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Title: Bayesian estimation of inefficiency heterogeneity in stochastic frontier models
Authors: Galán, J. E.
Veiga, H.
Wiper, M. P.
Keywords: Stochastic Frontier Models
Unobserved Heterogeneity
Bayesian Inference
Issue Date: 2014
Publisher: Springer
Abstract: 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 inefficiency heterogeneity has been little explored. In this work, we propose to capture it through a random parameter which may affect the location, scale, or both parameters of a truncated normal inefficiency distribution using a Bayesian approach. Our findings using two real data sets, suggest that the inclusion of a random parameter in the inefficiency distribution is able to capture latent heterogeneity and can be used to validate the suitability of observed covariates to distinguish heterogeneity from inefficiency. Relevant effects are also found on separating and shrinking individual posterior efficiency distributions when heterogeneity affects the location and scale parameters of the one-sided error distribution, and consequently affecting the estimated mean efficiency scores and rankings. In particular, including heterogeneity simultaneously in both parameters of the inefficiency distribution in models that satisfy the scaling property leads to a decrease in the uncertainty around the mean scores and less overlapping of the posterior efficiency distributions, which provides both more reliable efficiency scores and rankings.
Peer reviewed: Sim
ISSN: 0895-562X
Publisher version: The definitive version is available at:
Appears in Collections:BRU-RI - Artigo em revista científica internacional com arbitragem científica

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