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st: Predictions based on reoprob and gllamm


From   "Erik Melander" <erik.melander@pcr.uu.se>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Predictions based on reoprob and gllamm
Date   Fri, 27 Feb 2004 09:52:04 +0200

I have a panel dataset with an ordinal dependent variable, judgmentally
coded from 0 to 4. There is considerable inertia in the dependent variable
and I thus want to include a lagged dependent variable (actually a set of 4
dummies since it is ordinal scale) to control for autocorrelation. I have
tried to run random effects ordinal probit and logit for panel data, using
for example the stata commands below:


reoprob dependentvar dependentvar1t-1 dependentvar2t-1 dependentvar3t-1
dependentvar4t-1 indepvarA indepvarB indepvarC indepvarD, i (panelunit)

gllamm dependentvar dependentvar1t-1 dependentvar2t-1 dependentvar3t-1
dependentvar4t-1 indepvarA indepvarB indepvarC indepvarD, link(oprobit)
i(panelunit)

gllamm dependentvar dependentvar1t-1 dependentvar2t-1 dependentvar3t-1
dependentvar4t-1 indepvarA indepvarB indepvarC indepvarD, link(ologit)
i(panelunit)

In the output, one thing that seems a little weird is that the
cuts/thresholds give a broader range than the dependent variable itself. The
range of the coefficients of the categories of the lagged variable is only
about half the range of the cuts/thresholds.

Most disturbing of all is that the resulting models give rise to predictions
that are outside the range of the dependent variable. Why is this so, and is
there anything I can do in order to arrive at models with more reasonable
predictions?

Thanks for your attention.

Erik Melander

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