# Re: st: xttobit

 From Matt Dobra To statalist@hsphsun2.harvard.edu Subject Re: st: xttobit Date Fri, 27 Feb 2004 17:41:05 -0500

Sophia,

In general, with the default 12 quadratures, most of my estimated betas have a relative difference of between 1-4%, and a few are estimated with a very high relative difference, sometimes over 50%. I presume that I am running into this problem because some of my independent variables are time-invariant within groups. .

Thanks for offering to help me with this. There are actually 30 regressions I want to run...10 different dependent variables, each with three different sets of independent variables, captured in a macro called `vars'. Any guidance you could give me in how to generally set up a random effects tobit using gllamm would be greatly appreciated.

The general form of all of the random effects tobits I want to run is:
xttobit zteq `vars', ll(0) ul(100) i(sys_id)

Each of the dependent variables has a name starting with the letter "z" (e.g. zteq, zdeq, zieq), so for simplicity, I could do the data manipulation within a loop like:

. foreach var of varlist z* {
. stuff
. }

Matt

Sophia Rabe-Hesketh wrote:

```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,
gllamm (see http://www.gllamm.org). In situations where
to be more reliable, see e.g.

Rabe-Hesketh, S., Skrondal, A. and Pickles, A. (2002).
Reliable estimation of generalised linear mixed models

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
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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```*
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```
```
*
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```