Bookmark and Share

Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: xtnbreg - robusteness check and model relevance

From   "JVerkuilen (Gmail)" <>
Subject   Re: st: xtnbreg - robusteness check and model relevance
Date   Thu, 10 Jan 2013 09:30:26 -0500

On Thu, Jan 10, 2013 at 5:34 AM, Simon Falck <> wrote:
> Dear Statalist,
> The relevance or precision of a count model seems often to be described in terms of how close the predicted values are to the observed values, usually by comparing the distribution of probabilities of observed and predicted counts. However, from what I understand, it is not possible to use the command -prcounts- after -xtnbreg-, which is used after -nbreg- to derive predicted values. Any suggestions on what is a reasonable strategy in this case?>

I'm not sure what you mean by "robustness test" exactly, but I'll
assume you mean goodness of fit test.
More to the point that approach doesn't actually inform you much about
the difference between NB and Poisson, because NB and Poisson will
tend to make very similar point predictions. Where they will differ is
in terms of the level of uncertainty in the model. In general NB will
have wider confidence intervals than Poisson, sometimes much wider.

So a reasonable graphical test would be to generate predicted values
for important cases and see how often they line up with the
corresponding observed value. There are issues with this approach that
I can think of but it's a start.


*   For searches and help try:

© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   Site index