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In your case, I will check quadrature with -quadchk- and also I will
estimate the model using -tobit-. My guess is your results from RE with
full set of variables should be similar to the one obtained by a simple
model. 

Rodrigo.


 

-----Mensaje original-----
De: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de Michael E
Roettger
Enviado el: Viernes, 10 de Octubre de 2008 09:58 a.m.
Para: statalist@hsphsun2.harvard.edu
Asunto: st: Problem interpreting results from xttobit 


Dear STATA list,

I am trying to determine if my (i) regression results are valid using
xttobit and (ii) how to interpret my results due to a major shift in
likelihood functions.

I have been using a random effects tobit model to a measure
left-censored variable [a propensity to commit delinquent acts among a
panel of respondents].  My initial model provides negative log-likehood
with an individual-level random effect that seems reasonable:


>Random-effects tobit regression                 Number of obs      =
2401
>Group variable: aid                             Number of groups   =
804
>Random effects u_i ~ Gaussian                   Obs per group: min =
2
>                                                               avg =
3.0
>                                                               max =
3
>                                                Wald chi2(3)       =
91.53
>Log likelihood  = -4211.3782                    Prob > chi2        =
0.0000
.
.
.
>   /sigma_u |   2.079586   .2733142     7.61   0.000       1.5439
2.615272
>   /sigma_e |   5.835778   .1588994    36.73   0.000     5.524341
6.147215
>-------------+---------------------------------------------------------
>-------------+-------
>         rho |   .1126776   .0284796                      .0662654
.1786233
>-----------------------------------------------------------------------
>-------


However, after adding several variables, the log-likelihood becomes
highly positive and the individual-level random effect that is of the
order of E-39


>Random-effects tobit regression                 Number of obs      =
2294
>Group variable: aid                             Number of groups   =
768
>
>Random effects u_i ~ Gaussian                   Obs per group: min =
2
>                                                               avg =
3.0
>                                                               max =
3
>
>                                                Wald chi2(9)       =
300.30
>Log likelihood  =  52807.689                    Prob > chi2        =
0.0000
.
.
.
>   /sigma_u |   4.17e-39   1.06e-40    39.19   0.000     3.96e-39
4.38e-39
>    /sigma_e |   5.793114    .140685    41.18   0.000     5.517377
6.068852
>-------------+---------------------------------------------------------
>-------------+-------
>         rho |   5.19e-79   3.65e-80                      4.52e-79
5.95e-79
>-----------------------------------------------------------------------
>-------


Both models converge to a concave solution and I am estimating about 35
quadrature points for the
Gauss-Hermite function.   Other than the random effects, the estimated
coefficients for Beta's are
consistent with theory/prior research. Its just the change in likelihood
and a highly significant random effect near zero from the first to the
second models above that leave me scratching my head.

Thanks for your time and help!

Mike Roettger

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