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Re: Re: st: Error w/ "inteff" command
From
Erasmo Giambona <[email protected]>
To
[email protected]
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
> <[email protected]> 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 <[email protected]>
>>> 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
>>> >
>>> >
>>> > *
>>> > * For searches and help try:
>>> > * http://www.stata.com/help.cgi?search
>>> > * http://www.stata.com/support/faqs/resources/statalist-faq/
>>> > * http://www.ats.ucla.edu/stat/stata/
>>>
>>> *
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>>> * http://www.ats.ucla.edu/stat/stata/
>>
>>
>> -------------------------------------------
>> Richard Williams, Notre Dame Dept of Sociology
>> OFFICE: (574)631-6668, (574)631-6463
>> HOME: (574)289-5227
>> EMAIL: [email protected]
>> WWW: http://www.nd.edu/~rwilliam
>>
>> *
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*
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