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Re: st: probit with interaction dummies (significance and marginal effects)


From   Andrea Bennett <mac.stata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: probit with interaction dummies (significance and marginal effects)
Date   Fri, 18 Jul 2008 11:00:03 +0200

Thank you so much!

May I sum up for clarification: When I am using e.g. a probit model with a dependent variable Y and include an interaction term - female*wage- and I am primarily interested in the interaction effect of a woman with wage then it is save to use the standard regression output to interpret the direction (AND the significance?) from the regression table. E.g. if the beta-estimators are -female- ==0.5, - wage- == 0.34 and -female*wage- == -0.03 and all being significant then I can say that the wage effect is significantly smaller when being a woman? Does this also hold when one is formulating models like -female*low_education-, -female*mid_education-, -female- high_education-? Or did I misinterpret you line "as long as you interpret the effects in terms of the effect on the latent variable you are ok in simply using the output from -probit-"?

When I want to know if (and for which range) the interaction of female and wage has a significant effect on Y I should use -inteff-. When I want to do the same for the interaction of female with the education levels, then there is not yet consensus on how it shall be done. Norton et al. 2004 mention -predictnl- but urge to use it with extra care. Another source would be Rich Williams webpage.

Did I completely mess it up (I fear so!) or is it like I described?

Andrea


On Jul 17, 2008, at 6:13 PM, Maarten buis wrote:


Regarding problem 1, this is just a matter of interpretation, as long
as you interpret the effects in terms of the effect on the latent
variable you are ok in simply using the output from -probit-, if you
want to interpret the results in terms of the probability you should
use -inteff-.

Problem 2 is much harder to solve. Any solution would in one way or
another try to controll for things that haven't been observed. It
should not come as a surprise that that is hard (read: impossible). So,
the fact that "the solution" hasn't been implemented yet in Stata is
not so much a problem with Stata but with the state of the statistical
science: we know the problem, but we just don't know the answer. Though
Rich Williams discusses one solution on his website.

-- Maarten

--- Andrea Bennett <mac.stata@gmail.com> wrote:

Thanks for the link! Still, I wonder if there's really no Stata
command I could use to "simply" test if the interaction is
significant and what influence (direction) it has on the dependent
variable. I'd be just rather surprised if this does not exist
because it seems to me this is a very common issue in any regression
design (interaction effects).
--- Maarten buis wrote:
There are two distinct issues when interpreting interaction effects
in a probit:

1) a significant positive (negative) interaction in terms of the
latent
variable does not mean a significant positive (negative)
interaction effect in terms of the probability that y = 1.

2) The scale of the latent variable is identified by setting the
residual variance at 1. If the residual variance differs between
the groups than that means that the scale of the latent variable
differs between the groups and when comparing differences in
effects across the groups you are basically comparing apples and
oranges.

-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands

visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434

+31 20 5986715

http://home.fsw.vu.nl/m.buis/
-----------------------------------------


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