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RE: st: collin
From
DE SOUZA Eric <[email protected]>
To
"[email protected]" <[email protected]>
Subject
RE: st: collin
Date
Sat, 12 Mar 2011 17:50:32 +0100
If you had variables with the names y98 y99 y00 y01 y02 in your dataset, then -summarize y*- should produce one row under
Variable | Obs Mean Std. Dev. Min Max
for each of the variables:
y98
y99
y00
y01
y02
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Aggie Chidlow
Sent: 12 March 2011 17:41
To: [email protected]
Subject: Re: st: collin
Variable | Obs Mean Std. Dev. Min Max
-------------+----------------------------------------------------------
-------------+------------
y_hat | 2251 .3609488 .1824771 4.26e-06 1
This is the sum for y* (where y y98 y99 y00 y01 y02)
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
y_hat2| 2252 .3601243 .0537524 .102273 .3930885
On Sat, Mar 12, 2011 at 4:16 PM, Nick Cox <[email protected]> wrote:
> Your variables
>
> y y98 y99 y00 y01 y02
>
> should all be included in y*. Please show those too.
>
> Nick
>
> On Sat, Mar 12, 2011 at 4:10 PM, Aggie Chidlow
> <[email protected]> wrote:
>
>> 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
> <[email protected]> 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*- ?
>
> Aggie Chidlow
>
>>> 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
>>>
>>>
>>> On Sat, Mar 12, 2011 at 11:16 AM, DE SOUZA Eric <[email protected]> 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?
>>>>> From: Aggie Chidlow <[email protected]> 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?
>
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