Nick Cox wrote:
> Some confusion here between logarithms and logits?
If I'm thinking straight, you're arguing that the call to -glm,
link(logit)- only makes sense for models whose dependent variables are
already scaled 0-1, since the -link()- option does the transformation.
That's certainly what you suggested to me here:
http://www.stata.com/statalist/archive/2007-01/msg00315.html
I feel a Statalist sequel coming on. I've just finished re-fitting a
batch of fractional logit models to voting intention data after
discovering that I had log-transformed the dependent variables when it
wasn't necessary, largely due to the re-reading of the above post!
If this is so, which is the most appropriate Stata routine with which
to fit an LT-OLS regression model? Note that not everybody in my field
thinks this to be a good idea, anyway; indeed Paolino's (2001)
extensive Monte Carlo tests found that such models come off third best
against pure OLS and beta-distributed models in terms of bias,
efficiency and 'overconfidence', and across a range of distributions
to boot. It was this paper that encouraged me to move away from such
an approach.
--
Clive Nicholas
[Please DO NOT mail me personally here, but at
<[email protected]>. Thanks!]
Paolino P (2001) "Maximum Likelihood Estimation of Models with
Beta-Distributed Dependent Variables", Political Analysis 9(4):
325-46.
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