# Re: st: Collinearity in 2SLS

 From Tinna To statalist@hsphsun2.harvard.edu Subject Re: st: Collinearity in 2SLS Date Thu, 15 Sep 2005 18:45:21 -0400

```I tried to rescale the endogenous variable... actually used your pick
of numbers (1000).  It sure did shrink the endogenous variables
coefficient. However, it shrunk according to the scaling, so the
interpretations stays the same. And the number is way to large to make
sense in that context.

The results are below. The first coefficient is the one of interest,
and it did change as I mentioned before. However it only changed in
number, but not in terms of the real effect DP1 has on the dependent
variable. I tried rescaling the instrument also, but it didn't do
anything either.

Sorry if I am being slow here.  My code is below.

Tinna

. generate  dailysmoke1000= dailysmoke*1000

. ivreg   hrstotal  centage centagesq (DP1= dailysmoke1000 )  edu2
edu3 edu4 edu5 edu6 marr2 marr3 marr4 ch
> ildren health   if male==1 &  empl3!=1 &  empl5!=1 & empl7!=1

Instrumental variables (2SLS) regression

Source |       SS       df       MS              Number of obs =     404
-------------+------------------------------           F( 13,   390) =    1.95
Model | -17608.5123    13 -1354.50095           Prob > F      =  0.0235
Residual |  116855.128   390  299.628533           R-squared     =       .
Total |  99246.6155   403  246.269517           Root MSE      =   17.31

------------------------------------------------------------------------------
hrstotal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
DP1 |   23.35439    9.64544     2.42   0.016     4.390822    42.31795
centage |   .0961063   .1157894     0.83   0.407    -.1315433    .3237558
centagesq |  -.0162521   .0053245    -3.05   0.002    -.0267204   -.0057839
edu2 |  -3.520445   3.875886    -0.91   0.364    -11.14069    4.099799
edu3 |   1.202756   2.779328     0.43   0.665    -4.261585    6.667097
edu4 |  -4.174515   2.603464    -1.60   0.110    -9.293094    .9440648
edu5 |  -6.479983   3.118394    -2.08   0.038    -12.61095   -.3490167
edu6 |   -5.05074   3.751378    -1.35   0.179     -12.4262    2.324715
marr2 |    1.79778   4.550819     0.40   0.693    -7.149427    10.74499
marr3 |   4.296739   4.445934     0.97   0.334    -4.444259    13.03774
marr4 |  -8.389899   11.09344    -0.76   0.450    -30.20032    13.42052
children |   .0359085   .7727457     0.05   0.963     -1.48336    1.555177
health |  -3.022827   1.383256    -2.19   0.029    -5.742398    -.303256
_cons |   52.97049   5.593036     9.47   0.000     41.97421    63.96676
------------------------------------------------------------------------------
Instrumented:  DP1
Instruments:   centage centagesq edu2 edu3 edu4 edu5 edu6 marr2 marr3 marr4
children health dailysmoke1000
------------------------------------------------------------------------------

. ivreg   hrstotal  centage centagesq (DP1000= dailysmoke )  edu2 edu3
edu4 edu5 edu6 marr2 marr3 marr4 chi
> ldren health   if male==1 &  empl3!=1 &  empl5!=1 & empl7!=1

Instrumental variables (2SLS) regression

Source |       SS       df       MS              Number of obs =     404
-------------+------------------------------           F( 13,   390) =    1.95
Model | -17608.5123    13 -1354.50095           Prob > F      =  0.0235
Residual |  116855.128   390  299.628533           R-squared     =       .
