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Pseudolikelihood estimation of the stochastic frontier model

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  • Mark Andor
  • Christopher Parmeter

Abstract

Stochastic frontier analysis is a popular tool to assess firm performance. Almost universally it has been applied using maximum likelihood (ML) estimation. An alternative approach, pseudolikelihood (PL) estimation, which decouples estimation of the error component structure and the production frontier, has been adopted in both the non-parametric and panel data settings. To date, no formal comparison has yet to be conducted comparing these methods in a standard, parametric cross-sectional framework. We produce a comparison of these two competing methods using Monte Carlo simulations. Our results indicate that PL estimation enjoys almost identical performance to ML estimation across a range of scenarios and performance metrics, and for certain metrics, outperforms ML estimation when the distribution of inefficiency is incorrectly specified.

Suggested Citation

  • Mark Andor & Christopher Parmeter, 2017. "Pseudolikelihood estimation of the stochastic frontier model," Applied Economics, Taylor & Francis Journals, vol. 49(55), pages 5651-5661, November.
    • Handle: RePEc:taf:applec:v:49:y:2017:i:55:p:5651-5661
    DOI: 10.1080/00036846.2017.1324611
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    1. Recommended Reading for October
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2017-10-04 23:08:00

    Citations

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    Cited by:

    1. Kumbhakar, Subal C. & Peresetsky, Anatoly & Shchetynin, Yevgenii & Zaytsev, Alexey, 2020. "Technical efficiency and inefficiency: Reassurance of standard SFA models and a misspecification problem," MPRA Paper 102797, University Library of Munich, Germany.
    2. Mark A. Andor & David H. Bernstein & Stephan Sommer, 2021. "Determining the efficiency of residential electricity consumption," Empirical Economics, Springer, vol. 60(6), pages 2897-2923, June.
    3. Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2019. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," European Journal of Operational Research, Elsevier, vol. 274(1), pages 240-252.
    4. Ahn, Heinz & Clermont, Marcel & Langner, Julia, 2023. "Comparative performance analysis of frontier-based efficiency measurement methods – A Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 307(1), pages 294-312.
    5. José Luis Preciado Arreola & Daisuke Yagi & Andrew L. Johnson, 2020. "Insights from machine learning for evaluating production function estimators on manufacturing survey data," Journal of Productivity Analysis, Springer, vol. 53(2), pages 181-225, April.
    6. 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.
    7. Julia Schaefer & Marcel Clermont, 2018. "Stochastic non-smooth envelopment of data for multi-dimensional output," Journal of Productivity Analysis, Springer, vol. 50(3), pages 139-154, December.
    8. Juan Agar & William C. Horrace & Christopher F. Parmeter, 2022. "Overcapacity in Gulf of Mexico reef fish IFQ fisheries: 12 years after the adoption of IFQs," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 82(2), pages 483-506, June.
    9. 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.

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • D2 - Microeconomics - - Production and Organizations

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