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

 From "Jacobs, David" To "'statalist@hsphsun2.harvard.edu'" Subject st: RE: fixed effects glm - fractional dependent variable Date Thu, 29 Mar 2012 19:04:29 +0000

```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: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of joe j
Sent: Thursday, March 29, 2012 10:12 AM
To: statalist@hsphsun2.harvard.edu
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)
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?

Thanks,
Joe.
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