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.
