--- Joan Holand <[email protected]> wrote:
> I'm running a loglinear model (categorical data; Stata V.9, Windows
> XP) using the -glm- (option: fam(pois)) and the -poisson- command.
>
> I have a question about the option -robust-: When is it reasonable to
> use this option? I have read that it makes the confidence intervall
> narrower. Are there other reasons for / benefits of using this
> option?
When you are doing some type of regression modeling you are usually
making a model for both the mean and the variance. For example with the
poisson family you are assuming that the variance of the dependent
variable (the count) is the same as the mean. Another example is with
-regress- or the normal family you are assuming that the variance
remains constant (homoscedasticity). If you think you moddeled the mean
correctly but are not sure whether you modeled the variance correctly
then specifying the robust option corrects your standard errors for the
possible misspecification of your variance. This correction can lead
to both larger and smaller standard errors.
Robust is not a magic option though. It is based on an asymptotic
argument so I would not put too much faith in it in small samples.
Furthermore, it still assumes that you've modeled the mean correctly,
so you still need to check your model for that (i.e. look at the
residuals). Freedman (2006) is a (too) critical and readable piece on
the robust option.
Hope this helps,
Maarten
David A. Freedman (2006) `On The So-Called "Huber Sandwich Estimator"
and "Robust Standard Errors"', The American Statistician, 60(4), pp. 299-302.
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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