A Bayesian semi-parametric approach to stochastic frontier models with inefficiency heterogeneity

In this chapter, we present a semiparametric Bayesian approach for stochastic frontier (SF) models that incorporates exogenous covariates into the inefficiency component by using a Dirichlet process model for conditional distributions. We highlight the advantages of our method by contrasting it with traditional SF models and parametric Bayesian SF models using two different applications in the agricultural sector. In the first application, the accounting data of 2,500 dairy farms from five countries are analyzed. In the second case study, data from forty-three smallholder rice producers in the Tarlac region of the Philippines from 1990 to 1997 are analyzed. Our empirical results suggest that the semi-parametric Bayesian stochastic frontier model outperforms its counterparts in predictive efficiency, highlighting its robustness and utility in different agricultural contexts.