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Categorical data in local maximum likelihood: theory and applications to productivity analysis

Author

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  • Park, Byeong U.
  • Simar, Leopold
  • Zelenyuk, Valentin

Abstract

In this paper we consider estimation of models popular in efficiency and productivity analysis (such as the stochastic frontier model, truncated regression model, etc.) via the local maximum likelihood method, generalizing this method here to allow for not only continuous but also discrete regressors. We provide asymptotic theory, some evidence from simulations, and illustrate the method with an empirical example. Our methodology and theory can also be adapted for other models where a likelihood of the unknown functions can be used to identify and estimate the underlying model. Simulation results indicate flexibility of the approach and good performances in various complex scenarios, even with moderate sample sizes. Copyright Springer Science+Business Media New York 2015
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Suggested Citation

  • Park, Byeong U. & Simar, Leopold & Zelenyuk, Valentin, 2015. "Categorical data in local maximum likelihood: theory and applications to productivity analysis," LIDAM Reprints ISBA 2015018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2015018
    Note: In : Journal of Productivity Analysis, vol. 43, no. 2, p. 199-214 (2015)
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    Cited by:

    1. Tsionas, Mike & Parmeter, Christopher F. & Zelenyuk, Valentin, 2023. "Bayesian Artificial Neural Networks for frontier efficiency analysis," Journal of Econometrics, Elsevier, vol. 236(2).
    2. Worthington, Andrew C. & Zelenyuk, Valentin, 2018. "Data envelopment analysis, truncated regression and double-bootstrap for panel data with application to Chinese bankingAuthor-Name: Du, Kai," European Journal of Operational Research, Elsevier, vol. 265(2), pages 748-764.
    3. Christopher F. Parmeter & Léopold Simar & Ingrid Van Keilegom & Valentin Zelenyuk, 2024. "Inference in the nonparametric stochastic frontier model," Econometric Reviews, Taylor & Francis Journals, vol. 43(7), pages 518-539, August.
    4. Valentin Zelenyuk & Zhichao Wang, 2023. "Random vs. Explained Inefficiency in Stochastic Frontier Analysis: The Case of Queensland Hospitals," CEPA Working Papers Series WP052023, School of Economics, University of Queensland, Australia.
    5. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    6. Kok Fong See & Shawna Grosskopf & Vivian Valdmanis & Valentin Zelenyuk, 2021. "What do we know from the vast literature on efficiency and productivity in healthcare? A Systematic Review and Bibliometric Analysis," CEPA Working Papers Series WP072021, School of Economics, University of Queensland, Australia.
    7. Christopher F. Parmeter & Valentin Zelenyuk, 2016. "A Bridge Too Far? The State of the Art in Combining the Virtues of Stochastic Frontier Analysis and Data Envelopement Analysis," Working Papers 2016-10, University of Miami, Department of Economics.
    8. repec:qld:uqcepa:167 is not listed on IDEAS
    9. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2022. "Efficiency Analysis with Stochastic Frontier Models Using Popular Statistical Softwares," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 129-171, Springer.
    10. 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.
    11. Taining Wang & Feng Yao & Subal C. Kumbhakar, 2024. "A flexible stochastic production frontier model with panel data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(4), pages 564-588, June.
    12. 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.
    13. Robin C. Sickles & Wonho Song & Valentin Zelenyuk, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," CEPA Working Papers Series WP082018, School of Economics, University of Queensland, Australia.
    14. Lopez Gomez, Daniel & Parmeter, Christopher F., 2020. "Smooth coefficient estimation of stochastic frontier models," Economics Letters, Elsevier, vol. 193(C).
    15. 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.
    16. Léopold Simar & Paul W. Wilson, 2023. "Nonparametric, Stochastic Frontier Models with Multiple Inputs and Outputs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1391-1403, October.
    17. Cristian Barra & Raffaele Lagravinese & Roberto Zotti, 2022. "Exploring hospital efficiency within and between Italian regions: new empirical evidence," Journal of Productivity Analysis, Springer, vol. 57(3), pages 269-284, June.
    18. Valentin Zelenyuk, 2019. "Data Envelopment Analysis and Business Analytics: The Big Data Challenges and Some Solutions," CEPA Working Papers Series WP072019, School of Economics, University of Queensland, Australia.
    19. Kelly D.T.Trinh & Valentin Zelenyuk, 2015. "Productivity Growth and Convergence: Revisiting Kumar and Russell (2002)," CEPA Working Papers Series WP112015, School of Economics, University of Queensland, Australia.
    20. Yao, Feng & Wang, Taining & Tian, Jinjing & Kumbhakar, Subal C., 2018. "Estimation of a smooth coefficient zero-inefficiency panel stochastic frontier model: A semiparametric approach," Economics Letters, Elsevier, vol. 166(C), pages 25-30.
    21. Quaranta, Anna Grazia & Raffoni, Anna & Visani, Franco, 2018. "A multidimensional approach to measuring bank branch efficiency," European Journal of Operational Research, Elsevier, vol. 266(2), pages 746-760.
    22. 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.

    More about this item

    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
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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