Hi,
I have a panel dataset consisting of 3,617 IDs where each ID has about 9-10 observations for it (it is an unbalanced panel with gaps). My Y is an over-dispersed count variable so I used xtnbreg, fe for my data and the estimator converged, but I now want to cluster standard errors at a particular level. Since xtnbreg does not have any option for clustering standard errors (or does it? perhaps I am wrong about that), I figured the only approach to take would be nbreg with i.id and clustered standard errors. I tried running just the simpler version of nbreg Y X i.year i.id without clustering standard errors, but it's been many many hours and it isn't converging--- we're on Iteration 39 now after ~14 hours of running. Should I assume it isn't going to converge at this point or just keep going? And if it isn't going to converge and the Y variable in question is definitely over dispersed, would it be better to use xtpoisson, fe instead,or would leaving it at xtnbreg, fe without clustering standard errors given that Y is over-dispersed?
Lastly, is there any literature I can refer to that helps me figure out why it is that a negative binomial fixed effects regression doesn't have a standard error clustering option whereas a poisson fixed effects regression does (or perhaps there is no conceptual reason and that's just how the command is built).
Thank you very much! I really appreciate it.
Regards,
Mansi Jain
Stanford University
I have a panel dataset consisting of 3,617 IDs where each ID has about 9-10 observations for it (it is an unbalanced panel with gaps). My Y is an over-dispersed count variable so I used xtnbreg, fe for my data and the estimator converged, but I now want to cluster standard errors at a particular level. Since xtnbreg does not have any option for clustering standard errors (or does it? perhaps I am wrong about that), I figured the only approach to take would be nbreg with i.id and clustered standard errors. I tried running just the simpler version of nbreg Y X i.year i.id without clustering standard errors, but it's been many many hours and it isn't converging--- we're on Iteration 39 now after ~14 hours of running. Should I assume it isn't going to converge at this point or just keep going? And if it isn't going to converge and the Y variable in question is definitely over dispersed, would it be better to use xtpoisson, fe instead,or would leaving it at xtnbreg, fe without clustering standard errors given that Y is over-dispersed?
Lastly, is there any literature I can refer to that helps me figure out why it is that a negative binomial fixed effects regression doesn't have a standard error clustering option whereas a poisson fixed effects regression does (or perhaps there is no conceptual reason and that's just how the command is built).
Thank you very much! I really appreciate it.
Regards,
Mansi Jain
Stanford University
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