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Re: st: oglm and heterogeneous choice models


From   Richard Williams <richardwilliams.ndu@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: oglm and heterogeneous choice models
Date   Mon, 24 Oct 2011 18:19:43 -0500

At 01:12 PM 10/24/2011, Rourke O'Brien wrote:
I have a follow up question on heterogeneous choice models.

I am interested in testing which variables significantly predict
residual variance. I've tried many configurations of the "sw, pe(.05):
oglm y x1 x2 x3 x4, eq2(x1 x2 x3 x4) flip" but have not been able to
achieve convergence. Yet, when I use the gologit2 command and the
autofit option, the model runs smoothly and I am told which covariates
do not meet criteria for parallel line assumptions. I can then run the
model using gologit2 and specify which predictors meet the parallel
line assumptions and which do not. Is this an appropriate strategy? I
am ultimately interested in testing for an interaction in a logistic
model.

Is the dv dichotomous? These models can be difficult to estimate as is, and are even tougher with a dichotomous dv.

The oglm help recommends using the -lr- option of -sw-. The default -wald- option gets confused when the same variable is in both the variance and choice equations, i.e. it tests the variable in both equations when you only want it tested in the variance equation.

Either a brant test or gologit2 can identify variables that are problematic. You can try including those variables in the variance equation of a hetero model -- it may or may not work well.

Even though I programmed oglm to support sw, I am not crazy about its use. In my Stata Journal article (Stata Journal 10(4):540-567) I suggested you think of this as being like a diagnostic test. If the assumption of homogeneous errors seems to be violated, think about other ways to solve the problem, e.g. add a variable, add a squared term. I make the same advice for OLS models where hetero seems to be a problem -- see if there is some reasonable way to make the hetero go away by tweaking your model.


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Richard Williams, Notre Dame Dept of Sociology
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