Matt,
If the estimates seem robust with 30 quadrature points,
why not use 30?
However, if you are not sure that the estimates are robust,
you could use adaptive quadrature which is implemented in
gllamm (see http://www.gllamm.org). In situations where
ordinary quadrature performs poorly, adaptive quadrature tends
to be more reliable, see e.g.
Rabe-Hesketh, S., Skrondal, A. and Pickles, A. (2002).
Reliable estimation of generalised linear mixed models
using adaptive quadrature. The Stata Journal 2, 1-21.
However, estimating random effects tobit models in gllamm
is a bit involved. You have to treat the data as if you
had mixed responses and specify a linear model for the
non-censored responses and a scaled probit model for the
censored responses (with the same residual variance).
If you send me the xttobit command that you used,
I can send you the corresponding gllamm command
(and data manipulation steps).
Sophia
Matthew L Dobra wrote:
Statalisters,
Any advice you can give me would be appreciated. I have some data that
seems appropriate for xttobit analysis. My LHS variables are bounded
between 0 and 100 (percentages), and an unbalanced panel of approximately
300 i's and 4 t's. I used the quadchk command and found that my
estimates were quite sensitive to my choice of quadratures. I played
around with the quadrature option on xttobit, and found that only when I
increased the number of quadratures to about 30 did quadchk show that the
estimates were robust. So, I'm resigned to think that Stata's xttobit
command may not be the best option.
Moving on, I'm curious as to what I might do from here. Are there other
commands that might help me out? Has somebody written a version of
xttobit that uses a different method of approximation?
Matt
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