IDEAS home Printed from https://ideas.repec.org/p/ags/aaea14/169877.html
   My bibliography  Save this paper

A Comparison of Parametric and Nonparametric Estimation Methods for Cost Frontiers and Economic Measures

Author

Listed:
  • Parman, Bryon
  • Featherstone, Allen
  • Amanor-Boadu, Vincent

Abstract

This research examines the robustness of four different estimation approaches to evaluate their ability to estimate a “true” cost frontier and associated economic measures. The manuscript evaluates three parametric methods including a two-sided error system, OLS with only positive errors, and the stochastic frontier method. The fourth method is the nonparametric DEA method augmented to calculate multi-product and product-specific economies of scale. The robustness of the four estimation methods is examined using simulated data sets from two different distributions and two different observation quantity levels. The theoretical condition of curvature for the estimated cost functions was checked for the input price, and output quantity matrices. Calculation of the Eigenvalues revealed that all three parametric estimation methods violated curvature of either the price or quantity matrix, or both. Calculation of the estimated economic efficiency measures shows the parametric methods to be susceptible to distributional assumptions. However, the DEA method in all three simulations is fairly robust in estimating the “true” cost frontier and associated economic measures while maintaining curvature of the cost function.

Suggested Citation

  • Parman, Bryon & Featherstone, Allen & Amanor-Boadu, Vincent, 2014. "A Comparison of Parametric and Nonparametric Estimation Methods for Cost Frontiers and Economic Measures," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169877, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea14:169877
    DOI: 10.22004/ag.econ.169877
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/169877/files/Bryon%20Parman%20AAEA%20selecte%20Paper.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.169877?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
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jean-Paul Chavas & Bradford Barham & Jeremy Foltz & Kwansoo Kim, 2012. "Analysis and decomposition of scope economies: R&D at US research universities," Applied Economics, Taylor & Francis Journals, vol. 44(11), pages 1387-1404, April.
    2. 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.
    3. 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.
    4. Banker, Rajiv D & Maindiratta, Ajay, 1988. "Nonparametric Analysis of Technical and Allocative Efficiencies in Production," Econometrica, Econometric Society, vol. 56(6), pages 1315-1332, November.
    5. 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.
    6. Chavas, Jean-Paul & Aliber, Michael, 1993. "An Analysis Of Economic Efficiency In Agriculture: A Nonparametric Approach," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 18(01), pages 1-16, July.
    7. Lusk, Jayson L. & Featherstone, Allen M. & Marsh, Thomas L. & Abdulkadri, Abdullahi O., 1997. "Empirical properties of duality theory," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 46(01), pages 1-24.
    8. Mas-Colell, Andreu & Whinston, Michael D. & Green, Jerry R., 1995. "Microeconomic Theory," OUP Catalogue, Oxford University Press, number 9780195102680.
    9. Afriat, Sidney N, 1972. "Efficiency Estimation of Production Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(3), pages 568-598, October.
    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. Ben Amor, Tawfik & Mellah, Thuraya, 2023. "Cost efficiency of Tunisian water utility districts: Does heterogeneity matter?," Utilities Policy, Elsevier, vol. 84(C).
    2. Madhav Regmi & Allen M. Featherstone & Cortney A. Cowley & Mykel R. Taylor, 2021. "Big Banks versus Agricultural Banks: Has Too‐Big‐To‐Fail Regulation Affected Efficiency and Scale Economies Measures?," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1164-1178, May.
    3. Roman Neyter & Oleg Nivievskyi, 2023. "Effect of subsidies on farms' exit decision," Agribusiness, John Wiley & Sons, Ltd., vol. 39(4), pages 941-959, October.
    4. Noah J Miller & Jason S Bergtold & Allen M Featherstone, 2019. "Economic elasticities of input substitution using data envelopment analysis," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-15, August.
    5. Russell, Levi A. & Briggeman, Brian C. & Featherstone, Allen M., 2013. "Debt and Input Misallocation in Farm Supply and Marketing Cooperatives: A DEA Approach," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150445, Agricultural and Applied Economics Association.
