Economics and Finance Faculty Publications
Document Type
Article
Publication Date
4-21-2026
Abstract
We propose a deep neural network based stochastic frontier model that can handle complex non-linear patterns both in the frontier and in the distribution of inefficiency term. To illustrate our methodology, we employ quarterly data to estimate the technical efficiencies of large US banks from the first quarter of 1984 to the second quarter of 2010. The mean efficiency of US banks during this time period is 89.43%. Between 2004 and 2008, the mean efficiencies of these banks are significantly lower than the overall average, with an average of 81.30%. This is in line with the financial conditions experienced during this time period.
Recommended Citation
Kutlu, L., Mao, X. and Ni, X.S., 2026. A machine learning approach to stochastic frontier modeling. Journal of Productivity Analysis, 65(2), p.20. https://doi.org/10.1007/s11123-026-00800-x
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Publication Title
Journal of Productivity Analysis
DOI
10.1007/s11123-026-00800-x
Comments
This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.