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Re: st: Negative Binomial Fixed Effects estimator

From   Paul Millar <[email protected]>
To   [email protected]
Subject   Re: st: Negative Binomial Fixed Effects estimator
Date   Wed, 02 Mar 2005 22:49:38 -0700

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
University of Calgary

At 01:32 PM 02/03/2005, you wrote:


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]

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?



Anirban Basu Ph.D.
Section of General Internal Medicine
Department of Medicine
University of Chicago
5841 S. Maryland Ave, MC-2007
Chicago IL 60637

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