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A BIAS-CORRECTED NONPARAMETRIC ENVELOPMENT ESTIMATOR OF FRONTIERS

Published online by Cambridge University Press:  01 October 2009

Luiza Bădin
Affiliation:
Bucharest Academy of Economic Studies and “Gheorghe Mihoc–Caius Iacob” Institute of Mathematical Statistics and Applied Mathematics Romanian Academy
Léopold Simar*
Affiliation:
Institut de Statistique Université Catholique de Louvain
*
*Address correspondence to Léopold Simar, Institut de Statistique, Université Catholique de Louvain, Voie du Roman Pays, 20 B-1348 Louvain-la-Neuve, Belgium; e-mail: simar@stat.ucl.ac.be.

Abstract

In efficiency analysis, the production frontier is defined as the set of the most efficient alternatives among all possible combinations in the input-output space. The nonparametric envelopment estimators rely on the assumption that all the observations fall on the same side of the frontier. The free disposal hull (FDH) estimator of the attainable set is the smallest free disposal set covering all the observations. By construction, the FDH estimator is an inward-biased estimator of the frontier.

The univariate extreme values representation of the FDH allows us to derive a bias-corrected estimator for the frontier. The presentation is based on a probabilistic formulation where the input-output pairs are realizations of independent random variables drawn from a joint distribution whose support is the production set. The bias-corrected estimator shares the asymptotic properties of the FDH estimator. But in finite samples, Monte Carlo experiments indicate that our bias-corrected estimator reduces significantly not only the bias of the FDH estimator but also its mean squared error, with no computational cost. The method is also illustrated with a real data example. A comparison with the parametric stochastic frontier indicates that the parametric approach can easily fail under wrong specification of the model.

Type
ARTICLES
Copyright
Copyright © Cambridge University Press 2009

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References

REFERENCES

Aigner, D.J., Lovell, C.A.K., & Schmidt, P. (1977) Formulation and estimation of stochastic frontier models. Journal of Econometrics 6, 2137.CrossRefGoogle Scholar
Cazals, C., Florens, J.P., & Simar, L. (2002) Nonparametric frontier estimation: A robust approach. Journal of Econometrics 106, 125.CrossRefGoogle Scholar
Charnes, A., Cooper, W.W., & Rhodes, E. (1978) Measuring the inefficiency of decision making units. European Journal of Operational Research 2, 429444.CrossRefGoogle Scholar
Cooke, P.J. (1979) Statistical inference for bounds of random variables. Biometrika 66, 367374.CrossRefGoogle Scholar
Daraio, C. & Simar, L. (2005) Introducing environmental variables in nonparametric frontier models: A probabilistic approach. Journal of Productivity Analysis 24, 93121.CrossRefGoogle Scholar
Debreu, G. (1951) The coefficient of resource utilization. Econometrica 19, 273292.CrossRefGoogle Scholar
Deprins, D., Simar, L., & Tulkens, H. (1984) Measuring labor inefficiency in post offices. In Marchand, M., Pestieau, P., & Tulkens, H. (eds.), The Performance of the Public Enterprises: Concepts and Measurements, pp. 243267. North–Holland.Google Scholar
Farrell, M.J. (1957) The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A 120, 253281.CrossRefGoogle Scholar
Jeong, S.O. & Simar, L. (2006) Linearly interpolated FDH efficiency score for nonconvex frontiers. Journal of Multivariate Analysis 97, 21412161.CrossRefGoogle Scholar
Jondrow, J., Lovell, C.A.K., Materov, I.S., & Schmidt, P. (1982) On the estimation of technical inefficiency in stochastic frontier production models. Journal of Econometrics 19, 233238.CrossRefGoogle Scholar
Kneip, A., Simar, L., & Wilson, P.W. (2008) Asymptotics and consistent bootstraps for DEA estimators in nonparametric frontier models. Econometric Theory 24, 16631697.CrossRefGoogle Scholar
Loh, W. (1984) Estimating an endpoint of a distribution with resampling methods. Annals of Statistics 12, 15431550.Google Scholar
Park, B., Simar, L., , L. & Weiner, C. (2000) The FDH estimator for productivity efficiency scores: Asymptotic properties. Econometric Theory 16, 855877.CrossRefGoogle Scholar
Robson, D.S. & Whitlock, J.H. (1964) Estimation of a truncation point. Biometrika 51, 3339.Google Scholar
Simar, L. (2007) How to improve the performances of DEA/FDH estimators in the presence of noise. Journal of Productivity Analysis 28, 183201.CrossRefGoogle Scholar
Simar, L. & Wilson, P.W. (in press) Estimation and inference in cross-sectional stochastic frontier models. Econometric Reviews.Google Scholar