IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v258y2017i2p761-777.html
   My bibliography  Save this article

Semiparametric stochastic frontier models: A generalized additive model approach

Author

Listed:
  • Ferrara, Giancarlo
  • Vidoli, Francesco

Abstract

The choice of the functional form of the frontier into a stochastic frontier model is typically neglected in applications and canonical functions are usually considered. This paper introduces a semiparametric approach for stochastic frontier estimation that extends previous works based on pseudo-likelihood estimators allowing flexibility in model selection and capability of imposing monotonicity and concavity constraints. For these purposes the present work introduces a generalized additive framework that moreover permits to model the influence of contextual/environmental factors to the hypothesized production process by the relative extension given by generalized additive models for location, scale and shape. Through some Monte Carlo simulations and an application to European agricultural data the flexibility of the proposed framework in analyzing efficiency is illustrated.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:258:y:2017:i:2:p:761-777
    DOI: 10.1016/j.ejor.2016.09.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221716307445
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2016.09.008?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. Keshvari, Abolfazl & Kuosmanen, Timo, 2013. "Stochastic non-convex envelopment of data: Applying isotonic regression to frontier estimation," European Journal of Operational Research, Elsevier, vol. 231(2), pages 481-491.
    2. B. E. Bravo‐Ureta & L. Rieger, 1990. "Alternative Production Frontier Methodologies And Dairy Farm Efficiency," Journal of Agricultural Economics, Wiley Blackwell, vol. 41(2), pages 215-226, May.
    3. Coelli, Tim J., 1995. "Recent Developments In Frontier Modelling And Efficiency Measurement," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 39(3), pages 1-27, December.
    4. Revoredo-Giha, Cesar & Milne, Catherine E. & Leat, Philip M.K. & Cho, Woong Je, 2009. "Efficiency of Scottish Farms: A Stochastic Cost Frontier Analysis," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 10(2), pages 1-19, July.
    5. Camilla Mastromarco & Léopold Simar, 2015. "Effect of FDI and Time on Catching Up: New Insights from a Conditional Nonparametric Frontier Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(5), pages 826-847, August.
    6. Kuosmanen, Timo & Saastamoinen, Antti & Sipiläinen, Timo, 2013. "What is the best practice for benchmark regulation of electricity distribution? Comparison of DEA, SFA and StoNED methods," Energy Policy, Elsevier, vol. 61(C), pages 740-750.
    7. Tzouvelekas, Vangelis & Pantzios, Christos J. & Fotopoulos, Christos, 2001. "Economic Efficiency in Organic Farming: Evidence from Cotton Farms in Viotia, Greece," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 33(1), pages 35-48, April.
    8. Lee, Chia-Yen & Johnson, Andrew L. & Moreno-Centeno, Erick & Kuosmanen, Timo, 2013. "A more efficient algorithm for Convex Nonparametric Least Squares," European Journal of Operational Research, Elsevier, vol. 227(2), pages 391-400.
    9. Tauer, Loren W. & Mishra, Ashok K., 2006. "Can the small dairy farm remain competitive in US agriculture?," Food Policy, Elsevier, vol. 31(5), pages 458-468, October.
    10. Ernest Reig‐Martínez & José A. Gómez‐Limón & Andrés J. Picazo‐Tadeo, 2011. "Ranking farms with a composite indicator of sustainability," Agricultural Economics, International Association of Agricultural Economists, vol. 42(5), pages 561-575, September.
    11. Francesco Vidoli & Giancarlo Ferrara, 2015. "Analyzing Italian citrus sector by semi-nonparametric frontier efficiency models," Empirical Economics, Springer, vol. 49(2), pages 641-658, September.
    12. Andrew Johnson & Timo Kuosmanen, 2011. "One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient, root-n consistent StoNEZD method," Journal of Productivity Analysis, Springer, vol. 36(2), pages 219-230, October.
    13. 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.
    14. Hani, Fritz & Braga, Francesco S. & Stampfli, Andreas & Keller, Thomas & Fischer, Matthew & Porsche, Hans, 2003. "RISE, a Tool for Holistic Sustainability Assessment at the Farm Level," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 6(4), pages 1-13.
