Nonparametric, Stochastic Frontier Models with Multiple Inputs and Outputs

Stochastic frontier models along the lines of Aigner et al. (1977) are widely used to benchmark firms' performances in terms of efficiency. The models are typically fully-parametric, with functional form specifications for the frontier as well as both the noise and the inefficiency processes. Studies such as Kumbhakar et al. (2007) have attempted to relax some of the restrictions in parametric models, but so far all such approaches are limited to a univariate response variable. Some (e.g., Simar and Zelenyuk, 2011; Kuosmanen and Johnson, 2017) have proposed nonparametric estimation of directional distance functions to handle multiple inputs and outputs, raising issues of endogeneity that are either ignored or addressed by imposing restrictive and implausible assumptions. This paper extends nonparametric methods developed by Simar et al. (2017) and Hafner et al. (2018) to allow multiple inputs and outputs in an almost fullynonparametric framework while avoiding endogeneity problems. We discuss identification issues and properties of the resulting estimators, and examine their finite-sample performance through Monte-Carlo experiments. Practical implementation of the method is illustrated using data on U.S. commercial banks.