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Inference in the Nonparametric Stochastic Frontier Model

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Abstract

This paper 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 e ort 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 ineciency 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.

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  • Christopher F. Parameter & Léopold Simar & Ingrid Van Keilegom & Valentin Zelenyuk, 2021. "Inference in the Nonparametric Stochastic Frontier Model," CEPA Working Papers Series WP132021, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:167
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    File URL: https://economics.uq.edu.au/files/32910/WP132021.pdf
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    1. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643, September.
    2. 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.
    3. 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.
    4. 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.
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    17. 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.
    18. 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.
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    24. 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.
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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. Centorrino, Samuele & Parmeter, Christopher F., 2024. "Nonparametric estimation of stochastic frontier models with weak separability," Journal of Econometrics, Elsevier, vol. 238(2).
    3. 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.

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    More about this item

    Keywords

    Stochastic Frontier Analysis; Efficiency; Productivity Analysis; Local-Polynomial Least-Squares;
    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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