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

Andrés Ramírez–Hassan (), Juan David Rengifo–Castro (), Miguel Manzur () and Estephania Rueda-Ramírez ()
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Andrés Ramírez–Hassan: Universidad EAFIT
Juan David Rengifo–Castro: Universidad EAFIT
Miguel Manzur: Universidad EAFIT
Estephania Rueda-Ramírez: Universidad EAFIT

Journal of Productivity Analysis, 2025, vol. 64, issue 2, No 3, 145-166

Abstract: 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.

Keywords: Approximate Bayesian computation; efficiency; generalized true random-effects model; stochastic frontier analysis (search for similar items in EconPapers)
JEL-codes: C11 C23 D24 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11123-025-00765-3

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