IDEAS home Printed from https://ideas.repec.org/p/foi/wpaper/2019_06.html
   My bibliography  Save this paper

Estimating Stochastic Ray Production Frontiers

Author

Listed:
  • Mike Tsionas

    (Department of Economics, Lancaster University Management School)

  • Marwan Izzeldin

    (Department of Economics, Lancaster University Management School)

  • Arne Henningsen

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

  • Evaggelos Paravalos

    (Department of Economics, Athens University of Economics and Business (Greece))

Abstract

In this paper, we consider the stochastic ray production function that has been revived recently by Henningsen et al. (2017). We use a profit-maximizing framework to resolve endogeneity problems that are likely to arise, as in all distance functions, and we derive the system of equations after incorporating technical inefficiency. As technical inefficiency enters non-trivially into the system of equations and the Jacobian is highly complicated, we propose Monte Carlo methods of inference. We illustrate the new approach using US banking data and we also address the problems of missing prices and selection of ordering for outputs.

Suggested Citation

  • 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.
    • Handle: RePEc:foi:wpaper:2019_06
    as

    Download full text from publisher

    File URL: http://okonomi.foi.dk/workingpapers/WPpdf/WP2019/IFRO_WP_2019_06.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. O'Donnell, Christopher J. & Coelli, Timothy J., 2005. "A Bayesian approach to imposing curvature on distance functions," Journal of Econometrics, Elsevier, vol. 126(2), pages 493-523, June.
    2. Géraldine Henningsen & Arne Henningsen & Uwe Jensen, 2015. "A Monte Carlo study on multiple output stochastic frontiers: a comparison of two approaches," Journal of Productivity Analysis, Springer, vol. 44(3), pages 309-320, December.
    3. BARTEN, Anton P., 1969. "Maximum likelihood estimation of a complete system of demand equations," LIDAM Reprints CORE 34, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Barten, A. P., 1969. "Maximum likelihood estimation of a complete system of demand equations," European Economic Review, Elsevier, vol. 1(1), pages 7-73.
    5. Bernhard Brümmer & Thomas Glauben & Geert Thijssen, 2002. "Decomposition of Productivity Growth Using Distance Functions: The Case of Dairy Farms in Three European Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(3), pages 628-644.
    6. Henningsen, Arne & Bělín, Matěj & Henningsen, Géraldine, 2017. "New insights into the stochastic ray production frontier," Economics Letters, Elsevier, vol. 156(C), pages 18-21.
    7. Terrell, Dek, 1996. "Incorporating Monotonicity and Concavity Conditions in Flexible Functional Forms," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(2), pages 179-194, March-Apr.
    8. Arne Henningsen & Christian Henning, 2009. "Imposing regional monotonicity on translog stochastic production frontiers with a simple three-step procedure," Journal of Productivity Analysis, Springer, vol. 32(3), pages 217-229, December.
    9. Efthymios G. Tsionas & Subal C. Kumbhakar & Emir Malikov, 2015. "Estimation of Input Distance Functions: A System Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(5), pages 1478-1493.
    10. Emir Malikov & Subal C. Kumbhakar & Mike G. Tsionas, 2016. "A Cost System Approach to the Stochastic Directional Technology Distance Function with Undesirable Outputs: The Case of us Banks in 2001–2010," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1407-1429, November.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. Barnett, William A. & Serletis, Apostolos, 2008. "Consumer preferences and demand systems," Journal of Econometrics, Elsevier, vol. 147(2), pages 210-224, December.
    5. Michaelides, Panayotis G. & Vouldis, Angelos T. & Tsionas, Efthymios G., 2010. "Globally flexible functional forms: The neural distance function," European Journal of Operational Research, Elsevier, vol. 206(2), pages 456-469, October.
    6. Huang, Tai-Hsin & Lin, Chung-I & Chen, Kuan-Chen, 2017. "Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies," Pacific-Basin Finance Journal, Elsevier, vol. 41(C), pages 93-110.
    7. 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.
    8. Kellermann, Magnus A., 2015. "Total Factor Productivity Decomposition and Unobserved Heterogeneity in Stochastic Frontier Models," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 44(1), pages 1-25, April.
    9. Apostolos Serletis & Guohua Feng, 2015. "Imposing Theoretical Regularity on Flexible Functional Forms," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 198-227, February.
    10. Link, Heike, 2006. "An econometric analysis of motorway renewal costs in Germany," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(1), pages 19-34, January.
    11. Humberto Brea-Solis & Sergio Perelman & David Saal, 2017. "Regulatory incentives to water losses reduction: the case of England and Wales," Journal of Productivity Analysis, Springer, vol. 47(3), pages 259-276, June.
    12. William A. Barnett & Ikuyasu Usui, 2007. "The Theoretical Regularity Properties of the Normalized Quadratic Consumer Demand Model," International Symposia in Economic Theory and Econometrics, in: Functional Structure Inference, pages 107-127, Emerald Group Publishing Limited.
    13. Feng, Guohua & Serletis, Apostolos, 2008. "Productivity trends in U.S. manufacturing: Evidence from the NQ and AIM cost functions," Journal of Econometrics, Elsevier, vol. 142(1), pages 281-311, January.
    14. Fofana, Abdulai & Jaffry, Shabbar, 2008. "Measuring Oligopsony Power of UK Salmon Retailers," Working Papers 61116, Scotland's Rural College (formerly Scottish Agricultural College), Land Economy & Environment Research Group.
    15. A. Wondemu Kifle, 2016. "Working Paper 237 - Decomposing Sources of Productivity Change in Small-Scale Farming in Ethiopia," Working Paper Series 2332, African Development Bank.
    16. Keuzenkamp, Hugo A. & Barten, Anton P., 1995. "Rejection without falsification on the history of testing the homogeneity condition in the theory of consumer demand," Journal of Econometrics, Elsevier, vol. 67(1), pages 103-127, May.
    17. Laura Spierdijk & Sherrill Shaffer & Tim Considine, 2016. "Adapting to changing input prices in response to the crisis: The case of US commercial banks," CAMA Working Papers 2016-15, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    18. Barnett, William A. & Serletis, Apostolos, 2008. "The Differential Approach to Demand Analysis and the Rotterdam Model," MPRA Paper 12319, University Library of Munich, Germany.
    19. Paris, Quirino & Caracciolo, Francesco, 2012. "Quantity Versus Shares in Estimating Demand Systems," Working Papers 124575, University of California, Davis, Department of Agricultural and Resource Economics.
    20. Tai-Hsin Huang & Yi-Huang Chiu & Chih-Ying Mao, 2021. "Imposing Regularity Conditions to Measure Banks’ Productivity Changes in Taiwan Using a Stochastic Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(2), pages 273-303, June.

    More about this item

    Keywords

    Stochastic ray production frontier; Technical inefficiency; Profit maximization; Bayesian inference;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:foi:wpaper:2019_06. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Geir Tveit (email available below). General contact details of provider: https://edirc.repec.org/data/foikudk.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.