RE: st: comparing coefficients

[John Stymans <johnstymans@hotmail.be>]

Dear Richard,

many thanks for your comments. 

You are right, I use the oglm command with store, not ologit.

Best,

John
----------------------------------------
> Date: Wed, 9 Jan 2013 20:02:11 -0500
> To: statalist@hsphsun2.harvard.edu; statalist@hsphsun2.harvard.edu
> From: richardwilliams.ndu@gmail.com
> Subject: Re: st: comparing coefficients
>
> At 07:07 AM 1/9/2013, David Hoaglin wrote:
> >John,
> >
> >What's wrong with including both genders in a single model and
> >expanding the list of predictors to include gender and the interaction
> >of gender with each of the current predictors?
>
> Actually, that has some problems of its own. Suppose you ran
>
> use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";, clear
> reg warm i.yr89 i.white c.age c.ed if male==0
> reg warm i.yr89 i.white c.age c.ed if male==1
> reg warm i.male##(i.yr89 i.white c.age c.ed)
>
> It isn't hard to see that the last model with interactions gives you
> the exact same results as the 2 groups run separately. Things are
> just parameterized differently.
>
> But if instead you run
>
> ologit warm i.yr89 i.white c.age c.ed if male==0, nolog
> ologit warm i.yr89 i.white c.age c.ed if male==1, nolog
> ologit warm i.male##(i.yr89 i.white c.age c.ed), nolog
>
> You see the final model does not give exactly the same results as the
> first two models.
>
> Why not? In the final ologit model, you are allowing all the
> variables to have different effects in each group. BUT, you are
> constraining the cut points to be the same for each group, whereas
> when you run the model separately for the two groups the cutpoints
> can differ. That may not be a bad assumption to make, but nonetheless
> it is an assumption.
>
> Probably more important problems are outlined at
> http://www3.nd.edu/~rwilliam/xsoc73994/L31.pdf. The key issue is
> that, unless the two groups have the same residual variability, tests
> of equality across groups are distorted.
>
> The more I work with ordinal models, the more I think you should do
> everything you can to get continuous dependent variables. There are a
> lot of things you can do in OLS regression that don't generalize as
> you would expect to logit and ologit models.
>
> One other point: as far as I know this command from the original post
> is not legitimate, because there is no store option in ologit:
>
> ologit y1 educationmedium educationhigh age income class household
> country if gender=0, or store(step1a)
>
>
> >David Hoaglin
> >
> >On Wed, Jan 9, 2013 at 4:30 AM, John Stymans <johnstymans@hotmail.be> wrote:
> > > Dear Statalist Users,
> > > I have a problem when comparing coefficients across groups.
> > > I am using Stata 12.
> > > I first run an ordered logit model for two groups (males and females).
> > >
> > > ologit y1 i.education age income class household country if
> > gender=0, or store(step1a)
> > > ologit y1 i.education age income class household country if
> > gender=1, or store(step1b)
> > > goal is to compare across gender the effect of education which is
> > a categorical variable with 3 values, hence the use of i..
> > >
> > > when I use suest:
> > >
> > > suest step1a step1b
> > >
> > > test [step1a _y1]i.education=[step1b_y1]i.education
> > >
> > > the command does not work. I am however able to split education
> > in three values an add two of them to the regression (omitting educationlow);
> > >
> > > ologit y1 educationmedium educationhigh age income class
> > household country if gender=0, or store(step1a)
> > > ologit y1 educationmedium educationhigh age income class
> > household country if gender=1, or store(step1b)
> > > and than run suest:
> > >
> > > suest step1a step1b
> > >
> > > test [step1a _y1]loweducation=[step1b_y1]loweducation
> > >
> > > Yet I am unable to compare the difference in significance of
> > education across gender in general. Does anyone know how to solve this issue?
> > >
> > > Many thanks in advance and best regards,
> > >
> > > John
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>
> -------------------------------------------
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