Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

# Re: st: Fwd: Comparing marginal effects of two subsamples

 From Jianhong Chen To statalist@hsphsun2.harvard.edu Subject Re: st: Fwd: Comparing marginal effects of two subsamples Date Mon, 24 Oct 2011 11:33:58 -0400

```Hi, Maarten

Thank you very much for your response. They are very helpful. I really
appreciate it. I have a following question about reporting.

First, I used nbreg, dv iv moderator iv*moderator, the coefficient of
interaction terms in results section is 0.81. Then, I used nbreg, dv
iv moderator iv* moderator, irr; the IRR of interaction terms in the
results section is 1.20. Both is significant (p=0.023).

So when I report the results in the table, I will use IRR, not coef,
(1.20, not 0.81), right? Do you have a published paper who also used
the same approach for my reference?

Thanks again

Best

On Mon, Oct 24, 2011 at 4:12 AM, Maarten Buis <maartenlbuis@gmail.com> wrote:
> On Fri, Oct 21, 2011 at 9:23 PM, Jianhong Chen wrote:
>> Thank you very much for your response. I read your article on Stata
>> Journal and I think you are completely right.  Just to make sure that
>> I understand right, are the exponentiated coefficients you mentioned
>> the same coefficients which I can get after runing negative binomial
>> model?
>
> The magic option is -irr-, just as I did in the example I posted earlier.
>
>> Also, since the reviewers require that we should compare marginal
>> effect, we have no chance to avoid that and we plan to use the
>> marginal effects as supplemental analysis, not main analysis. Do you
>> have any idea to do t-test of marginal effects of two subsample?
>
> Typically, my response to the reviewer would be something like:
> "We agree with the reviewer that a substantive interpretation of the
> parameters would improve the article. We have chosen to do so in the
> form of rate ratios rather than marginal effects as that way we avoid
> the problem that correct marginal effects of interaction terms tend to
> be so variable across individuals that a one number summary, like a
> mean marginal effect, is hard to justify (Ai and Norton 2003)."
>
> Hope this helps,
> Maarten
>
> Ai, C., and E. Norton. 2003. Interaction terms in logit and probit
> models. Economics Letters 80: 123–129
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
>
> http://www.maartenbuis.nl
> --------------------------
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/
```

© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   Site index