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Inference in stochastic frontier analysis with dependent error terms

Author

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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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    Cited by:

    1. Alecos Papadopoulos, 2024. "The noise error component in stochastic frontier analysis," Advanced Studies in Theoretical and Applied Econometrics, in: Subal C. Kumbhakar & Robin C. Sickles & Hung-Jen Wang (ed.), Advances in Applied Econometrics, pages 333-367, Springer.
    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. 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.
    4. Alecos Papadopoulos & Christopher F. Parmeter, 2024. "The wrong skewness problem in stochastic frontier analysis: a review," Journal of Productivity Analysis, Springer, vol. 61(2), pages 121-134, April.
    5. 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.
    6. Yin, Lei & Du, Shanxing & Chen, Ge, 2024. "The influence of the bank–firm relationship on enterprises’ technological innovation efficiency: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1583-1600.
    7. 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.
    8. Rouven E. Haschka, 2024. "“Wrong” skewness and endogenous regressors in stochastic frontier models: an instrument-free copula approach with an application to estimate firm efficiency in Vietnam," Journal of Productivity Analysis, Springer, vol. 62(1), pages 71-90, August.
    9. 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.
    10. Mohd Irfan & Bamadev Mahapatra & Muhammad Shahbaz, 2024. "Energy efficiency in the Indian transportation sector: effect on carbon emissions," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(3), pages 6653-6676, March.
    11. de Oliveira, Luís Filipe Azevedo & Baião, Fernanda Araujo & Oliveira, Fernando Luiz Cyrino & Peres, Igor Tona, 2025. "Determinants of primary healthcare efficiency in the Brazilian Legal Amazon: A hybrid approach using Data Envelopment Analysis and Tobit regression," Socio-Economic Planning Sciences, Elsevier, vol. 101(C).
    12. 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.
    13. Schmidt, Rouven & Kneib, Thomas, 2023. "Multivariate distributional stochastic frontier models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    14. 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.

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