Adaptive non-parametric efficiency frontier analysis: a neural-network-based model

There have been two schools of efficiency analysis for private and public organizations. One is the data envelopment analysis (DEA) method which is based on a mathematical programming approach, and the other is the estimation of stochastic frontier functions (SFF) which is based on the econometric regression theory. Each of these two methodologies has its strength as well as major limitations. This paper proposes a non-parametric efficiency analysis method based on the adaptive neural network technique. The proposed computational method is able to find a stochastic frontier based on a set of input-output observational data. Like SFF, the proposed method considers two types of deviations involved in input-output data: managerial (external) and observational (internal) deviations. Like DEA, the proposed method does not require explicit assumptions about the function structure of the stochastic frontier. However, unlike any SFF and stochastic DEA methods, the proposed method does not require any parametric assumption of distribution functions. Using the neural networks, this method provides an adaptive way of obtaining empirical estimates of stochastic frontiers. An example using real data is presented for illustrative purposes. Simulation experiments demonstrate that the neural-network-based method would be effective as adaptive non-parametric efficiency analysis.