The "traditional" approach to the estimation of count panel data models with fixed effects is the conditional maximum-likelihood estimator.The pseudo maximum-likelihood principle can be used in these models to obtain orthogonality conditions that generate a robust estimator.However, this estimator is inconsistent when the instruments are not strictly exogenous.This paper proposes a GMM estimator for count panel data models with fixed fixed effects, based on a transformation of the conditional mean specification, that is consistent even when the explanatory variables are predetermined.Two applications are discussed: the relationship between patents and R&D expenditures and the explanation of technology transfer .