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# st: Re: st: test the coefficients in multinominal logit model‏

 From Maarten buis To statalist@hsphsun2.harvard.edu Subject st: Re: st: test the coefficients in multinominal logit model‏ Date Wed, 24 Feb 2010 17:50:12 -0800 (PST)

```--- On Thu, 25/2/10, atsu june wrote:
> I would like to run multinomial logit model for two
> sub-populations which are distinct and test whether a1 for
> X1 in group A differs b1 for X1 in group B.
>
> for example,  I would like to run the following
> model.
>
> mlogit educ age inc if men==1
> mlogit educ age inc if men==0
>
> then test whether the coefficient for age in group of men
> differs that in group of women.

Others have sugested -suest-. An alternative is to use
interaction terms. However, both -susest- and interaction
terms used in this way test one specific way of thinking
about interaction terms in non-linear models: a ratio
change in effects as measured in relative risk ratios. So
in the example below the baseline rrr is .24 meaning that
we expect to find .24 domestic cars of average price in
repair status 4 for every domestic car of average price in
repair status 3. This ratio will decrease 5% for every
1000\$ increase in price. This is the effect of price for
domestic cars. The effect of price for foreign cars is
1.79 times larger, i.e. 1.79*.95= 1.70, which means that
the ratio of foreign cars in repair status 4 per foreign
car in repair status 3 increases by 70% for every 1000\$
increase in price. The test statistic next to the
interaction term will give you the test of the null
hypothesis that this ratio of effects is 1.

*-------------- begin example ------------------------
sysuse auto, clear
recode rep78 1/2=3
gen byte baseline = 1
sum price , meanonly
gen c_price = (price - r(mean))/1000
mlogit rep78 i.foreign##c.c_price baseline, rrr nocons
*---------------- end example ------------------------
( For more on how to use examples I sent to statalist see:
http://www.maartenbuis.nl/stata/exampleFAQ.html )

Some disciplines like to think of effects in terms of
marginal effects, in that case testing for interactions
becomes a lot harder. Actually, the two test slightly
different hypotheses, see:
http://www.maartenbuis.nl/wp/interactions.html

Hope this helps,
Maarten

--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://www.maartenbuis.nl
--------------------------

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