    15. Kuosmanen, Timo, 2001. "DEA with efficiency classification preserving conditional convexity," European Journal of Operational Research, Elsevier, vol. 132(2), pages 326-342, July.
    16. Tone, Kaoru & Sahoo, Biresh K., 2004. "Degree of scale economies and congestion: A unified DEA approach," European Journal of Operational Research, Elsevier, vol. 158(3), pages 755-772, November.
    17. Timo Kuosmanen, 2008. "Representation theorem for convex nonparametric least squares," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 308-325, July.
    18. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    19. M. J. Farrell, 1959. "The Convexity Assumption in the Theory of Competitive Markets," Journal of Political Economy, University of Chicago Press, vol. 67, pages 377-377.
    20. 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.
    21. Khem Sharma & Pingsun Leung & Halina Zaleski, 1997. "Productive Efficiency of the Swine Industry in Hawaii: Stochastic Frontier vs. Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 8(4), pages 447-459, November.
    22. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    23. Giannis Karagiannis & Alexander Sarris, 2005. "Measuring and explaining scale efficiency with the parametric approach: the case of Greek tobacco growers," Agricultural Economics, International Association of Agricultural Economists, vol. 33(s3), pages 441-451, November.
    24. Heshmati, Almas & Kumbhakar, Subal C. & Hjalmarsson, Lennart, 1995. "Efficiency of the Swedish pork industry: A farm level study using rotating panel data 1976-1988," European Journal of Operational Research, Elsevier, vol. 80(3), pages 519-533, February.
    25. Cinzia Daraio & Léopold Simar, 2007. "Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach," Journal of Productivity Analysis, Springer, vol. 28(1), pages 13-32, October.
    26. Johansson, Helena, 2005. "Technical, Allocative, and Economic Efficiency in Swedish Dairy Farms: The Data Envelopment Analysis Versus the Stochastic Frontier Approach," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24478, European Association of Agricultural Economists.
    27. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    28. Sperlich, Stefan & Tjøstheim, Dag & Yang, Lijian, 2002. "Nonparametric Estimation And Testing Of Interaction In Additive Models," Econometric Theory, Cambridge University Press, vol. 18(2), pages 197-251, April.
    29. Muggeo, Vito M.R. & Ferrara, Giancarlo, 2008. "Fitting generalized linear models with unspecified link function: A P-spline approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2529-2537, January.
    30. Simon N. Wood, 2013. "On p-values for smooth components of an extended generalized additive model," Biometrika, Biometrika Trust, vol. 100(1), pages 221-228.
    31. 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.
    32. Timo Kuosmanen & Andrew L. Johnson, 2010. "Data Envelopment Analysis as Nonparametric Least-Squares Regression," Operations Research, INFORMS, vol. 58(1), pages 149-160, February.
    33. Rajiv D. Banker & Ram Natarajan, 2008. "Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis," Operations Research, INFORMS, vol. 56(1), pages 48-58, February.
    34. Tim Coelli & Sanzidur Rahman & Colin Thirtle, 2002. "Technical, Allocative, Cost and Scale Efficiencies in Bangladesh Rice Cultivation: A Non‐parametric Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 53(3), pages 607-626, November.
    35. A. M. Theodoridis & A. Psychoudakis, 2008. "Efficiency Measurement in Greek Dairy Farms: Stochastic Frontier vs. Data Envelopment Analysis," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 1(2), pages 53-67, December.
    36. Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 460-468, October.
    37. Kumbhakar, Subal C & Ghosh, Soumendra & McGuckin, J Thomas, 1991. "A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(3), pages 279-286, July.
    38. Griffin, Ronald C. & Montgomery, John M. & Rister, M. Edward, 1987. "Selecting Functional Form In Production Function Analysis," Western Journal of Agricultural Economics, Western Agricultural Economics Association, vol. 12(2), pages 1-12, December.
