IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v138y2016icp42-45.html
   My bibliography  Save this article

Maximum likelihood estimation of the revenue function system with output-specific technical efficiency

Author

Listed:
  • Kumbhakar, Subal C.
  • Lai, Hung-pin

Abstract

In this paper we propose a non-radial and output-specific measure of technical efficiency which is new in a stochastic frontier model. We consider a multi-output multi-input transformation function formulation that incorporates output-specific technical efficiency (OSTE) in a revenue maximizing framework. Starting from the dual revenue function with OSTE, we develop the maximum likelihood method to estimate the parameters of the translog revenue-share system as well as predict OSTE components using some novel results from the closed skew-normal distribution.

Suggested Citation

  • Kumbhakar, Subal C. & Lai, Hung-pin, 2016. "Maximum likelihood estimation of the revenue function system with output-specific technical efficiency," Economics Letters, Elsevier, vol. 138(C), pages 42-45.
    • Handle: RePEc:eee:ecolet:v:138:y:2016:i:c:p:42-45
    DOI: 10.1016/j.econlet.2015.11.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176515004772
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2015.11.021?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Simar, Léopold & Vanhems, Anne, 2012. "Probabilistic characterization of directional distances and their robust versions," Journal of Econometrics, Elsevier, vol. 166(2), pages 342-354.
    2. Roberto Colombi & Subal Kumbhakar & Gianmaria Martini & Giorgio Vittadini, 2014. "Closed-skew normality in stochastic frontiers with individual effects and long/short-run efficiency," Journal of Productivity Analysis, Springer, vol. 42(2), pages 123-136, October.
    3. Simar, Léopold & Vanhems, Anne & Wilson, Paul W., 2012. "Statistical inference for DEA estimators of directional distances," European Journal of Operational Research, Elsevier, vol. 220(3), pages 853-864.
    4. Murphy, Kevin M & Topel, Robert H, 2002. "Estimation and Inference in Two-Step Econometric Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 88-97, January.
    5. Kumbhakar,Subal C. & Wang,Hung-Jen & Horncastle,Alan P., 2015. "A Practitioner's Guide to Stochastic Frontier Analysis Using Stata," Cambridge Books, Cambridge University Press, number 9781107029514, October.
    6. Chen, Yi-Yi & Schmidt, Peter & Wang, Hung-Jen, 2014. "Consistent estimation of the fixed effects stochastic frontier model," Journal of Econometrics, Elsevier, vol. 181(2), pages 65-76.
    7. Subal C. Kumbhakar & Efthymios G. Tsionas, 2011. "Stochastic error specification in primal and dual production systems," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 270-297, March.
    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


    Cited by:

    1. Levent Kutlu & Shasha Liu & Robin C. Sickles, 2022. "Cost, Revenue, and Profit Function Estimates," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 16, pages 641-679, Springer.
    2. Reinaldo B. Arellano-Valle & Adelchi Azzalini, 2022. "Some properties of the unified skew-normal distribution," Statistical Papers, Springer, vol. 63(2), pages 461-487, April.
    3. Tsionas, Mike G., 2017. "The profit function system with output- and input-specific technical efficiency," Economics Letters, Elsevier, vol. 151(C), pages 111-114.
    4. Badunenko, Oleg & Kumbhakar, Subal C., 2017. "Economies of scale, technical change and persistent and time-varying cost efficiency in Indian banking: Do ownership, regulation and heterogeneity matter?," European Journal of Operational Research, Elsevier, vol. 260(2), pages 789-803.
    5. Yu, Yang & Fan, Linlin, 2021. "How Does Supplemental Nutrition Assistance Program Affect Household Food Waste?," 2021 Annual Meeting, August 1-3, Austin, Texas 313984, Agricultural and Applied Economics Association.

