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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