IDEAS home Printed from https://ideas.repec.org/a/dse/indecr/v39y2004i1p111-121.html
   My bibliography  Save this article

A Simple Statistical Test of Violation of the Weak Axiom of Cost Minimization

Author

Listed:
  • Subhash C. Ray

    (University of Connecticut, Storrs CT 06269-1063 USA.)

Abstract

A problem with a practical application of Varian's Weak Axiom of Cost Minimization is that an observed violation may be due to random variation in the output quantities produced by firms rather than due to inefficiency on the part of the firm. In this paper, unlike in Varian (1985), the output rather than the input quantities are treated as random and an alternative statistical test of the violation of WACM is proposed. We assume that there is no technical inefficiency and provide a test of the hypothesis that an observed violation of WACM is merely due to random variations in the output levels of the firms being compared. We suggest an intuitive approach for specifying a value of the variance of the noise term that is needed for the test. The paper includes an illustrative example utilizing a data set relating to a number of U.S. airlines.

Suggested Citation

  • Subhash C. Ray, 2004. "A Simple Statistical Test of Violation of the Weak Axiom of Cost Minimization," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 39(1), pages 111-121, January.
  • Handle: RePEc:dse:indecr:v:39:y:2004:i:1:p:111-121
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Ray,Subhash C., 2012. "Data Envelopment Analysis," Cambridge Books, Cambridge University Press, number 9781107405264.
    3. Varian, Hal R, 1984. "The Nonparametric Approach to Production Analysis," Econometrica, Econometric Society, vol. 52(3), pages 579-597, May.
    4. Douglas W. Caves & Laurits R. Christensen & Michael W. Tretheway, 1984. "Economies of Density versus Economies of Scale: Why Trunk and Local Service Airline Costs Differ," RAND Journal of Economics, The RAND Corporation, vol. 15(4), pages 471-489, Winter.
    5. 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.
    6. Varian, Hal R., 1985. "Non-parametric analysis of optimizing behavior with measurement error," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 445-458.
    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. W D A Bryant, 2009. "General Equilibrium:Theory and Evidence," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6875, December.
    2. Paul Oslington, 2012. "General Equilibrium: Theory and Evidence," The Economic Record, The Economic Society of Australia, vol. 88(282), pages 446-448, September.

    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. 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.
    2. 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.
    3. Subhash C. Ray, 2018. "Data Envelopment Analysis with Alternative Returns to Scale," Working papers 2018-20, University of Connecticut, Department of Economics.
    4. Mike G. Tsionas & Valentin Zelenyuk, 2022. "Testing for Optimization Behavior in Production when Data is with Measurement Errors: A Bayesian Approach," CEPA Working Papers Series WP012022, School of Economics, University of Queensland, Australia.
    5. Cherchye, L. & Post, G.T., 2001. "Methodological Advances in Dea," ERIM Report Series Research in Management ERS-2001-53-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    6. Jean-Paul Chavas & Kwansoo Kim, 2015. "Nonparametric analysis of technology and productivity under non-convexity: a neighborhood-based approach," Journal of Productivity Analysis, Springer, vol. 43(1), pages 59-74, February.
    7. H. Spencer Banzhaf & Yaqin Liu & Martin Smith & Frank Asche, 2019. "Non-Parametric Tests of the Tragedy of the Commons," NBER Working Papers 26398, National Bureau of Economic Research, Inc.
    8. Kuosmanen, Timo & Post, Thierry & Scholtes, Stefan, 2007. "Non-parametric tests of productive efficiency with errors-in-variables," Journal of Econometrics, Elsevier, vol. 136(1), pages 131-162, January.
    9. Leleu, Hervé, 2013. "Inner and outer approximations of technology: A shadow profit approach," Omega, Elsevier, vol. 41(5), pages 868-871.
    10. Ian Crawford & Bram De Rock, 2014. "Empirical Revealed Preference," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 503-524, August.
    11. Laurens Cherchye & Thomas Demuynck & Bram De Rock & Marijn Verschelde, 2018. "Nonparametric identification of unobserved technological heterogeneity in production," Working Paper Research 335, National Bank of Belgium.
    12. Gad Allon & Michael Beenstock & Steven Hackman & Ury Passy & Alexander Shapiro, 2007. "Nonparametric estimation of concave production technologies by entropic methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 795-816.
    13. C. Lovell & Shawna Grosskopf & Eduardo Ley & Jesús Pastor & Diego Prior & Philippe Eeckaut, 1994. "Linear programming approaches to the measurement and analysis of productive efficiency," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 2(2), pages 175-248, December.
    14. Laurens Cherchye & Thomas Demuynck & Bram De Rock & Marijn Verschelde, 2018. "Nonparametric Production Analysis with Unobserved Heterogeneity in Productivity," Working Papers ECARES 2018-25, ULB -- Universite Libre de Bruxelles.
    15. Jim Engle-Warnick & Natalia Mishagina, 2014. "Insensitivity to Prices in a Dictator Game," CIRANO Working Papers 2014s-19, CIRANO.
    16. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    17. James Andreoni & William Harbaugh, 2005. "Power Indicies for Revealed Preference Tests," Levine's Bibliography 784828000000000181, UCLA Department of Economics.
    18. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    19. Featherstone, Allen M. & Moghnieh, Ghassan A. & Goodwin, Barry K., 1995. "Farm-level nonparametric analysis of cost-minimization and profit-maximization behavior," Agricultural Economics, Blackwell, vol. 13(2), pages 109-117, November.
    20. Fare, Rolf & Grosskopf, Shawna, 1995. "Nonparametric tests of regularity, Farrell efficiency, and goodness-of-fit," Journal of Econometrics, Elsevier, vol. 69(2), pages 415-425, October.

    More about this item

    Keywords

    Non Parametric Analysis; Data Envelopment Analysis (DEA); Free Disposal Hull (FDH) Z-test;
    All these keywords.

    JEL classification:

    • D2 - Microeconomics - - Production and Organizations
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

    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:dse:indecr:v:39:y:2004:i:1:p:111-121. 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: Pami Dua (email available below). General contact details of provider: https://edirc.repec.org/data/deudein.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.