The classic stochastic frontier panel data models provide no mechanism to disentangle individual time invariant unobserved heterogeneity from inefficiency. Greene (2005a,b) proposed a fixed-effects model specification that distinguishes these two latent components and allows a time varying inefficiency distribution. However, the maximum likelihood estimator proposed by Greene leads to biased inefficiency estimates due to the incidental parameters problem. In this paper, we propose two alternative estimation procedures that, by relying on a first difference data transformation, achieve consistency for n goes to infinity with fixed T. Evidence from Monte Carlo simulations shows good finite sample performances of both approaches even in presence of small samples.