Advanced Search
MyIDEAS: Login to save this paper or follow this series

Stochastic FDH/DEA estimators for Frontier Analysis

Contents:

Author Info

  • Leopold Simar

    (Universite Catholique de Louvain and Toulouse School of Economics)

  • Valentin Zelenyuk

    (Kyiv School of Economics and Kyiv Economics Institute)

Abstract

In this paper we extend the work of Simar (2007) introducing noise in nonparametric frontier models. We develop an approach that synthesizes the best features of the two main methods in the estimation of production efficiency. Specifically, our approach first allows for statistical noise, similar to Stochastic Frontier Analysis (even in a more flexible way), and second, it allows modelling multiple-inputs-multiple-outputs technologies without imposing parametric assumptions on production relationship, similar to what is done in non-parametric methods (DEA, FDH, etc. . . ). The methodology is based on the theory of local maximum likelihood estimation and extends recent works of Park, Kumbhakar, Simar and Tsionas (2007) and Park, Simar and Zelenyuk (2006). Our method is suitable for modelling and estimation of the marginal effects onto inefficiency level jointly with estimation of marginal effects of input. The approach is robust to heteroskedastic cases and to various (unknown) distributions of statistical noise and inefficiency, despite assuming simple anchorage models. The method also improves DEA/FDH estimators, by allowing them to be quite robust to statistical noise and especially to outliers, which were the main problems of the original DEA/FDH. The procedure shows great performance for various simulated cases and is also illustrated for some real data sets.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://repec.kse.org.ua/pdf/KSE_dp8.pdf
File Function: First version, May 2008
Download Restriction: no

Bibliographic Info

Paper provided by Kyiv School of Economics in its series Discussion Papers with number 8.

as in new window
Length:
Date of creation: Jun 2008
Date of revision:
Handle: RePEc:kse:dpaper:8

Note: Under review in Journal of Business and Economic Statistics
Contact details of provider:
Postal: 13 Yakira Str, 04119 Kyiv
Phone: (38-044)492-8012
Fax: (38-044)492-8011
Email:
Web page: http://www.kse.org.ua/
More information through EDIRC

Related research

Keywords: Stochastic Frontier; Nonparametric Frontier; Local Maximum Likelihood;