    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. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    2. Dorgyles C.M. Kouakou, 2022. "Separating innovation short-run and long-run technical efficiencies: Evidence from the Economic Community of West African States (ECOWAS)," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 19(1), pages 103-141, June.
    3. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2021. "What do we know from the vast literature on efficiency and productivity in healthcare? A Systematic Review and Bibliometric Analysis," CEPA Working Papers Series WP092021, School of Economics, University of Queensland, Australia.
    4. Kutlu, Levent & Tran, Kien C. & Tsionas, Mike G., 2019. "A time-varying true individual effects model with endogenous regressors," Journal of Econometrics, Elsevier, vol. 211(2), pages 539-559.
    5. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2022. "Efficiency Analysis with Stochastic Frontier Models Using Popular Statistical Softwares," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 129-171, Springer.
    6. Dorgyles C.M. Kouakou, 2022. "Separating innovation short-run and long-run technical efficiencies: Evidence from the Economic Community of West African States (ECOWAS)," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 19(1), pages 103-141, June.
    7. Habtamu ALEM, 2018. "Effects of model specification, short-run, and long-run inefficiency: an empirical analysis of stochastic frontier models," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 64(11), pages 508-516.
    8. Huaping Sun & Bless Kofi Edziah & Xiaoqian Song & Anthony Kwaku Kporsu & Farhad Taghizadeh-Hesary, 2020. "Estimating Persistent and Transient Energy Efficiency in Belt and Road Countries: A Stochastic Frontier Analysis," Energies, MDPI, vol. 13(15), pages 1-19, July.
    9. Adom, Philip Kofi & Adams, Samuel, 2020. "Decomposition of technical efficiency in agricultural production in Africa into transient and persistent technical efficiency under heterogeneous technologies," World Development, Elsevier, vol. 129(C).
    10. Simar, Leopold & Wilson, Paul, 2018. "Technical, Allocative and Overall Efficiency: Inference and Hypothesis Testing," LIDAM Discussion Papers ISBA 2018018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Caitlin O’Loughlin & Léopold Simar & Paul W. Wilson, 2023. "Methodologies for assessing government efficiency," Chapters, in: António Afonso & João Tovar Jalles & Ana Venâncio (ed.), Handbook on Public Sector Efficiency, chapter 4, pages 72-101, Edward Elgar Publishing.
    12. Daraio, Cinzia & Simar, Leopold & Wilson, Paul, 2019. "Quality and its impact on efficiency," LIDAM Discussion Papers ISBA 2019004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    13. Magambo, Isaiah & Dikgang, Johane & Gelo, Dambala & Tregenna, Fiona, 2021. "Environmental and Technical Efficiency in Large Gold Mines in Developing Countries," MPRA Paper 108068, University Library of Munich, Germany.
    14. Tommaso Agasisti & Sabine Gralka, 2019. "The transient and persistent efficiency of Italian and German universities: a stochastic frontier analysis," Applied Economics, Taylor & Francis Journals, vol. 51(46), pages 5012-5030, October.
    15. Sabine Gralka, 2018. "Persistent inefficiency in the higher education sector: evidence from Germany," Education Economics, Taylor & Francis Journals, vol. 26(4), pages 373-392, July.
    16. Cheng, Xiaomei & Bjørndal, Endre & Lien, Gudbrand & Bjørndal, Mette, 2015. "Productivity Development for Norwegian Electricity Distribution Companies 2004-2013," Discussion Papers 2015/27, Norwegian School of Economics, Department of Business and Management Science.
    17. Kibrom A. Abay & Tesfamicheal Wossen & Jordan Chamberlin, 2023. "Mismeasurement and efficiency estimates: Evidence from smallholder survey data in Africa," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(2), pages 413-434, June.
    18. Wanglin Ma & Kathryn Bicknell & Alan Renwick, 2019. "Feed use intensification and technical efficiency of dairy farms in New Zealand," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 63(1), pages 20-38, January.
    19. Emilie Caldeira & Alou Adessé Dama & Ali Compaoré & Mario Mansour & Grégoire Rota-Graziosi, 2020. "Tax effort in Sub-Saharan African countries : evidence from a new dataset," Working Papers hal-02543162, HAL.
    20. Kneip, Alois & Simar, Léopold & Wilson, Paul W., 2015. "When Bias Kills The Variance: Central Limit Theorems For Dea And Fdh Efficiency Scores," Econometric Theory, Cambridge University Press, vol. 31(2), pages 394-422, April.

    More about this item

    Keywords

    Transformation function; Stochastic frontier; Technical inefficiency; Closed skew-normal distribution;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

    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:eee:ecolet:v:138:y:2016:i:c:p:42-45. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.