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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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