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
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.:
as in new window
  1. Simar, L. & Wilson, P.W., 1999. "Statistical Inference in Nonparametric Frontier Models: the State of the Art," Papers, Catholique de Louvain - Institut de statistique 9904, Catholique de Louvain - Institut de statistique.
  2. Daouia, Abdelaati & Simar, Leopold, 2007. "Nonparametric efficiency analysis: A multivariate conditional quantile approach," Journal of Econometrics, Elsevier, Elsevier, vol. 140(2), pages 375-400, October.
  3. Byeong U. Park & Leopold Simar & Valentin Zelenyuk, 2008. "Local Likelihood Estimation of Truncated Regression and Its Partial Derivatives: Theory and Application," Discussion Papers, Kyiv School of Economics 7, Kyiv School of Economics.
  4. RITTER, Christian & SIMAR, Leopold, 1994. "Pitfalls of Normal-Gamma Stochastic Frontier Models," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 1994041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  5. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, Elsevier, vol. 13(1), pages 57-66, May.
  6. Kneip, A. & Simar, L., . "A general framework for frontier estimation with panel data," CORE Discussion Papers RP, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) -1224, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  7. Kumbhakar, Subal C. & Park, Byeong U. & Simar, Leopold & Tsionas, Efthymios G., 2007. "Nonparametric stochastic frontiers: A local maximum likelihood approach," Journal of Econometrics, Elsevier, Elsevier, vol. 137(1), pages 1-27, March.
  8. Alois Kneip & Léopold Simar & Paul W. Wilson, 2006. "Asymptotics and Consistent Bootstraps for DEA Estimators in Non-parametric Frontier Models," Bonn Econ Discussion Papers, University of Bonn, Germany bgse12_2006, University of Bonn, Germany.
  9. Simar, Léopold, 2003. "How to Improve the Performances of DEA/FDH Estimators in the Presence of Noise?," SFB 373 Discussion Papers, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes 2003,33, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  10. Park, B.U. & Simar, L. & Weiner, Ch., 2000. "The Fdh Estimator For Productivity Efficiency Scores," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 16(06), pages 855-877, December.
  11. Kneip, Alois & Park, Byeong U. & Simar, L opold, 1998. "A Note On The Convergence Of Nonparametric Dea Estimators For Production Efficiency Scores," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 14(06), pages 783-793, December.
  12. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, Elsevier, vol. 19(2-3), pages 233-238, August.
  13. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, Elsevier, vol. 6(1), pages 21-37, July.
  14. Leopold Simar & Paul Wilson, 2010. "Inferences from Cross-Sectional, Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, Taylor & Francis Journals, vol. 29(1), pages 62-98.
  15. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, Elsevier, vol. 46(1-2), pages 141-163.
  16. Atkinson, Scott E. & Primont, Daniel, 2002. "Stochastic estimation of firm technology, inefficiency, and productivity growth using shadow cost and distance functions," Journal of Econometrics, Elsevier, Elsevier, vol. 108(2), pages 203-225, June.
  17. O. B. Olesen & N. C. Petersen, 1995. "Chance Constrained Efficiency Evaluation," Management Science, INFORMS, INFORMS, vol. 41(3), pages 442-457, March.
  18. Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, Elsevier, vol. 106(1), pages 1-25, January.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. G. Dosi & M. Grazzi & L. Marengo & S. Settepanella, 2014. "Production theory: accounting for firm heterogeneity and technical change," Working Papers, Dipartimento Scienze Economiche, Universita' di Bologna wp931, Dipartimento Scienze Economiche, Universita' di Bologna.
  2. Anne-Kathrin Last & Heike Wetzel, 2009. "Effizienzmessverfahren – eine Einführung," Working Paper Series in Economics, University of Lüneburg, Institute of Economics 145, University of Lüneburg, Institute of Economics.
  3. Bellenger, Moriah J. & Herlihy, Alan T., 2010. "Performance-based environmental index weights: Are all metrics created equal?," Ecological Economics, Elsevier, Elsevier, vol. 69(5), pages 1043-1050, March.
  4. Krasnopjorovs, Olegs, 2013. "Latvijas ekonomikas izaugsmi noteicošie faktori
    [Factors of Economic Growth in Latvia]
    ," MPRA Paper 47550, University Library of Munich, Germany.
  5. Minegishi, Kota, 2013. "Explaining Production Heterogeneity By Contextual Environments: Two-Stage DEA Application to Technical Change Measurement," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C., Agricultural and Applied Economics Association 150289, Agricultural and Applied Economics Association.
  6. Keshvari, Abolfazl & Kuosmanen, Timo, 2013. "Stochastic non-convex envelopment of data: Applying isotonic regression to frontier estimation," European Journal of Operational Research, Elsevier, Elsevier, vol. 231(2), pages 481-491.
  7. Mark Andor & Frederik Hesse, . "The StoNED age: The Departure Into a New Era of Efficiency Analysis? An MC study Comparing StoNED and the "Oldies" (SFA and DEA)," Working Papers, Institute of Spatial and Housing Economics, Munster Universitary 201285, Institute of Spatial and Housing Economics, Munster Universitary.
  8. Uwe Cantner & Jens J. Krüger & Rene Söllner, 2010. "Product Quality, Product Price, and Share Dynamics in the German Compact Car Market," Jena Economic Research Papers, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics 2010-024, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics.
  9. Madau, Fabio A., 2012. "Technical and scale efficiency in the Italian Citrus Farming: A comparison between Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis(DEA) Models," MPRA Paper 41403, University Library of Munich, Germany.
  10. Valentin Zelenyuk, 2014. "Scale efficiency and homotheticity: equivalence of primal and dual measures," Journal of Productivity Analysis, Springer, Springer, vol. 42(1), pages 15-24, August.
  11. Dongwei Su & Xingxing He, 2012. "Ownership structure, corporate governance and productive efficiency in China," Journal of Productivity Analysis, Springer, Springer, vol. 38(3), pages 303-318, December.
  12. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, Springer, vol. 38(1), pages 11-28, August.
  13. Valentin Zelenyuk & Claudia Curi & Paolo Guarda & Ana Lozano-Vivas, 2011. "Is foreign-bank efficiency in financial centers driven by home-country characteristics?," CEPA Working Papers Series, School of Economics, University of Queensland, Australia WP022011, School of Economics, University of Queensland, Australia.
  14. Picazo-Tadeo, Andrés J. & Beltrán-Esteve, Mercedes & Gómez-Limón, José A., 2012. "Assessing eco-efficiency with directional distance functions," European Journal of Operational Research, Elsevier, Elsevier, vol. 220(3), pages 798-809.
  15. Mark Andor & Frederik Hesse, 2014. "The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)," Journal of Productivity Analysis, Springer, Springer, vol. 41(1), pages 85-109, February.
  16. Chavas, Jean-Paul & Kim, Kwansoo, 2013. "Nonparametric Analysis of Technology and Productivity under Non-Convexity," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C., Agricultural and Applied Economics Association 149684, Agricultural and Applied Economics Association.
  17. Andor, Mark & Hesse, Frederik, 2012. "The StoNED age: The departure into a new era of efficiency analysis? An MC study comparing StoNED and the "oldies" (SFA and DEA)," CAWM Discussion Papers, Center of Applied Economic Research Münster (CAWM), University of Münster 60, Center of Applied Economic Research Münster (CAWM), University of Münster.
  18. Girard, Stéphane & Guillou, Armelle & Stupfler, Gilles, 2013. "Frontier estimation with kernel regression on high order moments," Journal of Multivariate Analysis, Elsevier, Elsevier, vol. 116(C), pages 172-189.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:kse:dpaper:8. 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: (Olena Nizalova).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.