Dear Stata Users,

I am trying to fit a Poisson model correcting for Sample selection using the heckpoisson command in Stata 17.0. I have a cross-sectional dataset for EU firms participating in meetings with the European Commission.

My dependent variable is the number of meetings held (meet) and the selection dependent variable is a dummy variable capturing whether any meeting was held for the specific firm (meet_d).

My explanatory variables include a dummy variable indicating whether a firm is in the top 2500 R&D firms (toprd_firm), a dummy variable indicating whether a firm has received an EU grant (grant) and the log of mean turnover approximating firm size (lmean_turn_m)

Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input float i_lobby int meet float(meet_d toprd_firm grant lmean_turn_m)
2034  1 1 0 0         .
 501  0 0 1 0         .
1402  3 1 0 0         .
2389  0 0 0 0         .
1552 14 1 0 0 15.396323
 206  2 1 0 1 13.301535
 600  0 0 0 0  10.76539
2409  . 0 0 0         .
2251  0 0 0 0  9.528091
1349  0 0 1 0 10.567322
2307  0 0 0 1 13.585784
2154  0 0 0 0  12.94011
1242  0 0 0 1 11.215186
1751 25 1 0 0         .
 565  0 0 0 0         .
end
label values grant g
label def g 0 "No Grant", modify
label def g 1 "Grant Receiver", modify



Typing the command:
Code:
# delimit ;
heckpoisson meet toprd_firm grant  lmean_turn_m i.cnt i.nace2, iter(100)
select(meet_d = toprd_firm lmean_turn_m);
I get
Code:
initial:       log likelihood =     -<inf>  (could not be evaluated)
could not find feasible values
Changing the maximizing method or using the difficult option does not help.

Fitting a Poisson model works fine and so does a Heckman model with the same variables, however given that my dependent variable is count it is not the appropriate way to go.