Abstract
The classic stochastic frontier panel data models provide no mechanism to disentangle individual time in- variant unobserved heterogeneity from ine ffi ciency. Greene (2005a,b) proposed a fixed-e ffects model spec- ification that distinguishes these two latent components and allows a time varying ine ffi ciency distribution. However, the maximum likelihood estimator proposed by Greene leads to biased ine ffi ciency estimates due to the incidental parameters problem. In this paper, we propose two alternative estimation procedures that, by relying on a first di fference data transformation, achieve consistency for n!1 with fixed T. Evidence from Monte Carlo simulations shows good finite sample performances of both approaches even in presence of small samples.
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