Efficiency Analysis and Bayesian Neural Networks

Abstract A key limitation of traditional efficiency measurement is the inability of typical functional forms to capture non-linearities. This study proposes an alternative framework for efficiency analysis by integrating a Bayesian neural network with a generalized true random-effects Stochastic Frontier Analysis (SFA) model. In doing so, complex functional forms are better approximated, while both time-varying and time-invariant inefficiencies are considered for. The neural network SFA model is empirically compared with the conventional SFA model using a Cobb-Douglas specification. The results highlight that the neural network model better captures underlying non-linearities and provides a more accurate assessment of inefficiency. Bayes factors provide strong evidence in favor of the neural network model. These initial findings signal the potential of neural networks to enhance the precision and flexibility of efficiency analysis.