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On estimating efficiency effects in a stochastic frontier model

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  • Paul, Satya
  • Shankar, Sriram

Abstract

This paper proposes a technical efficiency effects model within the framework of stochastic production frontier. The efficiency effects are specified by a standard normal cumulative distribution function of exogenous variables which ensures the efficiency scores to lie in a unit interval. This specification eschews one-sided error term present in almost all the existing inefficiency effects models. The efficiency scores are obtained directly once the parameters of the model are estimated. An empirical exercise based on the widely used Philippines rice farming data illustrates the simplicity and usefulness of the proposed model.

Suggested Citation

  • Paul, Satya & Shankar, Sriram, 2018. "On estimating efficiency effects in a stochastic frontier model," European Journal of Operational Research, Elsevier, vol. 271(2), pages 769-774.
    • Handle: RePEc:eee:ejores:v:271:y:2018:i:2:p:769-774
    DOI: 10.1016/j.ejor.2018.05.052
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    Cited by:

    1. Satya Paul & Sriram Shankar, 2020. "Estimating efficiency effects in a panel data stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 53(2), pages 163-180, April.
    2. Tsionas, Mike & Parmeter, Christopher F. & Zelenyuk, Valentin, 2023. "Bayesian Artificial Neural Networks for frontier efficiency analysis," Journal of Econometrics, Elsevier, vol. 236(2).
    3. Caroline Khan & Mike G. Tsionas, 2021. "Constraints in models of production and cost via slack-based measures," Empirical Economics, Springer, vol. 61(6), pages 3347-3374, December.
    4. K Hervé Dakpo & Laure Latruffe & Yann Desjeux & Philippe Jeanneaux, 2021. "Latent Class Modelling for a Robust Assessment of Productivity: Application to French Grazing Livestock Farms," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(3), pages 760-781, September.
    5. Paul, Satya & Shankar, Sriram, 2022. "Regulatory reforms and the efficiency and productivity growth in electricity generation in OECD countries," Energy Economics, Elsevier, vol. 108(C).
    6. Delis, Manthos D. & Iosifidi, Maria & Tsionas, Mike, 2020. "Management estimation in banking," European Journal of Operational Research, Elsevier, vol. 284(1), pages 355-372.
    7. Kumbhakar, Subal C. & Tsionas, Mike G., 2020. "On the estimation of technical and allocative efficiency in a panel stochastic production frontier system model: Some new formulations and generalizations," European Journal of Operational Research, Elsevier, vol. 287(2), pages 762-775.
    8. Tsionas, Mike G. & Mamatzakis, Emmanuel, 2019. "Further results on estimating inefficiency effects in stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 275(3), pages 1157-1164.
    9. Skevas, Ioannis, 2020. "Inference in the spatial autoregressive efficiency model with an application to Dutch dairy farms," European Journal of Operational Research, Elsevier, vol. 283(1), pages 356-364.
    10. Paul, Satya & Shankar, Sriram, 2018. "Modelling Efficiency Effects in a True Fixed Effects Stochastic Frontier," MPRA Paper 87437, University Library of Munich, Germany.
    11. Mike Tsionas & Valentin Zelenyuk, 2021. "Goodness-of-fit in Optimizing Models of Production: A Generalization with a Bayesian Perspective," CEPA Working Papers Series WP182021, School of Economics, University of Queensland, Australia.
    12. Tsionas, Mike G., 2023. "Combining data envelopment analysis and stochastic frontiers via a LASSO prior," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1158-1166.
    13. Arisara Romyen & Chonrada Nunti & Paramin Neranon, 2023. "Trade efficiency under FTA for Thailand’s agricultural exports: copula-based gravity stochastic frontier model," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 12(1), pages 1-17, December.
    14. Tsionas, Mike G., 2023. "Bayesian learning in performance. Is there any?," European Journal of Operational Research, Elsevier, vol. 311(1), pages 263-282.
    15. Mike Tsionas & Christopher F. Parmeter & Valentin Zelenyuk, 2021. "Bridging the Divide? Bayesian Artificial Neural Networks for Frontier Efficiency Analysis," CEPA Working Papers Series WP082021, School of Economics, University of Queensland, Australia.

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    More about this item

    Keywords

    Stochastic frontier; Non-linear least squares; Standard normal cumulative distribution function; Technical efficiency;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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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