    6. Matheus Koengkan & José Alberto Fuinhas & Emad Kazemzadeh & Fariba Osmani & Nooshin Karimi Alavijeh, 2022. "Measuring the economic efficiency performance in Latin American and Caribbean countries: An empirical evidence from stochastic production frontier and data envelopment analysis," International Economics, CEPII research center, issue 169, pages 43-54.
    7. Chia-Nan Wang & Minh Nhat Nguyen & Anh Luyen Le & Hector Tibo, 2020. "A DEA Resampling Past-Present-Future Comparative Analysis of the Food and Beverage Industry: The Case Study on Thailand vs. Vietnam," Mathematics, MDPI, vol. 8(7), pages 1-24, July.
    8. Kao, Chiang & Pang, Rui-Zhi & Liu, Shiang-Tai & Bai, Xue-Jie, 2021. "Optimal expansion paths for hospitals of different types: Viewpoint of scope economies and evidence from Chinese hospitals," European Journal of Operational Research, Elsevier, vol. 289(2), pages 628-638.
    9. 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.
    10. Rita, Rui & Marques, Vitor & Bárbara, Diogo & Chaves, Inês & Macedo, Pedro & Moutinho, Victor & Pereira, Mariana, 2023. "Crossing non-parametric and parametric techniques for measuring the efficiency: Evidence from 65 European electricity Distribution System Operators," Energy, Elsevier, vol. 283(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. 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.
    2. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    3. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    4. 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.
    5. Per J. Agrell & Mehdi Farsi & Massimo Filippini & Martin Koller, 2013. "Unobserved heterogeneous effects in the cost efficiency analysis of electricity distribution systems," Working Papers 0038, Swiss Economics.
    6. 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.
    7. Pillai N., Vijayamohanan & AM, Narayanan, 2019. "Energy Efficiency: A Sectoral Analysis for Kerala," MPRA Paper 101424, University Library of Munich, Germany.
    8. Macedo, Pedro & Scotto, Manuel, 2014. "Cross-entropy estimation in technical efficiency analysis," Journal of Mathematical Economics, Elsevier, vol. 54(C), pages 124-130.
    9. 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.
    10. Mike Tsionas & Valentin Zelenyuk, 2021. "Goodness-of-fit in Optimizing Models of Production: A Generalization with a Bayesian Perspective," CEPA Working Papers Series WP182021, School of Economics, University of Queensland, Australia.
    11. Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2019. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," European Journal of Operational Research, Elsevier, vol. 274(1), pages 240-252.
    12. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    13. Parman, Bryon & Featherstone, Allen & Coffey, Brian, 2015. "Is Efficiency Analysis All There Is With Data Envelopment Analysis," Working Papers 197532, Mississippi State University, Department of Agricultural Economics.
    14. Kanter, Christopher A. & Hueth, Brent & Gould, Brian W., 2013. "A Comparative Efficiency Analysis of Cooperative and Non-cooperative Dairy Manufacturing Firms," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150497, Agricultural and Applied Economics Association.
    15. 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.
    16. 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.
    17. Sickles, Robin C. & Hao, Jiaqi & Shang, Chenjun, 2015. "Panel Data and Productivity Measurement," Working Papers 15-018, Rice University, Department of Economics.
    18. Timo Kuosmanen & Andrew L. Johnson, 2010. "Data Envelopment Analysis as Nonparametric Least-Squares Regression," Operations Research, INFORMS, vol. 58(1), pages 149-160, February.
    19. José Solana‐Ibáñez & Manuel Caravaca‐Garratón, 2021. "Stakeholder engagement and corporate social reputation: The influence of exogenous factors on efficiency performance (stakeholder engagement and exogenous factors): Stakeholder engagement and exogenou," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 28(6), pages 1891-1905, November.
    20. Sickles, Robin C., 2005. "Panel estimators and the identification of firm-specific efficiency levels in parametric, semiparametric and nonparametric settings," Journal of Econometrics, Elsevier, vol. 126(2), pages 305-334, June.

    More about this item

    Keywords

    Agribusiness; Farm Management; Production Economics; Productivity Analysis;
    All these keywords.

    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:ags:aaea14:169877. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.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.