IDEAS home Printed from https://ideas.repec.org/a/bes/jnlbes/v15y1997i4p445-51.html
   My bibliography  Save this article

A Measure of Production Performance

Author

Listed:
  • Kokic, Philip, et al

Abstract

Suppose there are data available on the value of business output, as measured by a single variable y and the values of the corresponding inputs x, with the relationship between y and x determined by an appropriately chosen production function. It is shown how M-quantile regression methods can be used to construct a performance measure that allows a meaningful comparison of the production performance of the businesses. The method is illustrated using survey data collected from farm businesses in the pastoral zone of New South Wales and southern Queensland between 1978 and 1987. Coauthors are Ray Chambers, Jens Breckling, and Steve Beare.

Suggested Citation

  • Kokic, Philip, et al, 1997. "A Measure of Production Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 445-451, October.
    • Handle: RePEc:bes:jnlbes:v:15:y:1997:i:4:p:445-51
    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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cartone, Alfredo & Di Battista, Luca & Postiglione, Paolo, 2024. "A new approach for measuring poverty or social exclusion reduction in European NUTS 2 regions," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    2. Marchetti, Stefano & Tzavidis, Nikos & Pratesi, Monica, 2012. "Non-parametric bootstrap mean squared error estimation for M-quantile estimators of small area averages, quantiles and poverty indicators," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2889-2902.
    3. De Novellis, G. & Musile Tanzi, P. & Ranalli, M.G. & Stanghellini, E., 2024. "Leveraged finance exposure in the banking system: Systemic risk and interconnectedness," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
    4. Daouia, Abdelaati & Padoan, Simone A. & Stupfler, Gilles, 2023. "Extreme expectile estimation for short-tailed data, with an application to market risk assessment," TSE Working Papers 23-1414, Toulouse School of Economics (TSE), revised May 2024.
    5. Giulia Romano & Nicola Salvati & Andrea Guerrini, 2014. "Factors Affecting Water Utility Companies’ Decision to Promote the Reduction of Household Water Consumption," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5491-5505, December.
    6. Vinciotti Veronica & Yu Keming, 2009. "M-quantile Regression Analysis of Temporal Gene Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-22, September.
    7. Merlo, Luca & Petrella, Lea & Salvati, Nicola & Tzavidis, Nikos, 2022. "Marginal M-quantile regression for multivariate dependent data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    8. N. Salvati & N. Tzavidis & M. Pratesi & R. Chambers, 2012. "Small area estimation via M-quantile geographically weighted regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 1-28, March.
    9. Cartone, Alfredo & Postiglione, Paolo & Hewings, Geoffrey J.D., 2021. "Does economic convergence hold? A spatial quantile analysis on European regions," Economic Modelling, Elsevier, vol. 95(C), pages 408-417.
    10. Francesco Schirripa Spagnolo & Nicola Salvati & Antonella D’Agostino & Ides Nicaise, 2020. "The use of sampling weights in M‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 991-1012, August.
    11. Stefano Marchetti & Caterina Giusti & Nicola Salvati & Monica Pratesi, 2017. "Small area estimation based on M-quantile models in presence of outliers in auxiliary variables," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 531-555, November.
    12. repec:hal:journl:hal-04672516 is not listed on IDEAS
    13. Breckling, Jens & Kokic, Philip & Lübke, Oliver, 2001. "A note on multivariate M-quantiles," Statistics & Probability Letters, Elsevier, vol. 55(1), pages 39-44, November.

    More about this item

    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:bes:jnlbes:v:15:y:1997:i:4:p:445-51. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Christopher F. Baum (email available below). General contact details of provider: http://www.amstat.org/publications/jbes/index.cfm?fuseaction=main .

    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.