Sensitivity analysis of stochastic frontier analysis models

Abstract

Abstract The efficiency measures of the Stochastic Frontier Analysis (SFA) models are dependent on distributional assumptions of the one-sided error or inefficiency term. Given the intent of earlier researchers in the evaluation of a single inefficiency distribution using Monte Carlo (MC) simulation, much attention has not been paid to the comparative analysis of SFA models. Our paper aims to evaluate the effects of the assumption of the inefficiency distribution and thus compares different SFA model assumptions by conducting a MC simulation. In this paper, we derive the population statistical parameters of truncated normal, half-normal, and exponential inefficiency distributions of SFA models with the objective of having comparable sample mean and sample standard deviation during MC simulation. Thus, MC simulation is conducted to evaluate the statistical properties and robustness of the inefficiency distributions of SFA models and across three different misspecification scenarios, sample sizes, production functions, and input distributions. MC simulation results show that the misspecified truncated normal SFA model provides the smallest mean absolute deviation and mean square error when the true data generating process is a half-normal inefficiency distribution.