st: RE: Fitting a model when the outcome is a proportion - glm versus logistic command

[Joe Canner <jcanner1@jhmi.edu>]

Anny,

Are you sure they gave different results?  By default, -logistic- reports odds ratios rather than coefficients.  If you compute the exponential of the coefficients from -glm- you should get the odds ratios from -logistic-.  

As far as fit statistics are concerned I got the same log-likelihood for both.  The other differences reflect the difference between the purpose and function of the two commands rather than discrepancies between them.

Regards,
Joe Canner
Johns Hopkins University School of Medicine

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of anny fenton
Sent: Friday, March 21, 2014 10:05 AM
To: statalist@hsphsun2.harvard.edu
Subject: st: Fitting a model when the outcome is a proportion - glm versus logistic command

Dear All,


When fitting an outcome that is a proportion, I know the typical
approach is to follow Papke and Wooldridge (1996) and use glm with
family(binomial), link(logic). However, I don't understand why one
would use glm instead of the logistic command why the two commands
would produce different fit statistics and coefficients (as they have
with my own results).


Thank you for any insight in advance,


Anny


Reference

Papke, L. E. and J. Wooldridge. Econometric methods for fractional
response variables with an application to 401(k) plan participation
rates. Journal of Applied Econometrics 11: 619-632.
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