Re: st: oglm and heterogeneous choice models

[Cameron McIntosh <cnm100@hotmail.com>]

Rourke,

I might add that if you're going to be looking at interactions in this type of model, you should see:

Mallick, D. (January 2009). Marginal and Interaction Effects in Ordered Response Models. MPRA Paper No. 13325.http://mpra.ub.uni-muenchen.de/13325/1/MPRA_paper_13325.pdf

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