IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v160y2017icp1-3.html
   My bibliography  Save this article

Metafrontier efficiency analysis with convex and non-convex metatechnologies by stochastic nonparametric envelopment of data

Author

Listed:
  • Afsharian, Mohsen

Abstract

This paper suggests how stochastic nonparametric envelopment of data (StoNED) can be extended as an estimator in the metafrontier efficiency analysis. Both convex and non-convex metatechnologies are formed and a semi-nonparametric estimation technique for the corresponding metafrontiers is developed. Remaining consistent with the metafrontier theory, the resulting estimated metafrontiers always envelope the estimated group frontiers.

Suggested Citation

  • Afsharian, Mohsen, 2017. "Metafrontier efficiency analysis with convex and non-convex metatechnologies by stochastic nonparametric envelopment of data," Economics Letters, Elsevier, vol. 160(C), pages 1-3.
    • Handle: RePEc:eee:ecolet:v:160:y:2017:i:c:p:1-3
    DOI: 10.1016/j.econlet.2017.08.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176517303294
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2017.08.006?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. Timo Kuosmanen & Andrew Johnson & Antti Saastamoinen, 2015. "Stochastic Nonparametric Approach to Efficiency Analysis: A Unified Framework," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 7, pages 191-244, Springer.
    2. 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, vol. 19(2-3), pages 233-238, August.
    3. Sipilainen, Timo & Kuosmanen, Timo & Kumbhakar, Subal C., 2008. "Measuring productivity differentials – An application to milk production in Nordic countries," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 44277, European Association of Agricultural Economists.
    4. Hayami, Yujiro & Ruttan, Vernon W, 1970. "Agricultural Productivity Differences Among Countries," American Economic Review, American Economic Association, vol. 60(5), pages 895-911, December.
    5. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    6. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    7. Kuosmanen, Timo, 2006. "Stochastic Nonparametric Envelopment of Data: Combining Virtues of SFA and DEA in a Unified Framework," Discussion Papers 11864, MTT Agrifood Research Finland.
    8. Timo Kuosmanen & Andrew L. Johnson, 2010. "Data Envelopment Analysis as Nonparametric Least-Squares Regression," Operations Research, INFORMS, vol. 58(1), pages 149-160, February.
    9. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    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. Mohsen Afsharian, 2020. "A metafrontier-based yardstick competition mechanism for incentivising units in centrally managed multi-group organisations," Annals of Operations Research, Springer, vol. 288(2), pages 681-700, May.
    2. Yantuan Yu & Yun Zhang & Xiao Miao, 2018. "Impacts of Dynamic Agglomeration Externalities on Eco-Efficiency: Empirical Evidence from China," IJERPH, MDPI, vol. 15(10), pages 1-27, October.
    3. Manuel Espitia‐Escuer & Lucia Isabel Garcia‐Cebrian, 2020. "Efficiency of football teams from an organisation management perspective," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 41(3), pages 321-338, April.
    4. Christina Bampatsou & George Halkos, 2022. "Meta-frontier analysis of technology gap for efficiency and productivity comparisons across different sub-groups of EU-27 countries," Economics Bulletin, AccessEcon, vol. 42(3), pages 1413-1421.
    5. Alda, Erik & Cuesta, Jose, 2019. "Measuring the efficiency of humanitarian aid," Economics Letters, Elsevier, vol. 183(C), pages 1-1.
    6. Afsharian, Mohsen & Ahn, Heinz & Harms, Sören Guntram, 2019. "Performance comparison of management groups under centralised management," European Journal of Operational Research, Elsevier, vol. 278(3), pages 845-854.
    7. Núñez, F. & Arcos-Vargas, A. & Villa, G., 2020. "Efficiency benchmarking and remuneration of Spanish electricity distribution companies," Utilities Policy, Elsevier, vol. 67(C).
    8. Kerstens, Kristiaan & O’Donnell, Christopher & Van de Woestyne, Ignace, 2019. "Metatechnology frontier and convexity: A restatement," European Journal of Operational Research, Elsevier, vol. 275(2), pages 780-792.
    9. Mahmood Mehdiloo & Jafar Sadeghi & Kristiaan Kerstens, 2024. "Top Down Axiomatic Modeling of Metatechnologies and Evaluating Directional Economic Efficiency," Working Papers 2024-EQM-03, IESEG School of Management.
    10. Yantuan Yu & Jianhuan Huang & Yanmin Shao, 2019. "The Sustainability Performance of Chinese Banks: A New Network Data Envelopment Analysis Approach and Panel Regression," Sustainability, MDPI, vol. 11(6), pages 1-25, March.
    11. Alexander Arévalo S & Víctor Giménez G & Diego Prior J, 2022. "Análisis de eficiencia en educación: una aplicación del método StoNED," Revista Desarrollo y Sociedad, Universidad de los Andes,Facultad de Economía, CEDE, vol. 92(2), pages 45-91, October.
    12. Le, Minh Hanh & Afsharian, Mohsen & Ahn, Heinz, 2021. "Inverse Frontier-based Benchmarking for Investigating the Efficiency and Achieving the Targets in the Vietnamese Education System," Omega, Elsevier, vol. 103(C).

