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RE: st: ordinal dynamic panel data


From   "Erik Melander" <erik.melander@pcr.uu.se>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: ordinal dynamic panel data
Date   Mon, 16 Feb 2004 22:05:41 +0200

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Stas Kolenikov
> Sent: Tuesday, January 27, 2004 7:47 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: ordinal dynamic panel data
>
>
> Another potential problem I see in those results is that the cuts /
> thresholds give a very broad range of 5.4. It means that the underlying
> index should have at least that much of a range, or better a broader
> range. I would suspect that most of your data fall somewhere in the
> beginning of the scale, in the first two categories or so, and the highest
> categories of 4 and 5 are only observed in a handful of observations in
> the tail of the normal distribution, so those are not estimated very
> accurately. If that is the case, you might want to pool the upper
> categories into something like 4 and above.

I have now tested what happens when I pool categories 4 and 5, but very
similar results are obtained. I get absurd predictions based on the models I
estimate, often way off the actual range of the dependent variable (1 to 5).
I suppose this   is a consequence of the strangely broad range of the
cuts/thresholds. I have also tried ordered probit and ordered logit using
gllamm, but the results are again very similar. Is there anything that I can
do to arrive at models that produce more reasonable predictions?
>
> What exactly are you interested in in your model? What is the question
> that you need to answer with it? The estimated model will have some
> statistical problems from various sides, so you may need to think more
> about it before submitting it for a publication, but I personally would
> utilize those results to at least assess where I stand, especially as long
> as I don't see any better way to go with this model.
>

I am foremostly interested in demonstrating that one particular independent
variable significantly affects the level of pesonal integrity rights abuse
while controlling for the other determinants identified in previous
research. I also want to examine interaction effects involving my new
explanatory factor and one of the established predictors. In order to show
that my new variable also has substantially important impact I want to
compare the predicted levels of abuse for different combinations of
independent variables.

Thanks for your explanations and suggestions.

Erik Melander

> > 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
> >
> ------------------------------------------------------------------
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