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New insights into the stochastic ray production frontier

Author

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  • Arne Henningsen

    (Department of Food and Resource Economics, University of Copenhagen)

  • Matěj Bělín

    (Center for Economic Research and Graduate Education, Economics Institute, Czech Republic)

  • Géraldine Henningsen

    (Department of Management Engineering, Technical University of Denmark)

Abstract

The stochastic ray production frontier was developed as an alternative to the traditional output distance function to model production processes with multiple inputs and multiple outputs. Its main advantage over the traditional approach is that it can be used when some output quantities of some observations are zero. In this paper, we briefly discuss—and partly refute—a few existing criticisms of the stochastic ray production frontier. Furthermore, we discuss some shortcomings of the stochastic ray production frontier that have not yet been addressed in the literature and that we consider more important than the existing criticisms: taking logarithms of the polar coordinate angles, non-invariance to units of measurement, and ordering of the outputs. We also give some practical advice on how to address the newly raised issues.

Suggested Citation

  • Arne Henningsen & Matěj Bělín & Géraldine Henningsen, 2017. "New insights into the stochastic ray production frontier," IFRO Working Paper 2017/01, University of Copenhagen, Department of Food and Resource Economics.
  • Handle: RePEc:foi:wpaper:2017_01
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    References listed on IDEAS

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    4. Bhattacharyya, Aditi & Pal, Sudeshna, 2013. "Financial reforms and technical efficiency in Indian commercial banking: A generalized stochastic frontier analysis," Review of Financial Economics, Elsevier, vol. 22(3), pages 109-117.
    5. Kai Sun, 2015. "Constrained nonparametric estimation of input distance function," Journal of Productivity Analysis, Springer, vol. 43(1), pages 85-97, February.
    6. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    7. Managi, Shunsuke & Opaluch, James J. & Jin, Di & Grigalunas, Thomas A., 2006. "Stochastic frontier analysis of total factor productivity in the offshore oil and gas industry," Ecological Economics, Elsevier, vol. 60(1), pages 204-215, November.
    8. Kumbhakar, Subal C. & Park, Byeong U. & Simar, Leopold & Tsionas, Efthymios G., 2007. "Nonparametric stochastic frontiers: A local maximum likelihood approach," Journal of Econometrics, Elsevier, vol. 137(1), pages 1-27, March.
    9. Niquidet, Kurt & Nelson, Harry, 2010. "Sawmill production in the interior of British Columbia: A stochastic ray frontier approach," Journal of Forest Economics, Elsevier, vol. 16(4), pages 257-267, December.
    10. John C. Quiggin & Anh Bui‐Lan, 1984. "The Use Of Cross‐Sectional Estimates Of Profit Functions For Tests Of Relative Efficiency: A Critical Review," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 28(1), pages 44-55, April.
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    Citations

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    Cited by:

    1. Juan José Price & Arne Henningsen, "undated". "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application," Working Papers 5, International Society for Efficiency and Productivity Analysis.
    2. Ahn, Heinz & Clermont, Marcel & Langner, Julia, 2023. "Comparative performance analysis of frontier-based efficiency measurement methods – A Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 307(1), pages 294-312.
    3. Garcia Suarez, F. & Quesada, G. Perez & Molina Ricetto, C., 2018. "Rangeland cattle production in Uruguay: single-output versus multi-output efficiency measures," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277178, International Association of Agricultural Economists.
    4. Bigerna, Simona & D’Errico, Maria Chiara & Polinori, Paolo, 2021. "Energy security and RES penetration in a growing decarbonized economy in the era of the 4th industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    5. Mike Tsionas & Marwan Izzeldin & Arne Henningsen & Evaggelos Paravalos, 2019. "Estimating Stochastic Ray Production Frontiers," IFRO Working Paper 2019/06, University of Copenhagen, Department of Food and Resource Economics.
    6. Julia Schaefer & Marcel Clermont, 2018. "Stochastic non-smooth envelopment of data for multi-dimensional output," Journal of Productivity Analysis, Springer, vol. 50(3), pages 139-154, December.
    7. Juan José Price & Arne Henningsen, 2023. "A ray-based input distance function to model zero-valued output quantities: Derivation and an empirical application," Journal of Productivity Analysis, Springer, vol. 60(2), pages 179-188, October.
    8. Mike Tsionas & Marwan Izzeldin & Arne Henningsen & Evaggelos Paravalos, 2022. "Addressing endogeneity when estimating stochastic ray production frontiers: a Bayesian approach," Empirical Economics, Springer, vol. 62(3), pages 1345-1363, March.
    9. Heinz Ahn & Marcel Clermont & Julia Langner, 2022. "The impact of selected input and output factors on measuring research efficiency of university research fields: insights from a purpose-, field-, and method-specific perspective," Journal of Business Economics, Springer, vol. 92(8), pages 1303-1335, October.

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    More about this item

    Keywords

    Stochastic Ray Production Frontier; Distance Function; Multiple Outputs; Primal Approach; Zero Output Quantities;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis

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