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Nonparametric estimation of the determinants of inefficiency

Author

Listed:
  • Christopher F. Parmeter

    (University of Miami)

  • Hung-Jen Wang

    (National Taiwan University
    Academia Sinica)

  • Subal C. Kumbhakar

    (State University of New York at Binghamton
    University of Stavanger Business School)

Abstract

We consider the benchmark stochastic frontier model where inefficiency is directly influenced by observable determinants. In this setting, we estimate the stochastic frontier and the conditional mean of inefficiency without imposing any distributional assumptions. To do so we cast this model in the partly linear regression framework for the conditional mean. We provide a test of correct parametric specification of the scaling function. An empirical example is also provided to illustrate the practical value of the methods described here.

Suggested Citation

  • Christopher F. Parmeter & Hung-Jen Wang & Subal C. Kumbhakar, 2017. "Nonparametric estimation of the determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 47(3), pages 205-221, June.
    • Handle: RePEc:kap:jproda:v:47:y:2017:i:3:d:10.1007_s11123-016-0479-x
    DOI: 10.1007/s11123-016-0479-x
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    3. Luis Orea & Jevgenijs Steinbuks, 2018. "Estimating Market Power In Homogenous Product Markets Using A Composed Error Model: Application To The California Electricity Market," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 1296-1321, April.
    4. Deng, Yaguo, 2024. "A Bayesian semi-parametric approach to stochastic frontier models with inefficiency heterogeneity," DES - Working Papers. Statistics and Econometrics. WS 43837, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Tadesse Getacher Engida & Christopher F. Parmeter & Xudong Rao & Alfons G.J.M. Oude Lansink, 2022. "Investment Inefficiency and Corporate Social Responsibility," Journal of Productivity Analysis, Springer, vol. 58(1), pages 95-108, August.
    6. Paul, Satya & Shankar, Sriram, 2018. "Modelling Efficiency Effects in a True Fixed Effects Stochastic Frontier," MPRA Paper 87437, University Library of Munich, Germany.
    7. Christopher F. Parmeter & Valentin Zelenyuk, 2019. "Combining the Virtues of Stochastic Frontier and Data Envelopment Analysis," Operations Research, INFORMS, vol. 67(6), pages 1628-1658, November.
    8. Christopher F. Parmeter & Alan T. K. Wan & Xinyu Zhang, 2019. "Model averaging estimators for the stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 51(2), pages 91-103, June.
    9. Jun Cai & William C. Horrace & Christopher F. Parmeter, 2024. "Penalized sieve estimation of zero‐inefficiency stochastic frontiers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 41-65, January.
    10. Václava Pánková, 2019. "Are efficient firms motivated to invest? Evidence of manufacture of motor vehicles in Czech Republic," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 29(3), pages 67-76.
    11. Christopher F. Parmeter, 2018. "Estimation of the two-tiered stochastic frontier model with the scaling property," Journal of Productivity Analysis, Springer, vol. 49(1), pages 37-47, February.
    12. Zangin Zeebari & Kristofer Månsson & Pär Sjölander & Magnus Söderberg, 2023. "Regularized conditional estimators of unit inefficiency in stochastic frontier analysis, with application to electricity distribution market," Journal of Productivity Analysis, Springer, vol. 59(1), pages 79-97, February.
    13. Tsionas, Mike G., 2021. "Optimal combinations of stochastic frontier and data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 294(2), pages 790-800.
    14. 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.
    15. Alessandro Bonanno & Francesco Bimbo & Marco Costanigro & Alfons Oude Lansink & Rosaria Viscecchia, 2019. "Credence attributes and the quest for a higher price – a hedonic stochastic frontier approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 46(2), pages 163-192.
    16. 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.
    17. Cheng, Ming-Yen & Wang, Shouxia & Xia, Lucy & Zhang, Xibin, 2024. "Testing specification of distribution in stochastic frontier analysis," Journal of Econometrics, Elsevier, vol. 239(2).
    18. Fan Zhang & Joshua Hall & Feng Yao, 2018. "Does Economic Freedom Affect The Production Frontier? A Semiparametric Approach With Panel Data," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 1380-1395, April.
    19. Giovanni Forchini & Raoul Theler, 2023. "Semi-parametric modelling of inefficiencies in stochastic frontier analysis," Journal of Productivity Analysis, Springer, vol. 59(2), pages 135-152, April.
    20. Zhou, Jianhua & Parmeter, Christopher F. & Kumbhakar, Subal C., 2020. "Nonparametric estimation of the determinants of inefficiency in the presence of firm heterogeneity," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1142-1152.
    21. Centorrino, Samuele & Parmeter, Christopher F., 2024. "Nonparametric estimation of stochastic frontier models with weak separability," Journal of Econometrics, Elsevier, vol. 238(2).
    22. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Proxy variable estimation of productivity and efficiency," Southern Economic Journal, John Wiley & Sons, vol. 89(3), pages 885-923, January.

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    More about this item

    Keywords

    Partly linear; Heteroskedasticity; Kernel; Bandwidth;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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