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Re: st: xtreg - continuous or discrete time


From   Ricardo Ovaldia <ovaldia@yahoo.com>
To   statalist@hsphsun2.harvard.edu
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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