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Re: st: xtlogit: panel data transformation's recast to double makes model incomputable

From   Tom <>
Subject   Re: st: xtlogit: panel data transformation's recast to double makes model incomputable
Date   Wed, 3 Apr 2013 16:33:22 +0200

Hi Jay,

Just to be clear -- I am also "on Nick's side". Anyway I don't want to
be talking about sides, somehow that's somewhat associated with a
battle and that is absolutely unrelated to this fruitful discussion.

I want to stress that I did start with theory, and that it made me
choose a fixed effects logistic regression. I am not somehow "choosing
the model that gives me an answer". For example, I used - clogit,
group()- but that is identical to -xtlogit, fe- so there is no change
there. Moreover, I used -logit- but only to verify if this problem was
related to panel data or not. For your information, I already started

Anyway, I think I may have a very important pointer that could help in
finding the cause of this issue:
when I run -ladder close- it returns absolutely nothing, i.e.:

 ladder close

Transformation         formula               chi2(2)       P(chi2)
cubic                  close^3                    .            .
square                 close^2                    .            .
identity               close                      .            .
square root            sqrt(close)                .            .
log                    log(close)                 .            .
1/(square root)        1/sqrt(close)              .            .
inverse                1/close                    .            .
1/square               1/(close^2)                .            .
1/cubic                1/(close^3)                .            .

I believe this is very much related to the problems I am having with
this variable, close. If I can determine why ladder refuses to give me
any transformations, then I may be able to figure out what is the
source of this issue.

I am not just trying to get an answer, I am interested in why ladder
is not returning any transformations because this may give me
information on the root cause of the problem.

Thanks again.


On Wed, Apr 3, 2013 at 2:13 PM, JVerkuilen (Gmail)
<> wrote:
> On Wed, Apr 3, 2013 at 7:54 AM, Tom <> wrote:
>> I understand what you mean. Note that I did not use any of these other
>> models until unexpected computational problems appeared.
> This is crazily simple but try rescaling the relevant price data. I
> forget what the scale of them is, but try dividing them by, say, 1000.
> The QR decomposition which is used computationally effectively does
> this, but your horribly skewed and massively differently scaled data
> may well be overwhelming even it.
> That said, I'm with Nick. This problem needs a major rethink. There
> are simply too many levers being pulled right now to get a believable
> answer. Even if a model converges it is still got the attached funk of
> all the troubleshooting and tricks you needed to do to make things
> work.
> In order:
> -Regenerate the data file "clean". Make sure all variables are scaled
> appropriately and candidate transformations are chosen. (e.g., with
> price data, log transform). This rules out mistakes or problems with
> the datafile.
> -Then lay out the model assumptions and framework.
> -Then fit candidate models in a predictable sequence.
> You may well have done a lot of this, but going back to square one and
> checking in sequence will help nail down what the problem is. You may
> well end up back where you started, but the effort will not be wasted.
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