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Unknown latent structure and inefficiency in panel stochastic frontier models

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Abstract

This paper extends the fixed effect panel stochastic frontier models to allow group heterogeneity in the slope coefficients. We propose the first-difference penalized maximum likelihood (FDPML) and control function penalized maximum likelihood (CFPML) methods for classification and estimation of latent group structures in the frontier as well as inefficiency. Monte Carlo simulations show that the proposed approach performs well in finite samples. An empirical application is presented to show the advantages of data-determined identification of the heterogeneous group structures in practice.

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Notes

  1. C-Lasso is termed by SSP.

  2. Even if we do not formally establish the asymptotic properties of the FDPL estimator, it is worth pointing out that the results of our Monte Carlo simulations are consistent with the belief that these asymptotic properties hold. See Section 5 for more details.

  3. Note that \(\left| {\mathrm{\Sigma }} \right| = T\).

  4. Detailed results for the estimated frontier parameters are available from the authors up request.

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Acknowledgements

We would like to thank the Editor, the Associate Editor and two anonymous referees for constructive comments and suggestions that helped improve this paper. An earlier draft of this paper was presented at The EcoSta Conference in Hong Kong, June 2017. We would like to thank Artem Prokhorov, Dan Henderson, and the participants in our invited session for their comments and suggestions.

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Authors and Affiliations

  1. Department of Economics, University of Texas Rio Grande Valley, Edinburg, TX, USA

    Levent Kutlu

  2. Department of Economics, University of Lethbridge, 4401 University Drive W, Lethbridge, AB, Canada

    Kien C. Tran

  3. Lancaster University Management School, Lancaster, UK

    Mike G. Tsionas

Authors
  1. Levent Kutlu
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  2. Kien C. Tran
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  3. Mike G. Tsionas
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Correspondence to Kien C. Tran.

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Kutlu, L., Tran, K.C. & Tsionas, M.G. Unknown latent structure and inefficiency in panel stochastic frontier models. J Prod Anal 54, 75–86 (2020). https://doi.org/10.1007/s11123-020-00584-8

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