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Estimating Farm Efficiency in the Presence of Double Heteroscedasticity Using Panel Data

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Abstract

The accuracy of technical efficiency measures is important given the interest in such measures in policy discussions. In recent years the use of stochastic frontiers has become : popular for estimating technical inefficiency, but estimated inefficiencies are sensitive to : specification errors. One source of such errors is heteroscedasticity. This paper addresses : this issue by extending the Hadri (1999) correction for heteroscedasticity to stochastic : production frontiers and to panel data. It is argued that heteroscedasticity within an estimation : can have a significant effect on results, and that correcting for heteroscedasticity yields : more accurate measures of technical inefficiency. Using panel data on cereal farms, it is : found that the usual technical efficiency measures used in stochastic production frontiers : are significantly sensitive to the extended correction for heteroscedasticity. :

Suggested Citation

  • Kaddour Hadri & Cherif Guermat & Julie Whittaker, 2003. "Estimating Farm Efficiency in the Presence of Double Heteroscedasticity Using Panel Data," Journal of Applied Economics, Universidad del CEMA, vol. 6, pages 255-268, November.
    • Handle: RePEc:cem:jaecon:v:6:y:2003:n:2:p:255-268
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    1. Kaddour Hadri & Julie Whittaker, 1999. "Efficiency, Environmental Contaminants and Farm Size: Testing for Links Using Stochastic Production Frontiers," Journal of Applied Economics, Universidad del CEMA, vol. 2, pages 337-356, November.
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    Cited by:

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    5. Lukáš Čechura & Heinrich Hockmann, 2017. "Heterogeneity in Production Structures and Efficiency: An Analysis of the Czech Food Processing Industry," Pacific Economic Review, Wiley Blackwell, vol. 22(4), pages 702-719, October.
    6. Maria Nieswand & Matthias Walter, 2010. "Cost Efficiency and Subsidization in German Local Public Bus Transit," Discussion Papers of DIW Berlin 1071, DIW Berlin, German Institute for Economic Research.
    7. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    8. Xu, Baochang & Li, Sihui & Afzal, Ayesha & Mirza, Nawazish & Zhang, Meng, 2022. "The impact of financial development on environmental sustainability: A European perspective," Resources Policy, Elsevier, vol. 78(C).
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    10. Cechura, Lukas & Hockmann, Heinrich, 2011. "Efficiency and Heterogeneity in Czech Food Processing Industry," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114314, European Association of Agricultural Economists.
    11. Sabrina Auci & Donatella Vignani, 2020. "Climate variability and agriculture in Italy: a stochastic frontier analysis at the regional level," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 37(2), pages 381-409, July.
    12. Shinji Yane & Sanford Berg, 2013. "Sensitivity analysis of efficiency rankings to distributional assumptions: applications to Japanese water utilities," Applied Economics, Taylor & Francis Journals, vol. 45(17), pages 2337-2348, June.
    13. Salem Gheit, 2022. "A Stochastic Frontier Analysis of the Human Capital Effects on the Manufacturing Industries’ Technical Efficiency in the United States," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 8(3), pages 215-238, July.
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    15. Saldias, Rodrigo & von Cramon-Taubadel, Stephan, 2012. "Access to credit and the determinants of technical inefficiency among specialized small farmers in Chile," DARE Discussion Papers 1211, Georg-August University of Göttingen, Department of Agricultural Economics and Rural Development (DARE).

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    More about this item

    Keywords

    stochastic frontier production; heteroscedasticity; technical efficiency; panel data :;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

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