Hi Statalisters,

I am working on a project by using counting regression model of panel data. The data is in panel data form with panel variable (Company Name) and time variable (Year). Hausman test suggests a fixed-effect regression that fits in better. After detecting over-dispersion, I select negative binomial regression (xtnbreg) as my main regression model. However, the question is that the iteration can not reach a concave optimization result, which results in infinite loops.

I have read several related posts but still feel confused about the problem. If anyone could give me some suggestions based on the following alternatives, I will be more than grateful.

1. Use Poisson (xtpoisson) regression to substitute NB regression (xtnbreg), but it may result in an over-dispersion problem.

2. Use multilevel mixed-effects regression (menbreg) to substitute NB regression (xtnbreg), but as the manual book suggests, it may only work for the case -xtnbreg, re-. Because Hausman test already suggests fixed effects in my model, this alternative may not work well.

3. Add some tricks in NB regression. Some manual books suggest using some tricky commands to modify the optimization algorithm without losing the generality. For example, we can assign -difficult-, -technique()-, and -iterate()- in the regression. I have doubts about this because (1) The result usually keeps not concave after adding in some tricks; (2) I don't find enough theories to support the validity of these commands, like if I assign a fixed iteration number using -iterate()-, how many iterations should I assign?

An example is here
Code:
asdoc xtnbreg $y1list $x1list $controls i.industry i.fyear, fe dec(4) difficult technique(bhhh) iterate(50) replace
Hope anyone can kindly help me figure this out.
Thanks in advance