Re: st: xttobit

[Matt Dobra <mdobra@gmu.edu>]

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
. }

Again, thanks for any help you can give me.

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,
> 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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