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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   Tue, 8 Jan 2013 18:36:55 +0100

Dear Richard,

I have read your Stata's article carefully and I think I understand
better why marginal effects on interaction terms (e.g., interactio of
two dummy variables) do not exist in the case of non-linear models.
But what about Linear Probability Models? Can I simply use the slope
coefficient on the interaction term in this case to measure the
marginal effect? I would appreciate you answer on this issue.

Best regards,

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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>>
>>
>> -------------------------------------------
>> 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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