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Technical efficiency with state-contingent production frontiers using maximum entropy estimators

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  • Pedro Macedo
  • Elvira Silva
  • Manuel Scotto

Abstract

Although the theory of state-contingent production is well-established, the empirical implementation of this approach is still in an infancy stage. The possibility of finding a large number of states of nature, few observations per state and models affected by collinearity have led some researchers to claim the urgent need to develop robust estimation techniques. In this paper, we investigate the performance of some maximum entropy estimators to assess technical efficiency with state-contingent production frontiers. The methodological discussion and the simulation study provided in the paper reveal some of the potential of these estimators. Small mean squared error loss and small differences between the true and the estimated mean of technical efficiency show that the maximum entropy can be a powerful tool in the estimation of state-contingent production frontiers. Copyright Springer Science+Business Media, LLC 2014

Suggested Citation

  • Pedro Macedo & Elvira Silva & Manuel Scotto, 2014. "Technical efficiency with state-contingent production frontiers using maximum entropy estimators," Journal of Productivity Analysis, Springer, vol. 41(1), pages 131-140, February.
    • Handle: RePEc:kap:jproda:v:41:y:2014:i:1:p:131-140
    DOI: 10.1007/s11123-012-0314-y
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    References listed on IDEAS

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    Cited by:

    1. Elvira Silva & Pedro Macedo & Isabel Soares, 2019. "Maximum entropy: a stochastic frontier approach for electricity distribution regulation," Journal of Regulatory Economics, Springer, vol. 55(3), pages 237-257, June.
    2. Tsionas, Mike G., 2023. "Minimax regret priors for efficiency estimation," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1279-1285.
    3. Victor Moutinho & Mara Madaleno, 2021. "A Two-Stage DEA Model to Evaluate the Technical Eco-Efficiency Indicator in the EU Countries," IJERPH, MDPI, vol. 18(6), pages 1-21, March.
    4. Macedo, Pedro & Scotto, Manuel, 2014. "Cross-entropy estimation in technical efficiency analysis," Journal of Mathematical Economics, Elsevier, vol. 54(C), pages 124-130.
    5. Sansi Yang & C Richard Shumway, 2018. "Asset fixity under state-contingent production uncertainty," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(5), pages 831-856.
    6. Liu, Yunqiang & Zhu, Jialing & Li, Eldon Y. & Meng, Zhiyi & Song, Yan, 2020. "Environmental regulation, green technological innovation, and eco-efficiency: The case of Yangtze river economic belt in China," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    7. Victor MOUTINHO & Margarita ROBAINA & Pedro MACEDO, 2018. "Economic-environmental efficiency of European agriculture - a generalized maximum entropy approach," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 64(10), pages 423-435.

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    More about this item

    Keywords

    Maximum entropy; State-contingent production; Technical efficiency; C13; C15;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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