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Re: st: fixed effects glm - fractional dependent variable


From   "Joseph J. Bakker" <joe.stata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: fixed effects glm - fractional dependent variable
Date   Tue, 31 Jul 2012 14:59:43 +0200

point taken.

On Mon, Jul 30, 2012 at 4:03 PM, Steve Samuels <sjsamuels@gmail.com> wrote:
>
> Please note:
>
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>
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>
>
>
> On Jul 30, 2012, at 4:09 AM, joe j wrote:
>
> Thank you very much Jeff,
>
> Somehow I missed your message, but nevertheless I ended up using the
> solution you suggested (thanks to the paper, Papke, L.E., J.M.
> Wooldridge (2008) Panel data methods for fractional response variables
> with an application to test pass rates Journal of Econometrics 145:
> 121–133). I used time averages, as well as time dummies. As you
> indicate, the results from both glm and xtgee were quite similar.
>
> However, my data is unbalanced. Are your solutions for unbalanced data
> available yet?
>
> Best,
> Joe.
>
> On Sun, May 13, 2012 at 11:10 PM, Jeffrey Wooldridge
> <jmwooldridge60@gmail.com> wrote:
>> proposes a correlated random effects approach. As you suspect, putting
>> in cross-sectional dummies introduces an incidental parameters problem
>> (not to mention the computational problem). If you have a balanced
>> panel you put in the time averages. With unbalanced panels it is
>> trickier but I have recently worked on some solutions.
>>
>> We used xtgee and glm in our panel data work and found that, even
>> though glm does not exploit the panel structure, it was almost as
>> efficient. The important decision was including the time averages to
>> control for the heterogeneity being correlated with the time-varying
>> covariates.
>>
>> Jeff
>>
>> On Fri, Mar 30, 2012 at 4:26 AM, joe j <joe.stata@gmail.com> wrote:
>>> 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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