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


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
> --------------------------
>
>
>
>
>
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>


-- 

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

*
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*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/


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