Consistent inference in fixed-effects stochastic frontier models
Federico Belotti () and
Journal of Econometrics, 2018, vol. 202, issue 2, 161-177
The classical stochastic frontier panel data models provide no mechanism to disentangle individual time invariant unobserved heterogeneity from inefficiency. Greene (2005a, b) proposed the so-called “true” fixed-effects specification that distinguishes these two latent components. However, due to the incidental parameters problem, his maximum likelihood estimator may lead to biased variance estimates. We propose two alternative estimators that achieve consistency for n→∞ with fixed T. Furthermore, we extend the Chen et al. (2014) results providing a feasible estimator when the inefficiency is heteroskedastic and follows a first-order autoregressive process. We investigate the behavior of the proposed estimators through Monte Carlo simulations showing good finite sample properties, especially in small samples. An application to hospitals’ technical efficiency illustrates the usefulness of the new approach.
Keywords: Stochastic frontiers; Fixed-effects; Panel data; Marginal simulated likelihood; Pairwise differencing (search for similar items in EconPapers)
JEL-codes: C13 C16 C23 (search for similar items in EconPapers)
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Working Paper: Consistent inference in fixed-effects stochastic frontier models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:202:y:2018:i:2:p:161-177
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