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]

From |
Jianhong Chen <jianhongchen1985@gmail.com> |

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/

**Follow-Ups**:**Re: st: Fwd: Comparing marginal effects of two subsamples***From:*Maarten Buis <maartenlbuis@gmail.com>

**References**:**st: Fwd: Comparing marginal effects of two subsamples***From:*Jianhong Chen <jianhongchen1985@gmail.com>

**Re: st: Fwd: Comparing marginal effects of two subsamples***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: Fwd: Comparing marginal effects of two subsamples***From:*Jianhong Chen <jianhongchen1985@gmail.com>

**Re: st: Fwd: Comparing marginal effects of two subsamples***From:*Maarten Buis <maartenlbuis@gmail.com>

- Prev by Date:
**st: using encode to order string distances** - Next by Date:
**st: FE over two columns** - Previous by thread:
**Re: st: Fwd: Comparing marginal effects of two subsamples** - Next by thread:
**Re: st: Fwd: Comparing marginal effects of two subsamples** - Index(es):