    39. Laure Latruffe, 2010. "Competitiveness, Productivity and Efficiency in the Agricultural and Agri-Food Sectors," OECD Food, Agriculture and Fisheries Papers 30, OECD Publishing.
    40. 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.
    41. Konstantinos Giannakas & Kien C. Tran & Vangelis Tzouvelekas, 2003. "On the choice of functional form in stochastic frontier modeling," Empirical Economics, Springer, vol. 28(1), pages 75-100, January.
    42. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
    43. Stasinopoulos, D. Mikis & Rigby, Robert A., 2007. "Generalized Additive Models for Location Scale and Shape (GAMLSS) in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 23(i07).
    44. 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.
    45. Tzouvelekas, Vangelis & Pantzios, Christos J. & Fotopoulos, Christos, 2002. "Empirical Evidence of Technical Efficiency Levels in Greek Organic and Conventional Farms," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 3(2), pages 1-12, August.
    46. Timo Kuosmanen & Mika Kortelainen, 2012. "Stochastic non-smooth envelopment of data: semi-parametric frontier estimation subject to shape constraints," Journal of Productivity Analysis, Springer, vol. 38(1), pages 11-28, August.
    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. 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.
    2. Tsionas, Mike G. & Mallick, Sushanta K., 2019. "A Bayesian semiparametric approach to stochastic frontiers and productivity," European Journal of Operational Research, Elsevier, vol. 274(1), pages 391-402.
    3. Mohammed, Sadick & Abdulai, Awudu, 2021. "Extension Participation and Improved Technology Adoption: Impact on Efficiency and Welfare of Farmers in Ghana," 2021 Conference, August 17-31, 2021, Virtual 315362, International Association of Agricultural Economists.
    4. Hohsuk Noh & Seong J. Yang, 2020. "Comparing Groups of Decision-Making Units in Efficiency Based on Semiparametric Regression," Mathematics, MDPI, vol. 8(2), pages 1-16, February.
    5. Berner, Anne & Lange, Steffen & Silbersdorff, Alexander, 2022. "Firm-level energy rebound effects and relative efficiency in the German manufacturing sector," Energy Economics, Elsevier, vol. 109(C).
    6. Hazarika, Gautam & Khraiche, Maroula & Kutlu, Levent, 2023. "Gender Equity in Labor Market Opportunities and Aggregate Technical Efficiency: A Case of Equity Promoting Efficiency," IZA Discussion Papers 16096, Institute of Labor Economics (IZA).
    7. Schmidt, Rouven & Kneib, Thomas, 2023. "Multivariate distributional stochastic frontier models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    8. Zainab Bibi & Dilawar Khan & Ihtisham ul Haq, 2021. "Technical and environmental efficiency of agriculture sector in South Asia: a stochastic frontier analysis approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 9260-9279, June.
    9. Benini, Giacomo & Cattani, Gilles, 2022. "Measuring the long run technical efficiency of offshore wind farms," Applied Energy, Elsevier, vol. 308(C).
    10. Wanke, Peter & Araujo, Claudia & Tan, Yong & Antunes, Jorge & Pimenta, Roberto, 2023. "Efficiency in university hospitals: A genetic optimized semi-parametric production function," Operations Research Perspectives, Elsevier, vol. 10(C).
    11. Francisco Germano Carvalho Lucio & Jhorland Ayala‐García, 2022. "Public sector efficiency in Brazil," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(6), pages 308-321, December.
    12. Dilawar Khan & Muhammad Nouman & Arif Ullah, 2023. "Assessing the impact of technological innovation on technically derived energy efficiency: a multivariate co-integration analysis of the agricultural sector in South Asia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(4), pages 3723-3745, April.
    13. Giancarlo Ferrara & Arianna Campagna & Vincenzo Atella, 2019. "Disentangling tax evasion from inefficiency in firms tax declaration: an integrated approach," CEIS Research Paper 468, Tor Vergata University, CEIS, revised 06 Sep 2019.
    14. Michael L. Polemis & Mike G. Tsionas, 2022. "Endogenous productivity: a new Bayesian perspective," Annals of Operations Research, Springer, vol. 318(1), pages 425-451, November.
    15. ferrara, giancarlo & campagna, arianna & bucci, valeria & atella, vincenzo, 2021. "Presumptive taxation and firms’ efficiency: an integrated approach for tax compliance analysis," MPRA Paper 111516, University Library of Munich, Germany.
