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Re: Re: st: Error w/ "inteff" command


From   Erasmo Giambona <e.giambona@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: Re: st: Error w/ "inteff" command
Date   Wed, 2 Jan 2013 19:34:51 +0100

Thanks Richard. I will read the documents carefully. I just skimmed
through the power point presentation for now. I think I got why there
is no marginal effect for the interaction term (for e.g., the
interaction of two dummy variables). But I need to uderstand this part
better. I always thought the marginal effect exists from the inteff
article.

Erasmo

On Wed, Jan 2, 2013 at 6:22 PM, Richard Williams
<richardwilliams.ndu@gmail.com> wrote:
> I don't really understand how -inteff- works, nor do I have any great desire
> to find out. I am happy with the -margins- command, and the way you set it
> up is not correct for -margins-. When you compute the interaction term
> yourself, Stata has no way of knowing that the values of the interaction
> term are not independent of the values of the variables used to compute it.
> It should be
>
> webuse lbw2
> probit  low age lwt c.age#c.lwt
> margins, dydx(_all)
>
> For an explanation, see
>
> http://www.nd.edu/~rwilliam/stats/Margins01.pdf
>
> or else
>
> http://www.statajournal.com/article.html?article=st0260
>
>
> At 10:48 AM 1/2/2013, Erasmo Giambona wrote:
>>
>> Dear Kit,
>>
>> I was finally able to get the "inteff" command to work again. Inteff
>> and margins give me estimates on the interaction term that are
>> similar, but not the same. Is this simply do to different
>> approximation? Thanks. Please, see example below (using: webuse lbw2):
>>
>>
>> . g age_lwt=age*lwt
>>
>> . probit  low age lwt age_lwt
>>
>> Iteration 0:   log likelihood =   -117.336
>> Iteration 1:   log likelihood = -113.61015
>> Iteration 2:   log likelihood = -113.58509
>> Iteration 3:   log likelihood = -113.58509
>>
>> Probit regression                                 Number of obs   =
>> 189
>>                                                   LR chi2(3)      =
>> 7.50
>>                                                   Prob > chi2     =
>> 0.0575
>> Log likelihood = -113.58509                       Pseudo R2       =
>> 0.0320
>>
>>
>> ------------------------------------------------------------------------------
>>          low |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
>> Interval]
>>
>> -------------+----------------------------------------------------------------
>>          age |  -.0316919   .0896229    -0.35   0.724    -.2073495
>> .1439658
>>          lwt |  -.0087146   .0162868    -0.54   0.593     -.040636
>> .0232069
>>      age_lwt |   .0000561   .0006749     0.08   0.934    -.0012666
>> .0013788
>>        _cons |   1.186736   2.124989     0.56   0.577    -2.978165
>> 5.351637
>>
>> ------------------------------------------------------------------------------
>>
>> . inteff low age lwt age_lwt
>> Probit with two continuous variables interacted
>> (0 observations deleted)
>>
>>     Variable |       Obs        Mean    Std. Dev.       Min        Max
>> -------------+--------------------------------------------------------
>>   _probit_ie |       189    .0000473    7.14e-06   .0000265   .0000548
>>   _probit_se |       189    .0002247    .0000631   .0000304   .0002841
>>    _probit_z |       189    .2615582    .2009778   .1001468   1.322986
>>
>> . margins, dydx(_all)
>>
>> Average marginal effects                          Number of obs   =
>> 189
>> Model VCE    : OIM
>>
>> Expression   : Pr(low), predict()
>> dy/dx w.r.t. : age lwt age_lwt
>>
>>
>> ------------------------------------------------------------------------------
>>              |            Delta-method
>>              |      dy/dx   Std. Err.      z    P>|z|     [95% Conf.
>> Interval]
>>
>> -------------+----------------------------------------------------------------
>>          age |  -.0108404   .0306064    -0.35   0.723    -.0708278
>> .0491469
>>          lwt |  -.0029809   .0055547    -0.54   0.592    -.0138679
>> .0079061
>>      age_lwt |   .0000192   .0002308     0.08   0.934    -.0004332
>> .0004715
>>
>> ------------------------------------------------------------------------------
>>
>>
>>
>>
>>
>>
>>
>>
>> On Sat, Dec 29, 2012 at 11:16 PM, Christopher Baum <kit.baum@bc.edu>
>> wrote:
>> > <>
>> > Erasmo said
>> >
>> > Does - margins, dydx(_all) - also handle the interaction of two dummy
>> > variables?
>> >
>> > Yes. ht (hypertension, yes/no) and smoke (yes/no) are such, and
>> > interacted in the model below. Notice that each has a positive main effect
>> > on low bw, but if they appear together the effect is, strangely enough,
>> > reduced (although the negative interaction coefficient is not
>> > distinguishable from zero).
>> >
>> > probit low c.age##i.race i.ht##i.smoke
>> > margins, dydx(_all)
>> >
>> >
>> >
>> > Kit Baum   |   Boston College Economics & DIW Berlin   |
>> > http://ideas.repec.org/e/pba1.html
>> >                              An Introduction to Stata Programming  |
>> > http://www.stata-press.com/books/isp.html
>> >   An Introduction to Modern Econometrics Using Stata  |
>> > http://www.stata-press.com/books/imeus.html
>> >
>> >
>> > *
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>>
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>
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> HOME:   (574)289-5227
> EMAIL:  Richard.A.Williams.5@ND.Edu
> WWW:    http://www.nd.edu/~rwilliam
>
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