# RE: st: Negative Binomial Fixed Effects estimator

 From [email protected] To [email protected] Subject RE: st: Negative Binomial Fixed Effects estimator Date Thu, 3 Mar 2005 10:07:23 -0600

```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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```