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This paper describes a method to estimate a production frontier thatsatisfies the axioms of monotonicity and concavity in a non-parametric Bayesiansetting. An inefficiency term that allows for significant departure from priordistributional assumptions is jointly estimated in a single stage withparametric prior assumptions. We introduce heteroscedasticity into theinefficiency terms by local hyperplane-specific shrinkage hyperparameters andimpose monotonicity using bound-constrained local nonlinear regression. Ourminimum-of-hyperplanes estimator imposes concavity. Our Monte Carlo simulationexperiments demonstrate that the frontier and efficiency estimations arecompetitive, economically sound, and allow for the analysis of larger datasetsthan existing nonparametric methods. We validate the proposed method using datafrom 2007-2010 for Japan's concrete industry. The results show that theefficiency levels remain relatively high over the time period.

Estimating Stochastic Production Frontiers: A One-stage Multivariate Semi-Nonparametric Bayesian Concave Regression Method