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On the estimation of technical and allocative efficiency in a panel stochastic production frontier system model: Some new formulations and generalizations

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  • Kumbhakar, Subal C.
  • Tsionas, Mike G.

Abstract

In this paper we propose some alternative formulations and estimation of technical and allocative inefficiency in the presence of some exogenous variables in the context of a panel stochastic frontier model which includes time-invariant firm effects (heterogeneity) along with time-varying technical inefficiency and random noise. These exogenous variables are used to explain technical and allocative inefficiency as well as firm heterogeneity. The presence of these exogenous variables allows us to relax some of the assumptions made in a recent paper by Lai and Kumbhakar (2019). These variables also allow to add flexibility in estimating the model parameters as well as both technical and allocative inefficiency and costs therefrom. More specifically, the incidental parameters problem associated with firm heterogeneity in the production function as well in the first-order conditions of cost minimization can be avoided by parameterizing them in terms of the exogenous variables. We propose and implement model comparison based on Bayes factors and marginal likelihood.

Suggested Citation

  • Kumbhakar, Subal C. & Tsionas, Mike G., 2020. "On the estimation of technical and allocative efficiency in a panel stochastic production frontier system model: Some new formulations and generalizations," European Journal of Operational Research, Elsevier, vol. 287(2), pages 762-775.
    • Handle: RePEc:eee:ejores:v:287:y:2020:i:2:p:762-775
    DOI: 10.1016/j.ejor.2020.04.046
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    Cited by:

    1. Mike Tsionas & Mikael A. Martins & Almas Heshmati, 2023. "Effects of the vaccination and public support on covid-19 cases and number of deaths in Sweden," Operational Research, Springer, vol. 23(3), pages 1-28, September.
    2. Tsionas, Mike G. & Kumbhakar, Subal C., 2021. "Stochastic frontier models with time-varying conditional variances," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1115-1132.
    3. Liu, Tung, 2021. "Measuring cost inefficiency: A dual approach," Economic Modelling, Elsevier, vol. 99(C).
    4. Tsionas, Mike G., 2023. "Minimax regret priors for efficiency estimation," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1279-1285.
    5. Tsionas, Mike G., 2023. "Combining data envelopment analysis and stochastic frontiers via a LASSO prior," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1158-1166.
    6. Salem Gheit, 2022. "A Stochastic Frontier Analysis of the Human Capital Effects on the Manufacturing Industries’ Technical Efficiency in the United States," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 8(3), pages 215-238, July.
    7. Kumbhakar, Subal C. & Tsionas, Mike G., 2021. "Dissections of input and output efficiency: A generalized stochastic frontier model," International Journal of Production Economics, Elsevier, vol. 232(C).

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