Re: st: RE: ivreg2 and xtoverid error

[John Antonakis <john.antonakis@unil.ch>]

Hi Mark

Thanks for the note.

I get exactly the same estimates and standard errors with -ivreg- and 
-ivregress-, with the cluster robust variance estimator.  When using 
-ivreg2- with the  -noid- option it works and I get the same estimates; 
more importantly, I also get the Hansen J-test, which is what interests 
me most (the -ivregress- estimator does not report an overid for 
cluster-robust vce's):

Hansen J statistic (overidentification test of all instruments): 
402.476, Chi-sq(404) P-val =  0.5121

Note, my estimates make sense and the regressors I expected to be 
significant are mostly significant. I guess I can assume that my 
estimates are consistent--and this because the endogeneity test is 
significant (from -ivregres-), right?

. estat endogenous

 Tests of endogeneity
 Ho: variables are exogenous

 Robust regression F(13,417)     =  119.753  (p = 0.0000)
   (Adjusted for 418 clusters in lead_number)

Best,
J.

____________________________________________________

Prof. John Antonakis, Associate Dean 
Faculty of Business and Economics
Department of Organizational Behavior
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 03.04.2010 15:16, Schaffer, Mark E wrote:
> John,
>
> You've got a very large number of instruments (almost 500), but also 13
> endogenous regressors.  The under- and weak-id statistics are calculated
> by -ranktest-, and it's running of memory because it works with the
> reduced form in matrix form (13 first-stage regressions, each with
> almost 500 regressors).
>
> If you use the -noid- option, it might address that problem problem (but
> you won't get the weak id stats).
>
> The 
>
> invsym(): matrix has missing values
>
> is different.  -ivreg2- is having problems inverting this huge Z'Z
> matrix  and -ivregress- isn't (I think possibly because the latter uses
> a different Mata matrix inversion function).  I do wonder whether the
> results you're getting actually make sense.
>
> Have you tried using the old -ivreg-, which uses -regress-?  It is
> numerically very stable and accurate.  Does it give the same results as
> -ivregress-?
>
> Cheers,
> Mark
>
>   
> > -----Original Message-----
> > From: owner-statalist@hsphsun2.harvard.edu 
> > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of 
> > John Antonakis
> > Sent: 03 April 2010 01:20
> > To: statalist@hsphsun2.harvard.edu
> > Subject: st: ivreg2 and xtoverid error 
> >
> > Hi:
> >
> > I am running Stata version 11 and have everything up to date.
> >
> > When running a two-stage model with ivreg2, I get the following error:
> >
> > . xi: ivreg2 sat (x1-x13=i.lead_n) , cluster(lead_n)
> > i.lead_number     _Ilead_numb_1-484   (naturally coded; _Ilead_numb_1 
> > omitted)
> >              quadcross():  3900  unable to allocate real 
> > <tmp>[5421,5421]
> >                 rkstat():     -  function returned error
> >                  <istmt>:     -  function returned error
> > r(3900);
> >
> > If I remove the vce cluster command I get another error:
> >
> > . xi: ivreg2 y (x1-x13=i.lead_n) ,
> > i.lead_number     _Ilead_numb_1-484   (naturally coded; _Ilead_numb_1 
> > omitted)
> >
> > invsym(): matrix has missing values
> >
> > This model is estimated fine with the official -ivregress- 
> > command, whether I use a cluster-robust or normal variance 
> > estimator, e.g.,
> >
> > . xi: ivregress 2sls y (x1-x13=i.lead_n) ,cluster(lead_n)
> > i.lead_number     _Ilead_numb_1-484   (naturally coded; _Ilead_numb_1 
> > omitted)
> >
> > Instrumental variables (2SLS) regression               Number of obs 
> > =     832
> >                                                        Wald chi2(13) =
> > 1020.01
> >                                                        Prob > 
> > chi2   =  
> > 0.0000
> >                                                        
> > R-squared     =  
> > 0.7020
> >                                                        Root 
> > MSE      =   
> > .2955
> >
> >                           (Std. Err. adjusted for 418 clusters in
> > lead_number)
> > --------------------------------------------------------------
> > ----------------
> >              |               Robust
> >            y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. 
> > Interval]
> > -------------+------------------------------------------------
> > ----------
> > -------------+------
> >           x1 |   .3849406   .0509433     7.56   0.000     .2850935    
> > .4847877
> >           x2 |  -.0452167   .0541093    -0.84   0.403    -.1512689    
> > .0608355
> >           x3 |  -.0214062   .0392473    -0.55   0.585    -.0983295    
> > .0555171
> >           x4 |   .0743079   .0528296     1.41   0.160    
> > -.0292363     
> > .177852
> >           x5 |   .1559398    .056997     2.74   0.006     .0442276    
> > .2676519
> >           x6 |    .168241   .0577832     2.91   0.004     
> > .0549879     
> > .281494
> >           x7 |  -.1359489   .0290323    -4.68   0.000    -.1928512   
> > -.0790465
> >           x8 |   .0485811   .0358857     1.35   0.176    -.0217535    
> > .1189157
> >           x9 |  -.1772587   .0512706    -3.46   0.001    -.2777472   
> > -.0767701
> >          x10 |   .1785753   .0570718     3.13   0.002     .0667166    
> > .2904339
> >          x11 |   .0309138   .0533183     0.58   0.562    -.0735883    
> > .1354158
> >          x12 |   .2282491   .0554658     4.12   0.000     
> > .1195381      
> > .33696
> >          x13 |  -.0723148   .0486346    -1.49   0.137    -.1676369    
> > .0230072
> >        _cons |   .4183937   .1616004     2.59   0.010     .1016627    
> > .7351247
> > --------------------------------------------------------------
> > ----------------
> > Instrumented:  x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13
> > Instruments:   _Ilead_numb_2 _Ilead_numb_3 _Ilead_numb_4 _Ilead_numb_5
> > [output snipped]
> >
> >
> > Interestingly, when I run it with -xtivreg-, the model is 
> > estimated fine; however, -xtoverid- gives me the following error:
> >
> > . xtoverid
> > invsym(): matrix has missing values
> > r(504);
> >
> > This is the same error that follows ivreg2 estimation.
> >
> > I suspect it might have something to do with the fact that I 
> > have a large number of instruments (fixed-effects, with 483 
> > dummies) and clustering on those fixed-effects.
> >
> > Best,
> > John.
> >
> > --
> > ____________________________________________________
> >
> > Prof. John Antonakis, Associate Dean
> > Faculty of Business and Economics
> > Department of Organizational Behavior
> > 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
> > ____________________________________________________
> >
> >
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