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Nonparametric estimation of concave production technologies by entropic methods

Author

Listed:
  • Gad Allon

    (Kellogg School of Management, Northwestern University, USA)

  • Michael Beenstock

    (Department of Economics, Hebrew University of Jerusalem, Jerusalem, Israel)

  • Steven Hackman

    (School of Industrial and Systems Engineering, Georgia Institute of Technology Atlanta, GA USA)

  • Ury Passy

    (Faculty of Industrial Engineering and Management Technion-Israel Institute of Technology, Haifa, Israel)

  • Alexander Shapiro

    (School of Industrial and Systems Engineering, Georgia Institute of Technology Atlanta, GA USA)

Abstract

An econometric methodology is developed for nonparametric estimation of concave production technologies. The methodology, based on the principle of maximum likelihood, uses entropic distance and convex programming techniques to estimate production functions. Empirical applications are presented to demonstrate the feasibility of the methodology in small and large datasets. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Gad Allon & Michael Beenstock & Steven Hackman & Ury Passy & Alexander Shapiro, 2007. "Nonparametric estimation of concave production technologies by entropic methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 795-816.
    • Handle: RePEc:jae:japmet:v:22:y:2007:i:4:p:795-816
    DOI: 10.1002/jae.918
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    Cited by:

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    2. Tsionas, Mike G., 2023. "Performance estimation when the distribution of inefficiency is unknown," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1212-1222.
    3. Daniel J. Henderson, 2009. "A Non‐parametric Examination of Capital–Skill Complementarity," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(4), pages 519-538, August.
    4. Tsionas, Mike G. & Izzeldin, Marwan, 2018. "Smooth approximations to monotone concave functions in production analysis: An alternative to nonparametric concave least squares," European Journal of Operational Research, Elsevier, vol. 271(3), pages 797-807.
    5. Tsionas, Mike G., 2021. "Optimal combinations of stochastic frontier and data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 294(2), pages 790-800.
    6. Chumpitaz, Ruben & Kerstens, Kristiaan & Paparoidamis, Nicholas & Staat, Matthias, 2010. "Comparing efficiency across markets: An extension and critique of the methodology," European Journal of Operational Research, Elsevier, vol. 205(3), pages 719-728, September.
    7. Preciado Arreola, José Luis & Johnson, Andrew L. & Chen, Xun C. & Morita, Hiroshi, 2020. "Estimating stochastic production frontiers: A one-stage multivariate semiparametric Bayesian concave regression method," European Journal of Operational Research, Elsevier, vol. 287(2), pages 699-711.
    8. Dimitris Bertsimas & Nishanth Mundru, 2021. "Sparse Convex Regression," INFORMS Journal on Computing, INFORMS, vol. 33(1), pages 262-279, January.
    9. Aubin-Frankowski, Pierre-Cyril & Szabo, Zoltan, 2022. "Handling hard affine SDP shape constraints in RKHSs," LSE Research Online Documents on Economics 115724, London School of Economics and Political Science, LSE Library.
    10. Eunji Lim & Peter W. Glynn, 2012. "Consistency of Multidimensional Convex Regression," Operations Research, INFORMS, vol. 60(1), pages 196-208, February.

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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