Consistent inference in fixed-effects stochastic frontier models
Federico Belotti () and
No 1147, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area
The classical stochastic frontier panel data models provide no mechanism for disentangling individual time-invariant unobserved heterogeneity from inefficiency. Greene (2005a, b) proposed the ‘true’ fixed-effects specification, which distinguishes these two latent components while allowing for time-variant inefficiency. However, due to the incidental parameters problem, the maximum likelihood estimator proposed by Greene may lead to biased variance estimates. We propose two alternative estimation procedures that, by relying on a first-difference data transformation, achieve consistency when n goes to infinity with fixed T. Furthermore, we extend the approach of Chen et al. (2014) by providing a computationally feasible solution for estimating models in which inefficiency can be heteroskedastic and may follow a first-order autoregressive process. We investigate the finite sample behavior of the proposed estimators through a set of Monte Carlo experiments. Our results show good finite sample properties, especially in small samples. We illustrate the usefulness of the new approach by applying it to the technical efficiency of hospitals.
Keywords: stochastic frontiers; fixed-effects; panel data; pairwise differencing (search for similar items in EconPapers)
JEL-codes: C13 C16 C23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-eff and nep-ore
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Journal Article: Consistent inference in fixed-effects stochastic frontier models (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wptemi:td_1147_17
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