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Re: st: fixed effects glm - fractional dependent variable


From   joe j <joe.stata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: fixed effects glm - fractional dependent variable
Date   Fri, 30 Mar 2012 10:26:08 +0200

Thanks for the link. There is indeed some discussion on this topic.
Joe.

On Thu, Mar 29, 2012 at 8:47 PM, Anders Alexandersson
<andersalex@gmail.com> wrote:
> I do not have an answer to your question but -glm- ignores that you
> have panel data.
> For example, see http://www.stata.com/statalist/archive/2012-01/msg00595.html
>
> Anders Alexandersson
> andersalex@gmail.com
>
>
>
>
> On Mar 29,, joe j <joe.stata@gmail.com> wrote:
>> I have a panel data with the dependent variable being a faction,
>> including some zeros (about 1%) and ones (about 10%). These 0s and 1s
>> are real outcomes indeed (that is, not the results of censoring).
>>
>> So I am going in favor of a glm model as proposed in the literature
>> (e.g. Papke, Leslie E. and Jeffrey M. Wooldridge. 1996.  Econometric
>> Methods for Fractional Response Variables with an Application to
>> 401(k) Plan Participation Rates. Journal of Applied Econometrics
>> 11(6):619-632.):
>>
>> "glm dependent_variable independent_variable, family(binomial)
>> link(logit) robust"
>>
>> What I would like to do is run a fixed effect model. However, there
>> are too many dummy variables to create (over 16,000 in a sample of
>> over 40,000 observations); moreover, I am not sure dummy variable
>> approach is appropriate given the non-linear nature of the model.
>>
>> My first thought was to use:
>>
>> vce(cluster panel_variable)
>>
>> Is that the closest I could get to a fixed effect model?
>
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