Hi Paul,
Actually, my age variable does not vary over time. However Nick Cox
directed me to the STATA FAQ which explains why a coefficient on age is
estimated. It's just a variant parameterization of the traditional fixed
effects model.
Thanks,
Anirban
________________________________
Anirban Basu PhD
Section of General Internal Medicine
Department of Medicine
University of Chicago
5841 S. Maryland Ave, MC-2007
AMD B201
Chicago IL 60637
Tel: +1 773 834 1796
Fax: +1 773 834 2238
-----Original Message-----
From: Paul Millar [mailto:[email protected]]
Sent: Wednesday, March 02, 2005 11:50 PM
To: [email protected]
Subject: Re: st: Negative Binomial Fixed Effects estimator
Hello Anirban,
You can think of the "fixed effects" cross-sectional time series models
like pooled (or "stacked") cross-sections, with a dummy variable for each
unit of analysis. This makes them quite different than the more
traditional kind of "fixed effect" model, which is comparing treatment
effects.
Your model probably has a different age at each time point for each
person. That is why it varies, and why it has an effect.
- Paul Millar
Sociology
University of Calgary
At 01:32 PM 02/03/2005, you wrote:
>Hi,
>
> I am running Stata 8 SE.
>
>Does anyone know why xtnbreg, fe generates estimates for covariates that
>do not vary within group. For example, my outcome variable is y, which is
>a count variable and I have two period of data for every caseid.
>
>. xtnbreg y age time, fe nolog i(caseid)
>note: you are responsible for interpretation of non-count dep. variable
>note: 6096 groups (12192 obs) dropped due to all zero outcomes
>
>Conditional FE negative binomial regression Number of obs =
992
>Group variable (i): caseid Number of groups =
496
>
> Obs per group: min
> = 2
> avg
> = 2.0
> max
> = 2
>
> Wald
> chi2(2) = 188.18
>Log likelihood = -402.55541 Prob > chi2 =
0.0000
>
>---------------------------------------------------------------------------
---
> y | Coef. Std. Err. z P>|z| [95% Conf.
> Interval]
>-------------+-------------------------------------------------------------
---
> age
> | .0135565 .0216634 0.63 0.531 -.028903 .0560161
> time
> | -1.505405 .1098068 -13.71 0.000 -1.720623 -1.290188
> _cons
> | -2.399964 .5905975 -4.06 0.000 -3.557513 -1.242414
>---------------------------------------------------------------------------
---
>
>. sort caseid time
>
>. count if caseid ==caseid[_n-1] & age !=age[_n-1]
> 0
>
>Covariate age does not vary by period within caseid. What is the
>interpretation of the coefficient on age? Shouldn't age drop out in a
>fixed effects model?
>
>
>
>Thanks,
>
>Anirban
>
>
>_______________________________________
>Anirban Basu Ph.D.
>Section of General Internal Medicine
>Department of Medicine
>University of Chicago
>5841 S. Maryland Ave, MC-2007
>Chicago IL 60637
>
>Tel: +1 773 834 1796
>Fax: +1 773 834 2238
>
>
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