    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. Delnava, Haleh & Khosravi, Ali & El Haj Assad, Mamdouh, 2023. "Metafrontier frameworks for estimating solar power efficiency in the United States using stochastic nonparametric envelopment of data (StoNED)," Renewable Energy, Elsevier, vol. 213(C), pages 195-204.
    2. Phuc Trong Ho & Pham Xuan Hung & Nguyen Duc Tien, 2023. "Effects of varieties and seasons on cost efficiency in rice farming: A stochastic metafrontier approach," Asian Journal of Agriculture and Rural Development, Asian Economic and Social Society, vol. 13(2), pages 120-129.
    3. Sipilainen, Timo & Kuosmanen, Timo & Kumbhakar, Subal C., 2008. "Measuring productivity differentials – An application to milk production in Nordic countries," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 44277, European Association of Agricultural Economists.
    4. Ferrara, Giancarlo & Vidoli, Francesco, 2017. "Semiparametric stochastic frontier models: A generalized additive model approach," European Journal of Operational Research, Elsevier, vol. 258(2), pages 761-777.
    5. Kuosmanen, Timo & Johnson, Andrew, 2017. "Modeling joint production of multiple outputs in StoNED: Directional distance function approach," European Journal of Operational Research, Elsevier, vol. 262(2), pages 792-801.
    6. Saeid Hajihassaniasl & Recep Kök, 2016. "Scale effect in Turkish manufacturing industry: stochastic metafrontier analysis," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 5(1), pages 1-17, December.
    7. Cristina Polo & Julián Ramajo & Alejandro Ricci‐Risquete, 2021. "A stochastic semi‐non‐parametric analysis of regional efficiency in the European Union," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(1), pages 7-24, February.
    8. Stefan Seifert, 2015. "Measuring Productivity When Technologies Are Heterogeneous: A Semi-Parametric Approach for Electricity Generation," Discussion Papers of DIW Berlin 1526, DIW Berlin, German Institute for Economic Research.
    9. Tanko, Mohammed & Ismaila, Salifu, 2021. "How culture and religion influence the agriculture technology gap in Northern Ghana," World Development Perspectives, Elsevier, vol. 22(C).
    10. K Hervé Dakpo & Laure Latruffe & Yann Desjeux & Philippe Jeanneaux, 2022. "Modeling heterogeneous technologies in the presence of sample selection: The case of dairy farms and the adoption of agri‐environmental schemes in France," Agricultural Economics, International Association of Agricultural Economists, vol. 53(3), pages 422-438, May.
    11. Kuosmanen, Timo, 2012. "Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model," Energy Economics, Elsevier, vol. 34(6), pages 2189-2199.
    12. Economou, Polychronis & Malefaki, Sonia & Kounetas, Konstantinos, 2019. "Productive Performance and Technology Gaps using a Bayesian Metafrontier Production Function: A cross-country comparison," MPRA Paper 94462, University Library of Munich, Germany.
    13. John N. Ng’ombe, 2017. "Technical efficiency of smallholder maize production in Zambia: a stochastic meta-frontier approach," Agrekon, Taylor & Francis Journals, vol. 56(4), pages 347-365, October.
    14. Jose Manuel Cordero & Cristina Polo & Javier Salinas-Jiménez, 2021. "Subjective Well-Being and Heterogeneous Contexts: A Cross-National Study Using Semi-Nonparametric Frontier Methods," Journal of Happiness Studies, Springer, vol. 22(2), pages 867-886, February.
    15. Roengchai Tansuchat, 2023. "A Copula-Based Meta-Stochastic Frontier Analysis for Comparing Traditional and HDPE Geomembranes Technology in Sea Salt Farming among Farmers in Phetchaburi, Thailand," Agriculture, MDPI, vol. 13(4), pages 1-23, March.
    16. Owusu, Eric S. & Bravo-Ureta, Boris E., 2022. "Reap when you sow? The productivity impacts of early sowing in Malawi," Agricultural Systems, Elsevier, vol. 199(C).
    17. Laure Latruffe & Andreas Niedermayr & Yann Desjeux & K Herve Dakpo & Kassoum Ayouba & Lena Schaller & Jochen Kantelhardt & Yan Jin & Kevin Kilcline & Mary Ryan & Cathal O’Donoghue, 2023. "Identifying and assessing intensive and extensive technologies in European dairy farming," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(4), pages 1482-1519.
    18. 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.
    19. Chukwujekwu A. Obianefo & John N. Ng’ombe & Agness Mzyece & Blessing Masasi & Ngozi J. Obiekwe & Oluchi O. Anumudu, 2021. "Technical Efficiency and Technological Gaps of Rice Production in Anambra State, Nigeria," Agriculture, MDPI, vol. 11(12), pages 1-13, December.
    20. Jose M. Cordero & Cristina Polo & Daniel Santín, 2020. "Assessment of new methods for incorporating contextual variables into efficiency measures: a Monte Carlo simulation," Operational Research, Springer, vol. 20(4), pages 2245-2265, December.

    More about this item

    Keywords

    Metafrontier analysis; Convex and non-convex metatechnologies; Stochastic nonparametric envelopment of data (StoNED);
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

    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:160:y:2017:i:c:p:1-3. 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.