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Inference in stochastic frontier analysis with dependent error terms

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  • El Mehdi, Rachida
  • Hafner, Christian

Abstract

Stochastic frontier analysis (SFA) is often used to estimate technical efficiency of entities such as firms, countries or municipalities. A potential dependence between the two components of the error term can be taken into account by a copula function. While estimation of the model is straightforward using the Corrected Ordinary Least Squares (COLS) and Maximum Likelihood (ML) methods, an open issue concerns the inference of the technical efficiencies. We propose a parametric bootstrap algorithm which is suitable for the dependence case. This allows us to estimate the efficiency percentile confidence intervals. We apply the model to the estimation of technical efficiencies of moroccan municipalities.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • El Mehdi, Rachida & Hafner, Christian, 2014. "Inference in stochastic frontier analysis with dependent error terms," LIDAM Reprints ISBA 2014028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2014028
    Note: In : Mathematics and Computers in Simulation, vol. 102, p. 104-116 (2014)
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    References listed on IDEAS

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    1. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    2. Christian Ritter & Léopold Simar, 1997. "Pitfalls of Normal-Gamma Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 8(2), pages 167-182, May.
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    6. Greene, William H., 1980. "Maximum likelihood estimation of econometric frontier functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 27-56, May.
    7. Leopold Simar & Paul Wilson, 2010. "Inferences from Cross-Sectional, Stochastic Frontier Models," Econometric Reviews, Taylor & Francis Journals, vol. 29(1), pages 62-98.
    8. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    9. Murray D. Smith, 2008. "Stochastic frontier models with dependent error components," Econometrics Journal, Royal Economic Society, vol. 11(1), pages 172-192, March.
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    Cited by:

    1. Alecos Papadopoulos, 2023. "The noise error component in stochastic frontier analysis," Empirical Economics, Springer, vol. 64(6), pages 2795-2829, June.
    2. Kexin Li & Jianxu Liu & Yuting Xue & Sanzidur Rahman & Songsak Sriboonchitta, 2022. "Consequences of Ignoring Dependent Error Components and Heterogeneity in a Stochastic Frontier Model: An Application to Rice Producers in Northern Thailand," Agriculture, MDPI, vol. 12(8), pages 1-17, July.
    3. Miao, Chenglin & Fang, Debin & Sun, Liyan & Luo, Qiaoling, 2017. "Natural resources utilization efficiency under the influence of green technological innovation," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 153-161.
    4. Alecos Papadopoulos & Christopher F. Parmeter & Subal C. Kumbhakar, 2021. "Modeling dependence in two-tier stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 56(2), pages 85-101, December.
    5. Silva, Thiago Christiano & Tabak, Benjamin Miranda & Cajueiro, Daniel Oliveira & Dias, Marina Villas Boas, 2018. "Adequacy of deterministic and parametric frontiers to analyze the efficiency of Indian commercial banks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1016-1025.
    6. Jianxu Liu & Mengjiao Wang & Ji Ma & Sanzidur Rahman & Songsak Sriboonchitta, 2020. "A Simultaneous Stochastic Frontier Model with Dependent Error Components and Dependent Composite Errors: An Application to Chinese Banking Industry," Mathematics, MDPI, vol. 8(2), pages 1-23, February.
    7. Emilio Gómez-Déniz & Nancy Dávila-Cárdenes & Alejandro Leiva-Arcas & María J. Martínez-Patiño, 2021. "Measuring Efficiency in the Summer Olympic Games Disciplines: The Case of the Spanish Athletes," Mathematics, MDPI, vol. 9(21), pages 1-15, October.
    8. Mamonov Mikhail E. & Parmeter Christopher F. & Prokhorov Artem B., 2022. "Dependence modeling in stochastic frontier analysis," Dependence Modeling, De Gruyter, vol. 10(1), pages 123-144, January.
    9. Schmidt, Rouven & Kneib, Thomas, 2023. "Multivariate distributional stochastic frontier models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).

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