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# Re: st: RE: fixed effects glm - fractional dependent variable

 From joe j <[email protected]> To [email protected] Subject Re: st: RE: fixed effects glm - fractional dependent variable Date Fri, 30 Mar 2012 10:30:35 +0200

```Dave, the distribution of the proportion dependent variable (which is
a dissimilarity measure) is a bit odd in the sense that there are
relatively too many 1s (about 10%)-the rest of the distribution is
pretty flat.
Joe.

On Thu, Mar 29, 2012 at 9:04 PM, Jacobs, David
<[email protected]> wrote:
> It depends on the distribution and other characteristics of your dependent variable.
>
> Is it Poisson distributed?  If so, you could use the pooled time series fixed effects count models in Stata.
>
> Or is it a continuous variable with just a few zeros and distribution that is not terribly skewed or extremely odd in other ways.  If so, then you can probably use a regression based model such as -xtreg, fe-.
>
> Dave Jacobs
>
> -----Original Message-----
> From: [email protected] [mailto:[email protected]] On Behalf Of joe j
> Sent: Thursday, March 29, 2012 10:12 AM
> To: [email protected]
> Subject: st: fixed effects glm - fractional dependent variable
>
> Dear all,
>
> 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)
>
> 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?
>
> Thanks,
> Joe.
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```