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| From | Aggie Chidlow <mojamalarybka@googlemail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | Re: st: collin |
| Date | Sat, 12 Mar 2011 16:10:44 +0000 |
Here are the results for sum y*
Variable | Obs Mean Std. Dev. Min Max
-------------+-------------------------------------------------------------------
y_hat | 2251 .3609488 .1824771 4.26e-06 1
On Sat, Mar 12, 2011 at 3:52 PM, DE SOUZA Eric
<eric.de_souza@coleurope.eu> wrote:
> This is exactly what I thought you had, not just collinearity but perfect collinearity.
> The question is: why are you getting perfectly collinearity?
> Your y's appear to be constants.
> Could you produce the results of -summarize y*- ?
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Aggie Chidlow
> Sent: 12 March 2011 16:45
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: collin
>
> Dear Eric,
> Thank you for your advice... will definetly look this reference up.
>
> When I run my model with all dummies as the reviewer wants me to:
>
> probit y x1 x2 x3 lnx4 x5 y98 y99 y00 y01 y02
>
> where:
> y98=463
> y99=494
> y00=425
> y01=406
> y02=376
> y03=88 -not included in the model due to dummies trap
>
> I get the regression results that say the follwing:
> note: y00 omitted because of collinearity
> note: y01 omitted because of collinearity
> note: y02 omitted because of collinearity
>
> The coefficients for y00 y01 and y02 are not reported in the model and there is a note which says y00 (omitted); y01 (omitted) and y02 (omitted).
>
> By the way the collin for year dummies is as follow:
> Collinearity Diagnostics
>
> SQRT R-
> Variable VIF VIF Tolerance Squared
> ----------------------------------------------------
> y98 -3.37e+13 . -0.0000 1.0000
> y99 -3.53e+13 . -0.0000 1.0000
> y00 -3.16e+13 . -0.0000 1.0000
> y01 -3.05e+13 . -0.0000 1.0000
> y02 -2.87e+13 . -0.0000 1.0000
> y03 -7.74e+12 . -0.0000 1.0000
> ----------------------------------------------------
> Mean VIF -2.79e+13
>
> Cond
> Eigenval Index
> ---------------------------------
> 1 2.0000 1.0000
> 2 1.0000 1.4142
> 3 1.0000 1.4142
> 4 1.0000 1.4142
> 5 1.0000 1.4142
> 6 1.0000 1.4142
> 7 0.0000 .
> ---------------------------------
> Condition Number .
> Eigenvalues & Cond Index computed from scaled raw sscp (w/ intercept)
> Det(correlation matrix) -0.0000
>
>
> I would appreciate any suggestions.
>
> Many thanks in advance.
> .
>
>
> On Sat, Mar 12, 2011 at 11:16 AM, DE SOUZA Eric <eric.de_souza@coleurope.eu> wrote:
>> I haven't been following this thread till now.
>> Jeffrey Wooldridge in his introductory textbook (page 99, international edition) does not encourage use of the VIF . The variance of a coefficient depends on three factors: the standard error of the regression, the total sample variation in the variable attached to the coefficient and the partial R2 . Concentrating on the partial R2 has no justification, even less so the rule of 10.
>>
>> However, in this case, the referee will probably have to be satisfied in some way or the other.
>>
>> Aggie, when you say that the dummies were dropped on account of collinearity, what exactly do you mean?
>>
>> Eric
>>
>>
>> Eric de Souza
>> College of Europe
>> Brugge (Bruges), Belgium
>> http://www.coleurope.eu
>>
>>
>> -----Original Message-----
>> From: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Syed Basher
>> Sent: 12 March 2011 11:57
>> To: statalist@hsphsun2.harvard.edu
>> Subject: Re: st: collin
>>
>> Dear Aggie,
>>
>> I recently used VIF in one of my papers. You can find the discussion here:
>> http://ideas.repec.org/p/pra/mprapa/27348.html
>> -- See p. 14 (footnote 23) and p. 22
>>
>> A general rule of thumb in economics is a VIF>10 indicates harmful collinearity.
>> Hope you find this useful.
>>
>> Syed Basher
>> Doha, Qatar.
>>
>>
>>
>>
>> ----- Original Message ----
>> From: Aggie Chidlow <mojamalarybka@googlemail.com>
>> To: statalist@hsphsun2.harvard.edu
>> Sent: Sat, March 12, 2011 1:36:26 AM
>> Subject: Re: st: collin
>>
>> Dear Charls and Syed,
>> Thank you very much for your comments and suggestions.
>>
>> I would be thankful very much for your help Syed regarding how to interpret VIF professionaly. Any advice/references would be very much appreciated.
>>
>> Many thanks,Aggie
>>
>> On Thu, Mar 10, 2011 at 3:14 PM, Syed Basher <syed.basher@yahoo.com> wrote:
>>> Hi Aggie,
>>>
>>> I think diagnostic checking such as VIF comes before estimation, that
>>> is we first check the extent of collinearity among variables using
>>> VIF then decide which variables to include in the estimation. After
>>> running VIF, you can do
>> two
>>> sets of estimation: one with all dummies (what the reviewer asked
>>> for) and another with least collinear dummies (as you already did),
>>> this way the difference between two results will show up. As Charles
>>> mentioned, it is
>> better
>>> to follow what the reviewer has asked for. If you wanted to know how
>>> to interpret VIF results professionally, let me know.
>>>
>>> Syed Basher
>>> Doha, Qatar
>>>
>>>
>>>
>>> ----- Original Message ----
>>> From: Aggie Chidlow <mojamalarybka@googlemail.com>
>>> To: statalist@hsphsun2.harvard.edu
>>> Sent: Thu, March 10, 2011 4:30:51 PM
>>> Subject: st: collin
>>>
>>> Dear Stata users,
>>>
>>> I was appreciate some help regarding "collin"
>>>
>>> I just got a paper back from a reviewer and he/she wants me to
>>> include all my year dummies (i.e. y98 y99 y00 y01 y02 y03) in the
>>> following
>>> model: probit y x1 x2 x3 lnx4 x5 y98 y99 y00 y01 y02
>>>
>>> Previusly in the model I only included two year dummies (i.e y99 and
>>> y01) as the others we omitted automatically due to collinearity.
>>> I mentioned that in the paper, however, he/she says it is
>>> unsatisfactory and I should include them all and than comment on VIF.
>>>
>>> Please, can somebody tell me how I can go about this?
>>> Any advise and/or references will be more than appreciated.
>>>
>>> Many thanks in advance.
>>> Aggie
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