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Approximate Bayesian computation to estimate persistent and transient efficiency in stochastic frontier panel data models

Author

Listed:
  • Andrés Ramírez–Hassan

    (Universidad EAFIT)

  • Juan David Rengifo–Castro

    (Universidad EAFIT)

  • Miguel Manzur

    (Universidad EAFIT)

  • Estephania Rueda-Ramírez

    (Universidad EAFIT)

Abstract

We use approximate Bayesian computation (ABC) to estimate panel data stochastic frontier models, allowing for persistent and transient inefficiency, unobserved heterogeneity, and noise. We use ABC to estimate the generalized true random-effects (GTRE) specification. Simulation exercises for estimating technical efficiency show that our proposal has good finite-sample properties under different configurations of the variance parameters of the four random components, as well as on five well-known datasets. Our proposal is easy to implement in the half-normal case, and adaptable to different distributional assumptions regarding the one-sided error components.

Suggested Citation

  • Andrés Ramírez–Hassan & Juan David Rengifo–Castro & Miguel Manzur & Estephania Rueda-Ramírez, 2025. "Approximate Bayesian computation to estimate persistent and transient efficiency in stochastic frontier panel data models," Journal of Productivity Analysis, Springer, vol. 64(2), pages 145-166, October.
    • Handle: RePEc:kap:jproda:v:64:y:2025:i:2:d:10.1007_s11123-025-00765-3
    DOI: 10.1007/s11123-025-00765-3
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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