I don't consider the binomial to be a continuous distribution. However, it often
happens that quite what error family you use is not that important. I'd play
with normal (Gaussian) or gamma.
Paradoxically, the fact that your final model does not fit very well -- although
well enough to be interesting -- helps you here
as it means that predictions stay well within the possible range.
Downstream of this, in a thesis, paper or oral presentation, it would often be
a good idea to disarm potential critics by mentioning the question of violating
the outcome range only to dismiss it as not biting in practice.
Pavlos C. Symeou
Dear Nick,
thank you for this. I have tried your suggestion below (to confirm, for
the option "link" I use "logit" and for the option "family" I use
"binomial"). However, I found no statistical significance in any of the
coefficients and after a series of various permutations, it looked to me
that the model could not fit the data sufficiently. I therefore returned
back to my original random-effects OLS regression whose use you suggest
for simplicity reasons. The OLS model's results are also consistent with
my theoretical arguments. But still, I need to check whether the
predicted values will lie in [0,10]. I have used the command - predict,
xb - to save the fitted values in a new variable. The fitted values
range from 5.58 to 6.93. The range of values for my observed variable is
(2.95 - 8.32). Would this suggest that my model does not suffer from the
limitations you note below?
Yours truly,
Pavlos
Nick Cox wrote:
> The numeric result for skewness doesn't quite match the fact that the mean
> is nearer the maximum than the minimum, not that that need that be the case.
>
> You possibly have a bit of a tail of fairly lousy firms, but otherwise this distribution
> looks quite healthy to me. How about
>
> gen repute = reputation / 10
> xtgee repute ..., link(logit) family(<continuous>)
>
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