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Robust estimation in stochastic frontier models

Author

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  • Song, Junmo
  • Oh, Dong-hyun
  • Kang, Jiwon

Abstract

This study proposes a robust estimator for stochastic frontier models by integrating the idea of Basu et al. (1998) into such models. It is shown that the suggested estimator is strongly consistent and asymptotic normal under regularity conditions. The robust properties of the proposed approach are also investigated. A simulation study demonstrates that the estimator has strong robust properties with little loss in asymptotic efficiency relative to the maximum likelihood estimator. Finally, a real data analysis is performed to illustrate the use of the estimator.

Suggested Citation

  • Song, Junmo & Oh, Dong-hyun & Kang, Jiwon, 2017. "Robust estimation in stochastic frontier models," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 243-267.
    • Handle: RePEc:eee:csdana:v:105:y:2017:i:c:p:243-267
    DOI: 10.1016/j.csda.2016.08.005
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    4. Zhang, Ning & Huang, Xuhui & Liu, Yunxiao, 2021. "The cost of low-carbon transition for China's coal-fired power plants: A quantile frontier approach," Technological Forecasting and Social Change, Elsevier, vol. 169(C).

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