> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]]On Behalf Of Stas Kolenikov
> Sent: Tuesday, January 27, 2004 7:47 PM
> To: [email protected]
> 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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