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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 21:50:49 +0100
I assume that the year dummies have been correctly created. This is easy to check: just create a new variable which is the sum of y98 to y03. It should be equal to one for every observation.
Then run a linear regression with y x1 x2 x3 lnx4 x5 y98 y99 y00 y01 y02.
Drop the x1 x2 x3 lnx4 x5 one by one to see which are perfectly collinear with the year dummies.
Then examine the ones which are perfectly collinear with the year dummies and decide whether you really need them. If so, then drop the year dummies and explain in your text.
Eric
Eric de Souza
College of Europe
Brugge (Bruges), Belgium
http://www.coleurope.eu
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Aggie Chidlow
Sent: 12 March 2011 18:10
To: [email protected]
Subject: Re: st: collin
Here it is, I hope.
Variable | Obs Mean Std. Dev. Min Max
-------------+----------------------------------------------------------
-------------+------------
y98 | 2252 .205595 .4042255 0 1
y99 | 2252 .2193606 .4139053 0 1
y00 | 2252 .1887211 .3913738 0 1
y01 | 2252 .1802842 .3845094 0 1
y02 | 2252 .1669627 .3730254 0 1
-------------+----------------------------------------------------------
-------------+-------------
y03 | 2252 .0390764 .1938197 0 1
I think I can see the problem one, but I will wait for your response to see if it matches yours.
On Sat, Mar 12, 2011 at 4:50 PM, DE SOUZA Eric <[email protected]> wrote:
> 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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