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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 17:49:05 -0700 (PDT)
Thank you Jose Maria. I did understand why the coefficients are different, What I do know is which is the most appropriate parametrization of time. Or how to decide.
Ricardo
Ricardo Ovaldia, MS
Statistician
Oklahoma City, OK
--- On Tue, 8/16/11, José Maria Pacheco de Souza <[email protected]> wrote:
> From: José Maria Pacheco de Souza <[email protected]>
> Subject: Re: st: xtreg - continuous or discrete time
> To: [email protected]
> Date: Tuesday, August 16, 2011, 4:10 PM
> 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
> >
> >
> > *
> > * 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/
> >
> >
> >
> 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
> *
> * 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/
>
*
* 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/