Bayesian estimation of inefficiency heterogeneity in stochastic frontier models

摘要: Estimation of the one sided error component in stochastic frontier models may erroneously attribute firm characteristics to inefficiency if heterogeneity is unaccounted for. However, unobserved has been little explored. In this work, we propose capture it through a random parameter which affect location, scale, or both parameters truncated normal distribution using Bayesian approach. Our findings two real data sets, suggest that inclusion able latent and can be used validate suitability observed covariates distinguish from inefficiency. Relevant effects are also found on separating shrinking individual posterior efficiency distributions when affects location scale one-sided distribution, consequently affecting estimated mean scores rankings. particular, including simultaneously satisfy scaling property leads decrease uncertainty around less overlapping distributions, provides more reliable