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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Andrés Ramírez–Hassan,
  • Juan David Rengifo–Castro,
  • Miguel Manzur,
  • Estephania Rueda-Ramírez

摘要

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.