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Re: st: xtreg - continuous or discrete time
From
José Maria Pacheco de Souza <[email protected]>
To
[email protected]
Subject
Re: st: xtreg - continuous or discrete time
Date
Tue, 16 Aug 2011 18:10:09 -0300
Em 16/08/2011 15:45, Ricardo Ovaldia escreveu:
I have a longitudinal data on children measured at ages 5, 10, 15 and 20.
They were all measured within two weeks of their birthday.
When using -xtreg-, I get very different results depending of whether I use time as a continuous or categorical variable.
I am tempted to use time as continuous, but I am not sure which to use. Any suggestions will be appreciated.
Below is my output from the two models. I am interested in the group differences:
Than you,
Ricardo
Ricardo Ovaldia, MS
Statistician
Oklahoma City, OK
xtreg instad group##time ses
Random-effects GLS regression Number of obs = 1413
Group variable: id Number of groups = 360
R-sq: within = 0.1989 Obs per group: min = 1
between = 0.0435 avg = 3.9
overall = 0.1426 max = 4
Wald chi2(12) = 275.48
corr(u_i, X) = 0 (assumed) Prob> chi2 = 0.0000
------------------------------------------------------------------------------
instad | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
group |
2 | -.3593535 .8898889 -0.40 0.686 -2.103504 1.384797
3 | -1.664428 .8971943 -1.86 0.064 -3.422897 .0940402
|
time |
10 | 5.120189 .786916 6.51 0.000 3.577862 6.662516
15 | 6.054063 .7869046 7.69 0.000 4.511758 7.596368
20 | .6104585 .7870224 0.78 0.438 -.932077 2.152994
|
group#time |
2 10 | -1.245678 1.122178 -1.11 0.267 -3.445106 .9537501
2 15 | -1.581695 1.126637 -1.40 0.160 -3.789864 .6264734
2 20 | -2.830481 1.12774 -2.51 0.012 -5.04081 -.6201511
3 10 | -.3909519 1.135047 -0.34 0.731 -2.615604 1.8337
3 15 | -.7709906 1.134923 -0.68 0.497 -2.995398 1.453417
3 20 | -.5713752 1.135312 -0.50 0.615 -2.796547 1.653796
|
ses | -.0209192 .0203155 -1.03 0.303 -.0607368 .0188984
_cons | 104.1393 1.187133 87.72 0.000 101.8125 106.466
-------------+----------------------------------------------------------------
sigma_u | 3.1002125
sigma_e | 6.1590537
rho | .20215091 (fraction of variance due to u_i)
------------------------------------------------------------------------------
. xtreg instad group##c.time ses
Random-effects GLS regression Number of obs = 1413
Group variable: id Number of groups = 360
R-sq: within = 0.0049 Obs per group: min = 1
between = 0.0414 avg = 3.9
overall = 0.0193 max = 4
Wald chi2(6) = 21.62
corr(u_i, X) = 0 (assumed) Prob> chi2 = 0.0014
------------------------------------------------------------------------------
instad | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
group |
2 | .4061883 1.137796 0.36 0.721 -1.823851 2.636228
3 | -1.590677 1.146674 -1.39 0.165 -3.838116 .656763
|
time | .0580776 .0553659 1.05 0.294 -.0504374 .1665927
|
group#c.time |
2 | -.1741696 .079296 -2.20 0.028 -.329587 -.0187523
3 | -.0427001 .079865 -0.53 0.593 -.1992325 .1138324
|
ses | -.0261362 .0206384 -1.27 0.205 -.0665867 .0143142
_cons | 106.608 1.288649 82.73 0.000 104.0823 109.1337
-------------+----------------------------------------------------------------
sigma_u | 2.6938033
sigma_e | 6.8485734
rho | .1339852 (fraction of variance due to u_i)
------------------------------------------------------------------------------
Ricardo Ovaldia, MS
Statistician
Oklahoma City, OK
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Dear Ricardo:
probably some other Statalister will explain better than I, but I hope I
can give some initial explanation.
When you use the first model, time is categorical and the meanings of
the coeficients are differences in means of the "category" 10 against
the "category" 5, of the "category" 15 against "category" 5 etc. and
does not must use the intervals 5, 5, 5 and 5 between the categories,
because the variable is not numeric.
For the second model, the variable is continuous and the coeficient says
that there is an increase of .05 in instad for each unity of time, that
maybe 0 1 2 3 4 5 6 7 8 9 ......20.
The values are not exatly what I mentioned because you use interaction
which interferes in the linear estimation, and the data presents a
possible squared form.
FRegards,
josé maria
--
Jose Maria Pacheco de Souza
Professor Titular (aposentado), Colaborador Senior
Departamento de Epidemiologia/Faculdade de Saude Publica, USP
Av. Dr. Arnaldo, 715
01246-904 - S. Paulo/SP - Brasil
fones (11)3061-7747; (11)3768-8612
www.fsp.usp.br/~jmpsouza
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* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/