Efficiency analysis trees: A new methodology for estimating production frontiers through decision trees. (30th December 2020)

• Record Type: Journal Article
• Authors: Esteve, Miriam; Aparicio, Juan; Rabasa, Alejandro; Rodriguez-Sala, Jesus J.
• Is Part Of: Expert systems with applications. Volume 162(2020)
• Publication Date: 2020-12-30
• Subjects: Data envelopment analysis -- Frontier analysis -- Free disposal hull -- Overfitting -- Classification and Regression Trees; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113783
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A relationship between Machine learning and frontier efficiency analysis is stated. A method based on regression trees is introduced for estimating production frontiers. The new method guaranties the satisfaction of free disposability. The problem of overfitting is solved. A Monte Carlo experience shows that the new method outperforms FDH. Abstract: In this paper, we introduce a new methodology based on regression trees for estimating production frontiers satisfying fundamental postulates of microeconomics, such as free disposability. This new approach, baptized as Efficiency Analysis Trees (EAT), shares some similarities with the Free Disposal Hull (FDH) technique. However, and in contrast to FDH, EAT overcomes the problem of overfitting by using cross-validation to prune back the deep tree obtained in the first stage. Finally, the performance of EAT is measured via Monte Carlo simulations, showing that the new approach reduces the mean squared error associated with the estimation of the true frontier by between 13% and 70% in comparison with the standard FDH.