So, after reading the appropriate literature I now know, that in principle Poisson FE could be estimated by just including the unit dummies.
However, there are too many of them in my data set and now I'm stuck with implementing this log-likelihood

$\sum _{ i=1
        }^{ N }{ \left[ -{ \alpha  }_{ i }\sum _{ t=1 }^{ T }{ {
        \lambda  }_{ it }+ } \ln { \left( { \alpha  }_{ i } \right)  }
        \sum _{ t=1 }^{ T }{ { y }_{ it }+ } \sum _{ t=1 }^{ T }{ { y
        }_{ it }\ln { \left( { \lambda  }_{ it } \right)  }  }  \right] 

I think it wont work with 'mle' because of the 'within sums over t periods' and needs an approach like in felogit.

06.11.2013 11:54, Riccardo (Jack) Lucchetti:
On Wed, 6 Nov 2013, Pindar wrote:

I found Jack's felogit.gfn on the server. I guess there is no feprobit because of the incidental parameters problem, or?

Yes. Moreover, if you have a panel dataset you can use the --random-effects options to the probit command to obtain RE probit estimation via Gaussian quadrature. By the way, your post made me realise this is still undocumented, although it's been in for a while and I've even used it in a paper! I'll try to update the docs asap.

  Riccardo (Jack) Lucchetti
  Dipartimento di Scienze Economiche e Sociali (DiSES)

  Università Politecnica delle Marche
  (formerly known as Università di Ancona)

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