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From |
Matt Dobra <mdobra@gmu.edu> |

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

. }

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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**Follow-Ups**:**Re: st: xttobit***From:*Sophia Rabe-Hesketh <sophiarh@berkeley.edu>

**st: Class system failure***From:*Fred Wolfe <fwolfe@arthritis-research.org>

**References**:**st: 1st Australian and New Zealand Stata Users Group meeting***From:*Philip Ryan <philip.ryan@adelaide.edu.au>

**st: xttobit***From:*Matthew L Dobra <mdobra@gmu.edu>

**Re: st: xttobit***From:*Sophia Rabe-Hesketh <sophiarh@berkeley.edu>

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