Greetings,
At the risk of posing a stupid question, can somebody comment on when it
makes sense.to use gllapred or gllasim following the estimation of a
model using gllamm?
Specifically, I'd like to do the following. 1. Estimate a panel probit
model using:
gllamm y age other1 other2, i(persid) family(binom) link(probit) nip(20)
2. Holding other1 and other2 fixed at their mean values, generate
predicted values of y over different integer values of age, say, ranging
between 18 and 65.
To implement step 2, I've created an artificial data set in which other1
and other2 are fixed at their mean values and age varies over the range
of interest. So I first estimate the model on the real data. Then I open
the artificial data and type:
gllapred pred1, linpred fsample
The fsample option is apparently needed because otherwise gllapred isn't
pleased that I've opened another data set (I get an insufficient
observations error).
The above seems like the right approach given my objective. But I get a
very different answer when I type in step 2:
gllasim pred1, linpred fsample
evidently because the latter (gllasim) doesn't include empirical Bayes.
Is there a practical rule for deciding between the two?
Many thanks,
Colin
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