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A zero inefficiency stochastic frontier model

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  • Kumbhakar, Subal C.
  • Parmeter, Christopher F.
  • Tsionas, Efthymios G.

Abstract

Traditional stochastic frontier models impose inefficient behavior on all firms in the sample of interest. If the data under investigation represent a mixture of both fully efficient and inefficient firms then off-the-shelf frontier models are statistically inadequate. We introduce the zero inefficiency stochastic frontier model which can accommodate the presence of both efficient and inefficient firms in the sample. We derive the corresponding log-likelihood function, conditional mean of inefficiency, to estimate observation-specific inefficiency and discuss testing for the presence of fully efficient firms. We provide both simulated evidence as well as an empirical example which demonstrates the applicability of the proposed method.

Suggested Citation

  • Kumbhakar, Subal C. & Parmeter, Christopher F. & Tsionas, Efthymios G., 2013. "A zero inefficiency stochastic frontier model," Journal of Econometrics, Elsevier, vol. 172(1), pages 66-76.
  • Handle: RePEc:eee:econom:v:172:y:2013:i:1:p:66-76
    DOI: 10.1016/j.jeconom.2012.08.021
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Sickles, Robin C. & Hao, Jiaqi & Shang, Chenjun, 2015. "Panel Data and Productivity Measurement," Working Papers 15-018, Rice University, Department of Economics.
    2. William C. Horrace & Christopher F. Parmeter, 2014. "A Laplace Stochastic Frontier Model," Center for Policy Research Working Papers 166, Center for Policy Research, Maxwell School, Syracuse University.
    3. Seunghwa Rho & Peter Schmidt, 2015. "Are all firms inefficient?," Journal of Productivity Analysis, Springer, vol. 43(3), pages 327-349, June.
    4. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2016. "Efficiency and environmental factors in the US electricity transmission industry," Energy Economics, Elsevier, vol. 55(C), pages 234-246.
    5. Tran, Kien C. & Tsionas, Mike G., 2016. "On the estimation of zero-inefficiency stochastic frontier models with endogenous regressors," Economics Letters, Elsevier, vol. 147(C), pages 19-22.
    6. repec:aen:journl:ej38-4-orea is not listed on IDEAS
    7. Tran, Kien C. & Tsionas, Mike G., 2016. "Zero-inefficiency stochastic frontier models with varying mixing proportion: A semiparametric approach," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1113-1123.
    8. Huang, Tai-Hsin & Chiang, Dien-Lin & Lin, Chung-I, 2017. "A new approach to estimating a profit frontier using the censored stochastic frontier model," The North American Journal of Economics and Finance, Elsevier, vol. 39(C), pages 68-77.
    9. Orea, Luis & Jamasb, Tooraj, 2014. "Identifying efficient regulated firms with unobserved technological heterogeneity: A nested latent class approach to Norwegian electricity distribution networks," Efficiency Series Papers 2014/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    10. repec:eee:glofin:v:35:y:2018:i:c:p:58-71 is not listed on IDEAS
    11. Drivas, Kyriakos & Economidou, Claire & Tsionas, Efthymios G., 2014. "A Poisson Stochastic Frontier Model with Finite Mixture Structure," MPRA Paper 57485, University Library of Munich, Germany.
    12. Kutlu, Levent, 2017. "A constrained state space approach for estimating firm efficiency," Economics Letters, Elsevier, vol. 152(C), pages 54-56.
    13. repec:eee:ecolet:v:166:y:2018:i:c:p:25-30 is not listed on IDEAS

    More about this item

    Keywords

    Full efficiency; Zero-inefficiency; Mixture; Banking;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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