Nonlinear Deterministic Frontier Model Using Genetic Programming

Abstract

In economics, several parametric regression-based models have been proposed to measure the technical efficiency of decision making units (DMUs). However, the problem of misspecification restricts the use of these methods. In this paper, symbolic regression is employed to obtain the approximate optimal production function automatically using genetic programming (GP). Monte Carlo simulation is used to compare the performance of data envelopment analysis (DEA), deterministic frontier analysis (DFA) and GP-based DFA with respect to three different production functions and sample sizes. The simulated results indicated that the proposed method has better performance than that of others with respect to nonlinear production functions.