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

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  • Hadri, Kaddour
  • Guermat, Cherif
  • Whittaker, Julie

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

  • Hadri, Kaddour & Guermat, Cherif & Whittaker, Julie, 2003. "Estimating Farm Efficiency in the Presence of Double Heteroscedasticity Using Panel Data," Journal of Applied Economics, Universidad del CEMA, vol. 6(2), pages 1-14, November.
    • Handle: RePEc:ags:jaecon:43994
    DOI: 10.22004/ag.econ.43994
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    References listed on IDEAS

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    1. Kaddour Hadri, 1997. "A frontier approach to disequilibrium models," Applied Economics Letters, Taylor & Francis Journals, vol. 4(11), pages 699-701.
    2. 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.
    3. Hadri, Kaddour, 1999. "Estimation of a Doubly Heteroscedastic Stochastic Frontier Cost Function," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 359-363, July.
    4. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    5. Caudill, Steven B & Ford, Jon M & Gropper, Daniel M, 1995. "Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 105-111, January.
    6. Abadir, Karim M & Hadri, Kaddour, 2000. "Is More Information a Good Thing? Bias Nonmonotonicity in Stochastic Difference Equations," Bulletin of Economic Research, Wiley Blackwell, vol. 52(2), pages 91-100, April.
    7. K Hadri & J. Whittaker, 1999. "Efficiency, Environmental Contaminants and Farm Size: Testing for Links Using Stochastic Production Frontiers," Journal of Applied Economics, Taylor & Francis Journals, vol. 2(2), pages 337-356, November.
    8. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
    9. Karim Abadir & Kaddour Hadri, "undated". "Bias Nonmonotonicity in Stochastic Difference Equations," Discussion Papers 96/15, Department of Economics, University of York.
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    11. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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    3. 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.
    4. 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).
    5. Boris Bravo-Ureta & Daniel Solís & Víctor Moreira López & José Maripani & Abdourahmane Thiam & Teodoro Rivas, 2007. "Technical efficiency in farming: a meta-regression analysis," Journal of Productivity Analysis, Springer, vol. 27(1), pages 57-72, February.
    6. 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.
    7. Michael Zschille & Matthias Walter, 2012. "The performance of German water utilities: a (semi)-parametric analysis," Applied Economics, Taylor & Francis Journals, vol. 44(29), pages 3749-3764, October.
    8. 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.
    9. Jorge Galán & Helena Veiga & Michael Wiper, 2014. "Bayesian estimation of inefficiency heterogeneity in stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 42(1), pages 85-101, August.
    10. 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.
    11. 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.
    12. Nicholas Rada & David Schimmelpfennig, 2018. "Evaluating research and education performance in Indian agricultural development," Agricultural Economics, International Association of Agricultural Economists, vol. 49(3), pages 395-406, May.
    13. 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

    Farm Management; Research Methods/ Statistical Methods;

    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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