE = .3249

------------------------------------------------------------------------------
lw | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
iq | .0001747 .0039035 0.04 0.964 -.007476 .0078253 s | .0691759 .0129366 5.35 0.000 .0438206 .0945312 expr | .029866 .0066393 4.50 0.000 .0168533 .0428788 tenure | .0432738 .0076271 5.67 0.000 .0283249 .0582226 rns | -.1035897 .029481 -3.51 0.000 -.1613715 -.0458079 smsa | .1351148 .0266573 5.07 0.000 .0828674 .1873623 _Iyear_67 | -.052598 .0476924 -1.10 0.270 -.1460734 .0408774 _Iyear_68 | .0794686 .0447194 1.78 0.076 -.0081797 .1671169 _Iyear_69 | .2108962 .0439336 4.80 0.000 .1247878 .2970045 _Iyear_70 | .2386338 .0509733 4.68 0.000 .1387281 .3385396 _Iyear_71 | .2284609 .0437436 5.22 0.000 .1427251 .3141967 _Iyear_73 | .3258944 .0407181 8.00 0.000 .2460884 .4057004 _cons | 4.39955 .2685443 16.38 0.000 3.873213 4.925887
------------------------------------------------------------------------------
Underidentification test (Anderson canon. corr. LM statistic): 52.436 Chi-sq(4) P-val = 0.0000
------------------------------------------------------------------------------
Weak identification test (Cragg-Donald Wald F statistic): 13.786 Stock-Yogo weak ID test critical values: 5% maximal IV relative bias 16.85 10% maximal IV relative bias 10.27 20% maximal IV relative bias 6.71 30% maximal IV relative bias 5.34 10% maximal IV size 24.58 15% maximal IV size 13.96 20% maximal IV size 10.26 25% maximal IV size 8.31
Source: Stock-Yogo (2005).  Reproduced by permission.
------------------------------------------------------------------------------
Sargan statistic (overidentification test of all instruments): 87.655 Chi-sq(3) P-val = 0.0000
------------------------------------------------------------------------------
Instrumented:         iq
Included instruments: s expr tenure rns smsa _Iyear_67 _Iyear_68 _Iyear_69
                     _Iyear_70 _Iyear_71 _Iyear_73
Excluded instruments: med kww age mrt
------------------------------------------------------------------------------

. reg3 (lw =iq s expr tenure rns smsa _I*) (iq=med kww age mrt s expr tenure rns smsa _Iyear
> _67 _Iyear_68 _Iyear_69 _Iyear_70 _Iyear_71 _Iyear_73)

Three-stage least-squares regression
----------------------------------------------------------------------
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
----------------------------------------------------------------------
lw                758     12    .3249076    0.4255     560.57   0.0000
iq                758     15    11.09542    0.3354     384.36   0.0000
----------------------------------------------------------------------

------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lw           |
iq | .0001747 .0039035 0.04 0.964 -.007476 .0078253 s | .0691759 .0129366 5.35 0.000 .0438206 .0945312 expr | .029866 .0066393 4.50 0.000 .0168533 .0428788 tenure | .0432738 .0076271 5.67 0.000 .0283249 .0582226 rns | -.1035897 .029481 -3.51 0.000 -.1613715 -.0458079 smsa | .1351148 .0266573 5.07 0.000 .0828674 .1873623 _Iyear_67 | -.052598 .0476924 -1.10 0.270 -.1460734 .0408774 _Iyear_68 | .0794686 .0447194 1.78 0.076 -.0081797 .1671169 _Iyear_69 | .2108962 .0439336 4.80 0.000 .1247878 .2970045 _Iyear_70 | .2386338 .0509733 4.68 0.000 .1387281 .3385396 _Iyear_71 | .2284609 .0437436 5.22 0.000 .1427251 .3141967 _Iyear_73 | .3258944 .0407181 8.00 0.000 .2460884 .4057004 _cons | 4.39955 .2685443 16.38 0.000 3.873213 4.925887
-------------+----------------------------------------------------------------
iq           |
med | .2721714 .159856 1.70 0.089 -.0411405 .5854834 kww | .4427703 .0691238 6.41 0.000 .3072901 .5782505 age | -.9913636 .2201974 -4.50 0.000 -1.422943 -.5597846 mrt | -.9452683 .9319855 -1.01 0.310 -2.771926 .8813898 s | 2.624565 .2823757 9.29 0.000 2.071119 3.178011 expr | .0564757 .25055 0.23 0.822 -.4345933 .5475448 tenure | .690506 .2700872 2.56 0.011 .1611447 1.219867 rns | -2.581106 .9398181 -2.75 0.006 -4.423116 -.7390961 smsa | .0621573 .9124468 0.07 0.946 -1.726205 1.85052 _Iyear_67 | .7564285 1.638293 0.46 0.644 -2.454566 3.967423 _Iyear_68 | .2275306 1.557616 0.15 0.884 -2.82534 3.280401 _Iyear_69 | 2.026613 1.505147 1.35 0.178 -.9234201 4.976646 _Iyear_70 | 5.605308 1.677934 3.34 0.001 2.316619 8.893998 _Iyear_71 | 5.213041 1.545298 3.37 0.001 2.184313 8.241768 _Iyear_73 | 4.444228 1.467061 3.03 0.002 1.56884 7.319616 _cons | 68.58568 4.056543 16.91 0.000 60.63501 76.53636
------------------------------------------------------------------------------
Endogenous variables:  lw iq
Exogenous variables:   s expr tenure rns smsa _Iyear_67 _Iyear_68 _Iyear_69
    _Iyear_70 _Iyear_71 _Iyear_73 med kww age mrt
------------------------------------------------------------------------------

. overid

Number of equations : 2
Total number of exogenous variables in system : 16
Number of estimated coefficients : 29
Hansen-Sargan overidentification statistic :    87.655
Under H0, distributed as Chi-sq(3), pval = 0.0000

. which overid
H:\stata\ado\plus\o\overid.ado
*! overid V2.0.3 4Feb2007
*! Authors C F Baum, Vince Wiggins, Steve Stillman, Mark Schaffer


Regards,
John.

____________________________________________________

Prof. John Antonakis
Associate Dean Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland

Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305

Faculty page:
http://www.hec.unil.ch/people/jantonakis

Personal page:
http://www.hec.unil.ch/jantonakis
____________________________________________________



On 24.11.2009 04:17, Kit Baum wrote:
> <>
> John used the 2sls option on reg3 on a two-equation system and commented:
>
> What is a little odd, though, is that the Hansen Sargan statistic
> returned by overid (using a simple two-equation model with 2SLS) is not
> the same as ivreg2:
>
>
> Not really odd, in that the overid statistic from reg3 (even if you have estimated the model ignoring cross-equation correlation) is calculated for the entire system of two equations and five coefficients, rather than the single equation containing only two of those coefficients. As the -overid- help file describes, the overid stat for 3SLS takes account of the entire system estimates. Perhaps we should disable its use if you use ols, sure, 2sls or mvreg options in reg3, as it is not obvious that it is appropriate in those circumstances. It is meant to be used after 3sls estimation.
>
> Kit
>
> Kit Baum | Boston College Economics & DIW Berlin | http://ideas.repec.org/e/pba1.html > An Introduction to Stata Programming | http://www.stata-press.com/books/isp.html > An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.html
>
>
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