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A Measure of Production Performance

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  • 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
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    Citations

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    Cited by:

    1. 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.
    2. 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).
    3. 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.
    4. 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 1-20, September.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Breckling, Jens & Kokic, Philip & Lübke, Oliver, 2001. "A note on multivariate M-quantiles," Statistics & Probability Letters, Elsevier, vol. 55(1), pages 39-44, November.

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