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Zero-inefficiency stochastic frontier models with varying mixing proportion: A semiparametric approach

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  • Tran, Kien C.
  • Tsionas, Mike G.

Abstract

In this paper, we propose a semiparametric version of the zero-inefficiency stochastic frontier model of Kumbhakar, Parmeter, and Tsionas (2013) by allowing for the proportion of firms that are fully efficient to depend on a set of covariates via unknown smooth function. We propose a (iterative) backfitting local maximum likelihood estimation procedure that achieves the optimal convergence rates of both frontier parameters and the nonparametric function of the probability of being efficient. We derive the asymptotic bias and variance of the proposed estimator and establish its asymptotic normality. In addition, we discuss how to test for parametric specification of the proportion of firms that are fully efficient as well as how to test for the presence of fully inefficient firms, based on the sieve likelihood ratio statistics. The finite sample behaviors of the proposed estimation procedure and tests are examined using Monte Carlo simulations. An empirical application is further presented to demonstrate the usefulness of the proposed methodology.

Suggested Citation

  • Tran, Kien C. & Tsionas, Mike G., 2016. "Zero-inefficiency stochastic frontier models with varying mixing proportion: A semiparametric approach," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1113-1123.
    • Handle: RePEc:eee:ejores:v:249:y:2016:i:3:p:1113-1123
    DOI: 10.1016/j.ejor.2015.10.019
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    Cited by:

    1. Tsionas, Mike G. & Mallick, Sushanta K., 2019. "A Bayesian semiparametric approach to stochastic frontiers and productivity," European Journal of Operational Research, Elsevier, vol. 274(1), pages 391-402.
    2. Hou, Zhezhi & Zhao, Shunan & Kumbhakar, Subal C., 2023. "The GMM estimation of semiparametric spatial stochastic frontier models," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1450-1464.
    3. Tsionas, Euthimios G. & Mamatzakis, Emmanuel C., 2017. "Adjustment costs in the technical efficiency: An application to global banking," European Journal of Operational Research, Elsevier, vol. 256(2), pages 640-649.
    4. Kamil Makieła & Błażej Mazur, 2020. "Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis," Econometrics, MDPI, vol. 8(2), pages 1-22, April.
    5. Jianxu Liu & Sanzidur Rahman & Songsak Sriboonchitta & Aree Wiboonpongse, 2017. "Enhancing Productivity and Resource Conservation by Eliminating Inefficiency of Thai Rice Farmers: A Zero Inefficiency Stochastic Frontier Approach," Sustainability, MDPI, vol. 9(5), pages 1-18, May.
    6. 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.
    7. Kevork, Ilias S. & Pange, Jenny & Tzeremes, Panayiotis & Tzeremes, Nickolaos G., 2017. "Estimating Malmquist productivity indexes using probabilistic directional distances: An application to the European banking sector," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1125-1140.
    8. Tsionas, Mike G., 2024. "A generalized inefficiency model with input and output dependence," European Journal of Operational Research, Elsevier, vol. 312(1), pages 315-323.
    9. Preciado Arreola, José Luis & Johnson, Andrew L. & Chen, Xun C. & Morita, Hiroshi, 2020. "Estimating stochastic production frontiers: A one-stage multivariate semiparametric Bayesian concave regression method," European Journal of Operational Research, Elsevier, vol. 287(2), pages 699-711.

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