Hi,
I'im running regression on count data with 4 fixed effects.
Because I include 4 fixed effects it is not possible to use a simple poisson of nbreg (i set matsize at max and set emptycells drop). I used the the reghfde package by Sergio Correia which includes ppmlhdfe, a poisson regression with multiple fixed effects. However, I think my count data is overdispersed and should use a nbreg but there is no such thing in the reghdfe package.

1) Is there a test to run after ppmlhdfe to check for overdispersion?
2) or is there any alternative to run a nbreg with multiple fixed effects?

I ran the following code with results:
Code:
.  ppmlhdfe teamsize internetdummy invt_network_size invt_pat_count invt_career_age mobile_invt ,
vce(robust) absorb(cbsacode appyear uspc invt_id) d
(dropped 99460 observations that are either singletons or separated by a fixed effect)
note: 1 variable omitted because of collinearity: invt_career_age
Iteration 1:   deviance = 1.454e+05                  itol = 1.0e-04  subiters = 30  min(eta) =  
> -1.28                                                                                        
>        [p  ]
Iteration 2:   deviance = 1.407e+05  eps = 3.39e-02  itol = 1.0e-04  subiters = 19  min(eta) =  
> -1.94                                                                                        
>        [   ]
Iteration 3:   deviance = 1.406e+05  eps = 1.91e-04  itol = 1.0e-04  subiters = 10  min(eta) =  
> -2.03                                                                                        
>        [   ]
Iteration 4:   deviance = 1.406e+05  eps = 1.43e-07  itol = 1.0e-04  subiters = 3   min(eta) =  
> -2.03                                                                                        
>        [   ]
Iteration 5:   deviance = 1.406e+05  eps = 1.72e-07  itol = 1.0e-08  subiters = 62  min(eta) =  
> -2.02                                                                                        
>        [ s ]
Iteration 6:   deviance = 1.406e+05  eps = 7.80e-11  itol = 1.0e-08  subiters = 95  min(eta) =  
> -2.02                                                                                        
>        [ps ]
Iteration 7:   deviance = 1.406e+05  eps = 2.10e-14  itol = 1.0e-10  subiters = 116 min(eta) =  
> -2.02                                                                                        
>        [pso]
Iteration 8:   deviance = 1.406e+05  eps = 2.39e-14  itol = 1.0e-10  subiters = 117 min(eta) =  
> -2.02                                                                                        
>        [pso]
------------------------------------------------------------------------------------------------
> ------------
(legend: p: exact partial-out   s: exact solver   o: epsilon below tolerance)
Converged in 8 iterations and 452 HDFE sub-iterations (tol = 1.0e-08)

HDFE PPML regression                              No. of obs      =    362,605
Absorbing 4 HDFE groups                           Residual df     =    280,588
                                                  Wald chi2(4)    =    1354.34
Deviance             =   140641.425               Prob > chi2     =     0.0000
Log pseudolikelihood = -564388.4936               Pseudo R2       =     0.1827
-----------------------------------------------------------------------------------
                  |               Robust
         teamsize |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
    internetdummy |  -.0025327   .0053141    -0.48   0.634    -.0129481    .0078827
invt_network_size |   .0122162   .0003325    36.74   0.000     .0115645     .012868
   invt_pat_count |   -.001211   .0000895   -13.53   0.000    -.0013864   -.0010355
  invt_career_age |          0  (omitted)
      mobile_invt |  -.0057639   .0086128    -0.67   0.503    -.0226447     .011117
            _cons |   .9506205   .0060948   155.97   0.000     .9386748    .9625661
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
    cbsacode |       481           0         481     |
     appyear |         6           1           5     |
        uspc |       412           1         411    ?|
     invt_id |     81188          72       81116    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher
I could use a simple poisson regression with only 2 fixed effects, but that would affect the robustness.

Any suggestions?

Thanks
Ludo