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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Tue, 23 Mar 2010 01:08:48 -0700 (PDT) |

--- 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/

**Follow-Ups**:**Re: st: how to interpret interaction effects in negative binomial model***From:*"WANG Shiheng" <acwang@ust.hk>

**References**:**st: how to interpret interaction effects in negative binomial model***From:*"WANG Shiheng" <acwang@ust.hk>

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