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When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models

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  • Badunenko, Oleg
  • Kumbhakar, Subal C.

Abstract

In this paper we examine robustness of a recently developed panel data stochastic frontier model that allows for both persistent and transient (also known as long-run and short-run or time-invariant and time-varying) inefficiency along with random firm-effects (heterogeneity) and noise. We address some concerns that the practitioners might have about this model. First, given that there are two random time-invariant components (persistent inefficiency and firm-effects) the concern is whether the model can accurately identify them, and if so how precisely can the model estimate them? Second, there are two time-varying random components (transient inefficiency and noise), and the concern is whether the model can separate noise from transient inefficiency, and if so how precisely can the model estimate transient inefficiency? Third, how well are persistent and transient inefficiency estimated under different scenarios, viz., under different configurations of the variance parameters of the four random components? Given that the model is quite complex, relatively new and becoming quite popular in the panel efficiency literature, we feel that there is need for a detailed simulation study to examine when, where and how one can use this model with confidence to estimate persistent and transient inefficiency.

Suggested Citation

  • Badunenko, Oleg & Kumbhakar, Subal C., 2016. "When, where and how to estimate persistent and transient efficiency in stochastic frontier panel data models," European Journal of Operational Research, Elsevier, vol. 255(1), pages 272-287.
  • Handle: RePEc:eee:ejores:v:255:y:2016:i:1:p:272-287
    DOI: 10.1016/j.ejor.2016.04.049
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    References listed on IDEAS

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    Cited by:

    1. Antonio Carvalho, 2016. "Energy Efficiency in Transition Economies: A Stochastic Frontier Approach," CEERP Working Paper Series 004, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    2. Gralka, Sabine, 2018. "Stochastic frontier analysis in higher education: A systematic review," CEPIE Working Papers 05/18, Technische Universität Dresden, Center of Public and International Economics (CEPIE).
    3. Kamil Makieła, 2017. "Bayesian Inference and Gibbs Sampling in Generalized True Random-Effects Models," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 9(1), pages 69-95, March.
    4. Agasisti, Tommaso & Gralka, Sabine, 2017. "The transient and persistent efficiency of Italian and German universities: A stochastic frontier analysis," CEPIE Working Papers 14/17, Technische Universität Dresden, Center of Public and International Economics (CEPIE).
    5. Tsionas, Mike G., 2017. "Microfoundations for stochastic frontiers," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1165-1170.
    6. repec:eee:ejores:v:271:y:2018:i:1:p:250-261 is not listed on IDEAS
    7. repec:eee:ejores:v:273:y:2019:i:3:p:1165-1179 is not listed on IDEAS

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