Total |  99246.6155   403  246.269517           Root MSE      =   17.31

------------------------------------------------------------------------------
hrstotal |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
DP1000 |   .0233544   .0096454     2.42   0.016     .0043908     .042318
centage |   .0961063   .1157894     0.83   0.407    -.1315433    .3237558
centagesq |  -.0162521   .0053245    -3.05   0.002    -.0267204   -.0057839
edu2 |  -3.520445   3.875886    -0.91   0.364    -11.14069    4.099799
edu3 |   1.202756   2.779328     0.43   0.665    -4.261585    6.667097
edu4 |  -4.174515   2.603464    -1.60   0.110    -9.293094    .9440648
edu5 |  -6.479983   3.118394    -2.08   0.038    -12.61095   -.3490167
edu6 |   -5.05074   3.751378    -1.35   0.179     -12.4262    2.324715
marr2 |    1.79778   4.550819     0.40   0.693    -7.149427    10.74499
marr3 |   4.296739   4.445934     0.97   0.334    -4.444259    13.03774
marr4 |  -8.389899   11.09344    -0.76   0.450    -30.20032    13.42052
children |   .0359085   .7727457     0.05   0.963     -1.48336    1.555177
health |  -3.022827   1.383256    -2.19   0.029    -5.742398    -.303256
_cons |   52.97049   5.593036     9.47   0.000     41.97421    63.96676
------------------------------------------------------------------------------
Instrumented:  DP1000
Instruments:   centage centagesq edu2 edu3 edu4 edu5 edu6 marr2 marr3 marr4
children health dailysmoke
------------------------------------------------------------------------------

On 9/15/05, Tinna <statalist@gmail.com> wrote:
> Thanks Mark,
> I think you understood my problem right and I am going to try your
> suggestions.  It is very good to know what this problem is called.
>
> Tinna
>
>
>
>
> On 9/15/05, Mark Schaffer <M.E.Schaffer@hw.ac.uk> wrote:
> > Tinna,
> >
> > I don't think you have a collinearity problem, strictly speaking.  Rather,
> > it sounds like you have a scaling problem that could be causing you
> > numerical problems with your estimator.
> >
> > When you say the instrumented coefficient in the second stage is "blowing
> > up", do you mean that the estimated size of the coefficient is very large
> > (several+ orders of magnitude) compared to the other coefficients?  Then
> > you could indeed have a scaling problem.
> >
> > The way to find out (and to deal with the problem, if it exists) is to
> > rescale your endogenous variable.  Just create a new variable that is 1000
> > or whatever times your original variable, and use it in the regression
> > rescaling too.  It's easy enough to work out what to do once you see what
> > is going on.
> >
> > Hope this helps.
> >
> > Cheers,
> > Mark
> >
> >
> > > Dear Statalisters,
> > >
> > > I am running 2SLS estimations. The instrument used in the first stage
> > > is quite good according to traditional standards and tests and its
> > > coefficient in the first stage regression is highly significant.
> > > HOWEVER, the coefficient although significant is very small. I think
> > > this is causing collinearity (if it can be called that in this context
> > > - makes sense to me). The instrumented coefficient in the second stage
> > > is blowing up big time. However, it is significant and my
> > > Durbin-Wu-Hausman test is indicating endogeneity, so that the 2SLS
> > > would really be what is called for.
> > >
> > > 1. Someone told me that I could still trust the sign on the
> > > instrumented coefficient, although it is blown up.  This "someone"
> > > says they read it "somewhere" but are not sure where.  I have reached
> > > the end of the Internet without finding much. Can I trust the sign of
> > > the instrumented coefficient?
> > >
> > > 2. Can I trust my Durbin-Wu-Hausman test?
> > >
> > > 3. Any suggestion for what I should do?
> > >
> > > Tinna
> > >
> > > *
> > > *   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/
> > >
> >
> >
> > Prof. Mark Schaffer
> > Director, CERT
> > Department of Economics
> > School of Management & Languages
> > Heriot-Watt University, Edinburgh EH14 4AS
> > tel +44-131-451-3494 / fax +44-131-451-3294
> > email: m.e.schaffer@hw.ac.uk
> > web: http://www.sml.hw.ac.uk/ecomes
> >
> >
> >
> > __________________________________________________________________
> >
> > DISCLAIMER:
> >
> > This e-mail message is subject to http://www.hw.ac.uk/disclaim.htm
> > __________________________________________________________________
> >
> > *
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> >
>

*
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```