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

[anny fenton <afenton@fas.harvard.edu>]

Hi Rich,

That solved it. Thanks for the information.

Anny

On Fri, Mar 21, 2014 at 10:16 AM, Richard Goldstein
<richgold@ix.netcom.com> wrote:
> -logistic- is not doing what you think it is; logistic automatically
> forces everything that is not 0 to be non-zero so that the outcome is
> binary; -glm- (make sure to use "vce(robust)" also) does not do this;
> here is a quote from the help file for -logistic-: "logistic fits a
> logistic regression model of depvar on indepvars, where depvar is a 0/1
> variable (or, more precisely, a 0/non-0 variable)."
>
> Rich
>
> On 3/21/14, 10:05 AM, anny fenton wrote:
>> 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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-- 
Anny Fenton
Doctoral Candidate in Sociology
Harvard University
33 Kirkland Street
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