An introduction to CNLS and stoNED methods for efficiency analysis: Economic insights and computational aspects

Abstract

This chapter describes the economic insights of the unifying framework known as Stochastic semi-Nonparametric Envelopment of Data (StoNED), which combines the virtues of the widely used neoclassic production models, Data Envelopment Analysis (DEA), and Stochastic Frontier Analysis (SFA). Like DEA, StoNED is able to estimate an axiomatic production function relaxing the functional form specification required in most implementations of SFA. However, StoNED is also consistent with the econometric models of noise, providing a distinct advantage over standard DEA models. Further, StoNED allows for the possibility that systematic inefficiency is negligible consistent with neoclassical theory, thus providing a unifying framework. StoNED is implemented by estimating a conditional mean using convex nonparametric least squares (CNLS) followed by using standard SFA techniques to estimate the average efficiency and decompose the residual. Detailed descriptions of General Algebraic Modeling System (GAMS) and matrix laboratory (MATLAB) code will aid readers in implementing the StoNED estimator.