Efficient Semiparametric Estimation in a Stochastic Frontier Model

Byeong U. Park · Léopold Simar

108 citations0/0 files storedJournal of the American Statistical Association

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Efficiency Analysis Using DEA Statistical Methods and Inference Spatial and Panel Data Analysis

Abstract

Abstract This article considers the semiparametric stochastic frontier model with panel data that arises in the problem of measuring technical inefficiency in production processes. We assume a parametric form for the frontier function, which is linear in production inputs. The density of the individual firm-specific effects is considered to be unknown. We construct an efficient estimator of the slope parameters in the frontier function. We also give an estimator of the level of the frontier function and investigate its asymptotic properties. Furthermore, we provide a predictor of the individual effects that can be directly translated to firm-specific technical inefficiencies. Finally, we illustrate our methods through a real data example.

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References

1977Formulation and estimation of stochastic frontier production function models 1977Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error 1981Panel data and unobservable individual effects 1981Panel Data and Unobservable Individual Effects 1984Production Frontiers and Panel Data 1990Production frontiers with cross-sectional and time-series variation in efficiency levels 1992Estimating Efficiencies from Frontier Models with Panel Data: A Comparison of Parametric, Non-Parametric and Semi-Parametric Methods with Bootstrapping 1992Estimating efficiencies from frontier models with panel data: A comparison of parametric, non-parametric and semi-parametric methods with bootstrapping

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2012A NEW PANEL DATA TREATMENT FOR HETEROGENEITY IN TIME TRENDS 2000Multiple comparisons with the best, with economic applications 1998Stochastic panel frontiers: A semiparametric approach 2000A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data 2004

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