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Re: st: Stata journal paper on interaction terms in count models


From   Chris <chris.stata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Stata journal paper on interaction terms in count models
Date   Wed, 8 May 2013 11:29:30 +0200

Also, if you have a simple dif-in-dif design (where the dif-in-dif
estimator is not interacted with anything else), you can just compute
the marginal effect of the dif-in-dif estimator dummy. Basically, you
just switch on and off the treatment, and don't want to change group
assignment along with it. No need for inteff and the like.

See
http://www.sciencedirect.com/science/article/pii/S0165176511004769

On Wed, May 8, 2013 at 9:46 AM, Maarten Buis <maartenlbuis@gmail.com> wrote:
> On Wed, May 8, 2013 at 8:10 AM, James Bernard wrote:
>> Norton, Wang and Ai (2004) have discussed a Stat command to test for
>> interaction term in Logit models. This could be pretty handy in
>> difference-in-difference models where one needs to include an
>> interaction term involving treatment and time indicators.
>>
>> May I check with you if you know of similar commands for testing for
>> interaction effects in Poisson/negative binomial models? I would need
>> to build a difference-in-difference model in a Poisson model.
>
> There is no need for such a command as you can interpret the
> exponentiated coefficient in a poisson or negative binomial model as a
> ratio of ratios. Take the example below: In this case we expect that
> women with a "good" (managerial or professional) job but not in a
> union earn (1.35 - 1)*100% = 35% more than similar women with a "bad"
> job while adjusting for race, place of residence, education and
> experience. This effect of a good job is (1-.81)*100%= -19% smaller
> for union jobs. This is the interaction effect.
>
> *------------------ begin example ------------------
> sysuse nlsw88, clear
>
> gen byte black = race == 2 if race < 3
> label variable black "race"
> label define black 0 "white" ///
>                    1 "black"
> label value black black
>
> gen byte goodjob = occupation < 3 if occupation < .
> label variable goodjob `"respondent has a "good" job"'
> label define goodjob 1 "profesional or managerial" ///
>                      0 "other"
> label value goodjob goodjob
>
> glm wage i.goodjob##i.union i.black south grade ///
>     c.ttl_exp##c.ttl_exp,                       ///
>     link(log) family(poisson) vce(robust) eform
> *------------------- end example -------------------
> (For more on examples I sent to the Statalist see:
> http://www.maartenbuis.nl/example_faq )
>
> This way of interpreting your interaction term is simple enough and
> avoids a whole lot of problems associated with the Norton, Wang and Ai
> approach, and preventing problems is always better than trying to cure
> them afterwards. In essence the Norton, Wang, and Ai approach tries to
> get the marginal effect of an interaction effect, and most people who
> use marginal effects (not Norton, Wang and Ai though) want to report 1
> marginal effect per variable or interaction term and not as many
> effects as there are observations. This is understandable, as a model
> is supposed to simplify reality and N different effects for each
> variable is not much of a simplification. However, if you report just
> one marginal effect you are in essence estimating a linear model on
> top of your non-linear model. If that is really what you want then you
> should do this in one go and estimate a simple linear regression (and
> live with the consequences...).
>
> Hope this helps,
> Maarten
>
> ---------------------------------
> Maarten L. Buis
> WZB
> Reichpietschufer 50
> 10785 Berlin
> Germany
>
> http://www.maartenbuis.nl
> ---------------------------------
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