Re: st: Count data model with underdispersion

[F Tollnek <f.tollnek@student.uni-tuebingen.de>]

Dear Maarten,

thank you very much for your answer! I might also calculate a model,  
where I keep the persons with zero children. The problem is that the  
data stems from the 18th century and sometimes it is not clear if the  
people didn't have children or if they just were not reported. Also,  
there are couples with children, but from another marriage, where the  
number of children is set to zero; as we only would like to analyse  
families with children of both spouses, we have to leave out the  
couples with "zero" children at some point.

Regarding the modeling: yes, I want to calculate the average number of  
children given some external parameters.

Thanks again!

Best

Franziska

Zitat von Maarten Buis <maartenlbuis@gmail.com>:

> On Mon, Jan 23, 2012 at 11:43 AM, F Tollnek wrote:
> > Because there are uncertainties about the people who reported "zero"
> > children, I have to truncate the data at the zero values. As often proposed,
> > I used the Zero Truncated Poisson model for analyzing my data, but the
> > problem is that there is significant underdispersion given.
>
> I would need some really good reasons before doing that. In all
> likelihood you have created more problems than you solved, and you are
> much better off by keeping the childless persons (or couples) in your
> analysis. I would not be surprised if it is still significantly
> under-dispersed, but than you can use Joe Hilbe's -gnpoisson-, (see:
> -ssc desc gnpoisson-).
>
> All this depends on what you really want to model: the average number
> of kids given some explanatory variables or the decision process that
> leads to 0, 1, 2, ... kids. In the latter case none of these models
> apply: The decision to have no children is very different from the
> decision to 1 or more children. The decision between 1 or 2 or more
> children is likely to be very different than the decision between 2
> and 3 or more children, etc. So trying to summarize all these
> decisions with one set of parameters is likely to fail.
>
> This suggests to me a sequential logit model, but there are some
> difficulties with giving a causal interpretation to the parameters,
> which is probably what you have in mind when you are interested in the
> decision process. It is even difficult to define what a causal effect
> would be in such situations, see (Mare 2011).
>
> Hope this helps,
> Maarten
>
> Robert D. Mare (2011) Introduction to symposium on unmeasured
> heterogeneity in school transition models, Research in Social
> Stratification and Mobility, Volume 29, Issue 3, Pages 239-245.
> <http://dx.doi.org/10.1016/j.rssm.2011.05.004>
>
> --------------------------
> 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/