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Re: st: problem with marginal effect after running a logit regression


From   "Justina Fischer" <JAVFischer@gmx.de>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: problem with marginal effect after running a logit regression
Date   Mon, 30 Jul 2012 02:49:29 +0200

Hi Jeremy,

please do not forget to thank Rieza in your acknowledgement as  she resolved half of your problems of your master thesis.

Actually, I do think that with reading of some basic econometrics books + reading the xtensive Stata handbook on margins you should have been able to answer most of these questions all by yourself.

Justina
-------- Original-Nachricht --------
> Datum: Sun, 29 Jul 2012 18:15:44 -0500
> Von: Rieza Soelaeman <rsoelaeman@gmail.com>
> An: statalist@hsphsun2.harvard.edu
> Betreff: Re: st: problem with marginal effect after running a logit regression

> Hi Jeremy,
> Your advisor is correct that the coefficients of a logistic regression
> cannot be interpreted in the same way as OLS.  Using the margins
> command allows for an estimation of the marginal effect (e.g. the
> increase in probability of your outcome = 1, here I assumed outcome is
> binary). One question for you: when your advisor meant by "at median,"
> did he mean at median values for all the characteristics in your
> model, or just the median level of education?
> 
> If the specific effect of interest is going from mstudymid to
> mstudyhigh, I would suggest making mstudymid the reference category in
> your set of dummy variables for education.  Here I assume you have
> mstudylow as the reference (excluded) category.  If you make mstudymid
> your reference, then the marginal effect of mstudyhigh would be the
> marginal effect of going from mstudymid to mstudyhigh.  Similarly, the
> marginal effect of mstudylow would be the marginal effect of going
> from mstudylow to mstudymid.
> 
> Typically, if your predictors are continuous, it makes sense to have
> Stata calculate marginal effects at the means of each value of your
> predictors. This can be achieved by executing the following command
> after running your regression:
> 
> margins, atmeans
> 
> However, because your predictors are categorical (or if you are using
> a version of Stata before Stata 12), you may be able to get away with
> specifying criteria for the "typical" individual in your dataset for
> which you are calculating the marginal effect.  Then justify the
> choices you made in describing the "typical" individual.
> 
> For example, in your dataset, the "typical" individual may be a 35
> year old, male, who is a chief wage earner, with high education,
> mintpol = "mid", mpol = "right", and mincome = "high," then the
> command you would run would be something like:
> 
> mfx, at (mstudymid=0 mstudyhigh=1 mhomme=1 mchiefwageearner=1 mage28_37=1
> mage38_47=0 mage48_57=0 .............. mincomehigh=1)
> 
> *Note the ........... means you should assign a 0 or 1 value for your
> categorical predictors as appropriate to describe your person.
> 
> I see there are several variables in your dataset that could benefit
> from being continuous, though.  If age were continuous, you can simply
> plug in the average age (from any of the univariate commands you can
> use to describe the mean of a vbl).  Same thing with income.  I think
> it would make your regression more robust to use the continuous.
> 
> Of course using this method (with -mfx-) is complicated by the
> clustering in your data and the interactions between the cluster
> variables S003 and S002 (it appears to me these are polychotomous
> categorical variables, as you have used the i. in adding them to your
> regression).  Because I don't know what they represent and how many
> levels of each they are, I am not sure how they would be specified in
> the -mfx- command.  Do you absolutely need to know the marginal effect
> of each of those clusters, or were they included just so you can
> control for them?  If you included them just to control for them,
> consider using -xtmelogit- (mixed effects logit) instead, and specify
> S003 and S002 for random intercept calculation.
> 
> HTH,
> Rieza
> 
> *I invite other statalisters to correct me if I have said something in
> error
> above.
> 
> On Thu, Jul 26, 2012 at 2:17 PM, Jeremy Franklin <jfrankli@ulb.ac.be>
> wrote:
> > Dear all,
> >
> > Here is my little trouble:
> >
> > For my master degree thesis I decided to test for the role of education
> level in assession the importance of fighting inflation.
> >
> > Here is my final regression formula:
> >
> > xi: logit mfirstchoice  mstudymid mstudyhigh mhomme mchiefwageearner
> mage28_37 mage38_47 mage48_57 mage58 mintpollow mintpolmid mintpolhigher
> mpolleft mpolright  mincomemid mincomehigh i.s003 i.s002 i.s003*i.s002,
> vce(cluster s003)
> >
> > I hate the results but my thesis coordinator told me that the results of
> logit regression cannot be interpreted like coefficients of a linear
> regression. Therefore, he suggested me to check for the marginal effects at the
> median in order to see the marginal effects of one individual coming from
> mstudymid to mstudyhigh
> >
> > I googled everything, i tried hundreds of formulas, both with mfx and
> margins but i still cannot find the correct one in order to interpret my
> results.
> >
> > Can ANYONE help me please.
> >
> > ps: a robustness test included in my thesis include the following
> formula (this time with ologit)-
> >
> > xi: ologit minflation  mstudymid mstudyhigh mhomme mchiefwageearner
> mage28_37 mage38_47 mage48_57 mage58 mintpollow mintpolmid mintpolhigher
> mpolleft mpolright x047 i.s003 i.s002 i.s003*i.s002, vce(cluster s003)
> >
> > *
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> > *   http://www.ats.ucla.edu/stat/stata/
> 
> *
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-- 
Justina AV Fischer, PhD
COFIT Fellow
World Trade Institute
University of Bern

homepage: http://www.justinaavfischer.de/
e-mail: javfischer@gmx.de. justina.fischer@wti.org
papers: http://ideas.repec.org/e/pfi55.html


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