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Inference in the nonparametric stochastic frontier model

Author

Listed:
  • Parmeter, Christopher F.

    (University of Miami)

  • Simar, Léopold

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Van Keilegom, Ingrid

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Zelenyuk, Valentin

    (The University of Queensland)

Abstract

This article is the first in the literature to discuss in detail how to conduct various types of inference in the stochastic frontier model when it is estimated using nonparametric methods. We discuss a general and versatile inferential technique that allows for a range of practical hypotheses of interest to be tested. We also discuss several challenges that currently exist in this framework in an effort to alert researchers to potential pitfalls. Namely, it appears that when one wishes to estimate a stochastic frontier in a fully nonparametric framework, separability between inputs and determinants of inefficiency is an essential ingredient for the correct empirical size of a test. We showcase the performance of the test with a variety of Monte Carlo simulations.

Suggested Citation

  • Parmeter, Christopher F. & Simar, Léopold & Van Keilegom, Ingrid & Zelenyuk, Valentin, 2024. "Inference in the nonparametric stochastic frontier model," LIDAM Reprints ISBA 2024013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2024013
    DOI: https://doi.org/10.1080/07474938.2024.2339193
    Note: In: Econometric Reviews, 2024, vol. 43(7), p. 518-539
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    References listed on IDEAS

    as
    1. Chen, Yi-Yi & Schmidt, Peter & Wang, Hung-Jen, 2014. "Consistent estimation of the fixed effects stochastic frontier model," Journal of Econometrics, Elsevier, vol. 181(2), pages 65-76.
    2. 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.
    3. Léopold Simar & Ingrid Keilegom & Valentin Zelenyuk, 2017. "Nonparametric least squares methods for stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 47(3), pages 189-204, June.
    4. Orea, Luis & Álvarez, Inmaculada C., 2019. "A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms," Journal of Econometrics, Elsevier, vol. 213(2), pages 556-577.
    5. Kumbhakar, Subal C. & Park, Byeong U. & Simar, Leopold & Tsionas, Efthymios G., 2007. "Nonparametric stochastic frontiers: A local maximum likelihood approach," Journal of Econometrics, Elsevier, vol. 137(1), pages 1-27, March.
    6. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, October.
    7. Christian M. Hafner & Hans Manner & Léopold Simar, 2018. "The “wrong skewness” problem in stochastic frontier models: A new approach," Econometric Reviews, Taylor & Francis Journals, vol. 37(4), pages 380-400, April.
    8. John Xu Zheng, 1996. "A consistent test of functional form via nonparametric estimation techniques," Journal of Econometrics, Elsevier, vol. 75(2), pages 263-289, December.
    9. William C. Horrace & Ian A. Wright, 2020. "Stationary Points for Parametric Stochastic Frontier Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 516-526, July.
    10. Henderson,Daniel J. & Parmeter,Christopher F., 2015. "Applied Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521279680, October.
    11. Wang, Hung-Jen & Ho, Chia-Wen, 2010. "Estimating fixed-effect panel stochastic frontier models by model transformation," Journal of Econometrics, Elsevier, vol. 157(2), pages 286-296, August.
    12. Olson, Jerome A. & Schmidt, Peter & Waldman, Donald M., 1980. "A Monte Carlo study of estimators of stochastic frontier production functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 67-82, May.
    13. Glass, Anthony J. & Kenjegalieva, Karligash & Sickles, Robin C., 2016. "A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers," Journal of Econometrics, Elsevier, vol. 190(2), pages 289-300.
    14. Byeong Park & Léopold Simar & Valentin Zelenyuk, 2015. "Categorical data in local maximum likelihood: theory and applications to productivity analysis," Journal of Productivity Analysis, Springer, vol. 43(2), pages 199-214, April.
    15. Carlos Martins-Filho & Feng Yao, 2015. "Semiparametric Stochastic Frontier Estimation via Profile Likelihood," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 413-451, April.
    16. Kutlu, Levent & Tran, Kien C. & Tsionas, Mike G., 2019. "A time-varying true individual effects model with endogenous regressors," Journal of Econometrics, Elsevier, vol. 211(2), pages 539-559.
    17. Kneip, Alois & Simar, Léopold & Van Keilegom, Ingrid, 2015. "Frontier estimation in the presence of measurement error with unknown variance," Journal of Econometrics, Elsevier, vol. 184(2), pages 379-393.
    18. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    19. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    20. Yi-Ting Chen & Yu-Chin Hsu & Hung-Jen Wang, 2020. "A Stochastic Frontier Model with Endogenous Treatment Status and Mediator," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 243-256, April.
    21. Kim, Myungsup & Schmidt, Peter, 2008. "Valid tests of whether technical inefficiency depends on firm characteristics," Journal of Econometrics, Elsevier, vol. 144(2), pages 409-427, June.
    22. Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 460-468, October.
    23. Fan, Yanqin & Li, Qi, 1996. "Consistent Model Specification Tests: Omitted Variables and Semiparametric Functional Forms," Econometrica, Econometric Society, vol. 64(4), pages 865-890, July.
    24. Li, Q. & Wang, Suojin, 1998. "A simple consistent bootstrap test for a parametric regression function," Journal of Econometrics, Elsevier, vol. 87(1), pages 145-165, August.
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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. 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.
    3. Centorrino, Samuele & Parmeter, Christopher F., 2024. "Nonparametric estimation of stochastic frontier models with weak separability," Journal of Econometrics, Elsevier, vol. 238(2).

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    More about this item

    Keywords

    Efficiency ; local-polynomial least-squares ; productivity analysis ; simulation ; stochastic frontier analysis;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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