Dear Martin,
Thanks so much for your reference to the webpage. I looked into the
page you mentioned. It talks about two different cases which may cause
this problem 1) this happens when a continuous variable (or a
combination of a continuous variable with dummy variables) is a great
predictor; and 2) hidden collinearity-- this happens when all the
regressors are dummy variables. Since my regressor list includes both
continuous variables and dummy variables (a lot of individual dummies),
can I simply ignore this warning message of "4,305 observations
completely determined. s.e. questionable" after my regression? Thanks!
Jinhu
On Fri, 14 Aug 2009 20:56:59 +0200
"Martin Weiss" <[email protected]> wrote:
>
> <>
>
>
> http://www.stata.com/support/faqs/stat/logitcd.html
>
>
>
>
> HTH
> Martin
>
>
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of J. Li
> Sent: Freitag, 14. August 2009 20:54
> To: [email protected]
> Subject: st: ordered probit panel data model with fixed effects
>
> Hi all,
>
> I have a question about running ordered probit panel data model with
> fixed effects. I know the discussion on the incidental parameters
> problem with using fixed effects nonlinear panel data model with
> large
> N and small T. However, my work involves estimating the basic ordered
> probit panel data model with individual fixed effects before applying
> adjustment to the estimates so that the bias could be corrected with
> respect to the parameters of interests. My problem now is with the
> simple estimation with ordered probit fixed effects model. The
> estimation line can be simplied as this:
>
> xi: oprobit health education income age gender i.ID, robust
> cluster(individual)
>
> where i.ID are all the individual dummies (there are 3,733 dummies in
> my case)
>
> After the estimation, I got the warning message as: 4,305
> observations
> completely determined. s.e. questionable. (note that this panel is
> with
> N=3,733 and T=6). Should I be concerned with this warning message?
> What
> does this mean? And what should I do to solve this problem? Thank you
> so much!
>
> Jinhu
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>
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