    16. Lin, Winston T. & Chen, Yueh H. & Hung, TingShu, 2019. "A partial adjustment valuation approach with stochastic and dynamic speeds of partial adjustment to measuring and evaluating the business value of information technology," European Journal of Operational Research, Elsevier, vol. 272(2), pages 766-779.
    17. Federico Belotti & Giancarlo Ferrara, 2019. "Imposing monotonicity in stochastic frontier models: an iterative nonlinear least squares procedure," CEIS Research Paper 462, Tor Vergata University, CEIS, revised 29 Jan 2021.
    18. Dilawar Khan & Muhammad Nouman & József Popp & Muhammad Asif Khan & Faheem Ur Rehman & Judit Oláh, 2021. "Link between Technically Derived Energy Efficiency and Ecological Footprint: Empirical Evidence from the ASEAN Region," Energies, MDPI, vol. 14(13), pages 1-16, June.
    19. Liu, Fangmei & Li, Li & Ye, Bin & Qin, Quande, 2023. "A novel stochastic semi-parametric frontier-based three-stage DEA window model to evaluate China's industrial green economic efficiency," Energy Economics, Elsevier, vol. 119(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. Francesco Vidoli & Giancarlo Ferrara, 2015. "Analyzing Italian citrus sector by semi-nonparametric frontier efficiency models," Empirical Economics, Springer, vol. 49(2), pages 641-658, September.
    2. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Schmidt, Rouven & Kneib, Thomas, 2023. "Multivariate distributional stochastic frontier models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    8. Keshvari, Abolfazl & Kuosmanen, Timo, 2013. "Stochastic non-convex envelopment of data: Applying isotonic regression to frontier estimation," European Journal of Operational Research, Elsevier, vol. 231(2), pages 481-491.
    9. Federico Belotti & Giancarlo Ferrara, 2019. "Imposing monotonicity in stochastic frontier models: an iterative nonlinear least squares procedure," CEIS Research Paper 462, Tor Vergata University, CEIS, revised 29 Jan 2021.
    10. 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.
    11. 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.
    12. Mark Andor & Frederik Hesse, 2014. "The StoNED age: the departure into a new era of efficiency analysis? A monte carlo comparison of StoNED and the “oldies” (SFA and DEA)," Journal of Productivity Analysis, Springer, vol. 41(1), pages 85-109, February.
    13. Stefan Seifert, 2016. "Semi-Parametric Measures of Scale Characteristics of German Natural Gas-Fired Electricity Generation," Discussion Papers of DIW Berlin 1571, DIW Berlin, German Institute for Economic Research.
    14. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
    15. Dai, Xiaofeng, 2016. "Non-parametric efficiency estimation using Richardson–Lucy blind deconvolution," European Journal of Operational Research, Elsevier, vol. 248(2), pages 731-739.
    16. Liu, Fangmei & Li, Li & Ye, Bin & Qin, Quande, 2023. "A novel stochastic semi-parametric frontier-based three-stage DEA window model to evaluate China's industrial green economic efficiency," Energy Economics, Elsevier, vol. 119(C).
    17. Fabio Pammolli & Francesco Porcelli & Francesco Vidoli & Guido Borà, 2014. "La spesa sanitaria delle Regioni in Italia - Saniregio 3," Working Papers CERM 02-2014, Competitività, Regole, Mercati (CERM).
    18. Mark Andor & Frederik Hesse, "undated". "The StoNED age: The Departure Into a New Era of Efficiency Analysis? An MC study Comparing StoNED and the "Oldies" (SFA and DEA)," Working Papers 201285, Institute of Spatial and Housing Economics, Munster Universitary.
    19. Tran, Kien C. & Tsionas, Efthymios G., 2009. "Estimation of nonparametric inefficiency effects stochastic frontier models with an application to British manufacturing," Economic Modelling, Elsevier, vol. 26(5), pages 904-909, September.
    20. Christopher F. Parmeter & Valentin Zelenyuk, 2019. "Combining the Virtues of Stochastic Frontier and Data Envelopment Analysis," Operations Research, INFORMS, vol. 67(6), pages 1628-1658, November.

    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:ejores:v:258:y:2017:i:2:p:761-777. 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/eor .

    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.