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Re: st: fixed effects glm - fractional dependent variable
From
Jeffrey Wooldridge <[email protected]>
To
[email protected]
Subject
Re: st: fixed effects glm - fractional dependent variable
Date
Sun, 13 May 2012 17:10:18 -0400
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 <[email protected]> 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
> <[email protected]> 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
>> [email protected]
>>
>>
>>
>>
>> On Mar 29,, joe j <[email protected]> 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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>
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