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From |
"WANG Shiheng" <acwang@ust.hk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: how to interpret interaction effects in negative binomial model |

Date |
Wed, 24 Mar 2010 13:47:31 +0800 (HKT) |

Thank you very much! This is helpful. Regards, Shiheng > --- On Tue, 23/3/10, WANG Shiheng wrote: >> I have a question about how to interpret the interaction >> items in negative binomial regression. >> >> In the following model “post” is a dummy variable (0 or >> 1) to indicate two different periods (0 represents the >> first period, 1 represents the second period). >> “treatment” is a dummy variable (0 or 1) to indicate two >> different groups –“treatment sample”(1) vs. “control >> sample” (0). The interaction is the product of the two >> dummies. The dependent variable is the number of analysts. > <snip> >> coef se >> post .0610886 .0743914 >> treatmen -2.975135 .1591135 >> post*treatment .214007 .0730457 > > I would analyse these results in terms of incidence rate > ratios, by adding the -irr- option. You can do it also by > hand, by computing irr = exp(coef) (but why do it yourself > if Stata can do it for you?). The basic logic behind this > type of interpretation of interaction terms in non-linear > models is discussed here: > http://www.maartenbuis.nl/wp/interactions.html > > To come back to your case: > > The expected number of analysist in the non-treatment group > increases by a factor exp(.061)= 1.06 (i.e. 6%) when a firm > went from the pre-period to the post-period. This ratio is > however not significant. [1] > > This effect of post increases by a factor of exp(.214) = > 1.24 (i.e. 24%) if the firm is in the treatment group. This > change in effect is significant. [1] > > The expected number of analysists in the pre-period group > changes by a factor of exp(-2.975) = .05 (i.e. a change of > -95%) when a firm receives the treatment. This effect is > significant. [1] > > This effect of treatment changes by a factor of exp(.214) = > 1.24 (i.e. the effect becomes 24% less negative) in the > post-period. This effect is significant. [1] > > Hope this helps, > Maarten > > [1] It may come as a surprise that I use the test that > coef = 0 to test the hypothesis that exp(coef) = 1. The > logic behind this choice is discussed here: > http://www.stata.com/support/faqs/stat/2deltameth.html > > -------------------------- > 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/ > -- Shiheng Wang Assistant Professor Department of Accounting School of Business and Management Hong Kong University of Science and Technology Tel: 852 2358 7570 Fax: 852 2358 1693 Email: acwang@ust.hk * * 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/

**References**:**Re: st: how to interpret interaction effects in negative binomial model***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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