RE: st: ordinal dynamic panel data

["Guillaume Frechette" <gurst1@hotmail.com>]

Hi Mike.

"Erik Melander" wrote:

> But the log of the likelihood iterations (included below) looks funny to 
> me:
> what does it mean in this context that rho >=1?
>
>
> Fitting constant-only model:
>
> Iteration 0:   log likelihood = -1913.2714
> Iteration 1:   log likelihood = -1624.8395
> rho >= 1, set to rho = 0.99
> Iteration 2:   log likelihood = -1618.5555  (not concave)
> Iteration 3:   log likelihood = -1612.0323  (not concave)
> Iteration 4:   log likelihood = -1610.2913
> Iteration 5:   log likelihood = -1609.5952
> Iteration 6:   log likelihood = -1609.5949
> Iteration 7:   log likelihood = -1609.5949
>
> Fitting full model:
>
> Iteration 0:   log likelihood = -1416.2615  (not concave)
> Iteration 1:   log likelihood = -1378.9136  (not concave)
> Iteration 2:   log likelihood = -1362.3694
> Iteration 3:   log likelihood = -1352.2499
> Iteration 4:   log likelihood = -1351.2405
> Iteration 5:   log likelihood = -1351.2266
> Iteration 6:   log likelihood = -1351.2266
>
> Random Effects Ordered Probit                     Number of obs   =
> 1615
>                                                   LR chi2(12)     =
> 516.74
> Log likelihood = -1351.2266                       Prob > chi2     =
> 0.0000
>
> ----------------------------------------------------------------------------
> --
>        PolTS |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
> Interval]
> -------------+--------------------------------------------------------------
> --
> eq1          |
>     y t-1 =2 |   1.190776   .1362784     8.74   0.000      .923675
> 1.457877
>     y t-1 =3 |   1.984349   .1622633    12.23   0.000     1.666319
> 2.30238
>     y t-1 =4 |   2.873937    .190133    15.12   0.000     2.501283
> 3.246591
>     y t-1 =5 |   3.719126   .2362928    15.74   0.000     3.256001
> 4.182252
>     X1	 |  -.0177493   .0063743    -2.78   0.005    -.0302427   -.0052559
>     X2	 |  -.0329977   .0071047    -4.64   0.000    -.0469225   -.0190728
>     X3	 |   1.229424   .6062995     2.03   0.043     .0410985    2.417749
>     X4	 |  -.1695904   .0519423    -3.26   0.001    -.2713954   -.0677854
>     X5	 |    .194112   .0497613     3.90   0.000     .0965816    .2916424
>     X6	 |   .9392769   .1504445     6.24   0.000     .6444111    1.234143
>     X7	 |   .6116019   .1881763     3.25   0.001     .2427832    .9804206
> -------------+--------------------------------------------------------------
> --
> _cut1        |
>        _cons |   1.075184    .850905     1.26   0.206    -.5925591
> 2.742927
> -------------+--------------------------------------------------------------
> --
> _cut2        |
>        _cons |    3.07571    .856076     3.59   0.000     1.397832
> 4.753588
> -------------+--------------------------------------------------------------
> --
> _cut3        |
>        _cons |   4.857695    .864742     5.62   0.000     3.162832
> 6.552558
> -------------+--------------------------------------------------------------
> --
> _cut4        |
>        _cons |   6.432284   .8734574     7.36   0.000     4.720339
> 8.144229
> -------------+--------------------------------------------------------------
> --
> rho          |
>        _cons |   .2358363   .0489779     4.82   0.000     .1398414
> .3318312
> ----------------------------------------------------------------------------
> --
rho is not >= 1, it is 0.23..., Between iterations 1 and 2, reoprob received 
from Stata's optimizing routine a rho >= 1 and reset it at a value below 1, 
that's all. Your final rho is OK.

Note that if you make the very srtong assumption that your dependent 
variable does not depend on the random effect in period 1, then I believe 
you can include a lagged dep. var. in a random effects specification. This 
is known as the initial conditions problem. You can read about solutions to 
that problem in standard advanced econometric textbooks.

Good luck.

g

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