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Re: st: xtreg - continuous or discrete time
From
Ricardo Ovaldia <[email protected]>
To
[email protected]
Subject
Re: st: xtreg - continuous or discrete time
Date
Tue, 16 Aug 2011 11:45:42 -0700 (PDT)
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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