The robustness of the hyperbolic efficiency estimator
The robustness properties of a specific type of orientation in the context of efficiency measurement using partial frontiers are investigated. This so called unconditional hyperbolic quantile estimator of efficiency has been recently studied and can be seen as an extension of the input/output methodology of partial frontiers that was introduced previously. The influence function as well as the breakdown point of this fully non-parametric and unconditional estimator are derived for a complete multivariate setup (multiple inputs and outputs). Like for the input and output quantile estimators, the hyperbolic quantile estimator is B-robust but unlike the two former types of estimator its breakdown point does not depend on the actual input or output level of the production unit. Some examples are given to assess the relevance of this type of estimator and to show the differences with the input and output quantile estimators of efficiency from both a robustness and a statistical efficiency point of view. Finally, a real life example is used to illustrate how the hyperbolic efficiency estimator might be used in a robust context. © 2012 Elsevier B.V. All rights reserved.
|Date of creation:||2013|
|Date of revision:|
|Publication status:||Published in: Computational statistics & data analysis (2013) v.57 n° 1,p.349–363|
|Contact details of provider:|| Postal: CP135, 50, avenue F.D. Roosevelt, 1050 Bruxelles|
Web page: http://difusion.ulb.ac.be
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Laurens Cherchye & Piet Vanden Abeele, 2002.
"On Research Efficiency: A Micro-Analysis of Dutch University Research in Economics and Business Management,"
Public Economics Working Paper Series
ces0206, Katholieke Universiteit Leuven, Centrum voor Economische Studiën, Working Group Public Economics.
- Cherchye, L. & Abeele, P. Vanden, 2005. "On research efficiency: A micro-analysis of Dutch university research in Economics and Business Management," Research Policy, Elsevier, vol. 34(4), pages 495-516, May.
- Simar, Léopold & Vanhems, Anne, 2012.
"Probabilistic characterization of directional distances and their robust versions,"
Journal of Econometrics,
Elsevier, vol. 166(2), pages 342-354.
- Simar, Léopold & Vanhems, Anne, 2010. "Probabilistic Characterization of Directional Distances and their Robust Versions," TSE Working Papers 10-195, Toulouse School of Economics (TSE).
- Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
- David C. Wheelock & Paul W. Wilson, 2007.
"Non-parametric, unconditional quantile estimation for efficiency analysis with an application to Federal Reserve check processing operations,"
2005-027, Federal Reserve Bank of St. Louis.
- Wheelock, David C. & Wilson, Paul W., 2008. "Non-parametric, unconditional quantile estimation for efficiency analysis with an application to Federal Reserve check processing operations," Journal of Econometrics, Elsevier, vol. 145(1-2), pages 209-225, July.
- Daouia, Abdelaati & Simar, Leopold, 2007. "Nonparametric efficiency analysis: A multivariate conditional quantile approach," Journal of Econometrics, Elsevier, vol. 140(2), pages 375-400, October.
- Aragon, Y. & Daouia, A. & Thomas-Agnan, C., 2005. "Nonparametric Frontier Estimation: A Conditional Quantile-Based Approach," Econometric Theory, Cambridge University Press, vol. 21(02), pages 358-389, April.
- repec:hal:journl:peer-00796744 is not listed on IDEAS
- Rolf Färe & Shawna Grosskopf, 2000. "Theory and Application of Directional Distance Functions," Journal of Productivity Analysis, Springer, vol. 13(2), pages 93-103, March.
- Daouia, Abdelaati & Gijbels, Irène, 2011. "Robustness and inference in nonparametric partial frontier modeling," Journal of Econometrics, Elsevier, vol. 161(2), pages 147-165, April.
When requesting a correction, please mention this item's handle: RePEc:ulb:ulbeco:2013/131706. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Benoit Pauwels)
If references are entirely missing, you can add them using this form.