The determination of technical efficiency through the previous estimation of a production frontier has been a relevant topic in the literature related to production theory and engineering. Many parametric and nonparametric approaches have been introduced in the last forty years for estimating production frontiers given a data sample. However, few of these methodologies are based on machine learning techniques, despite being a growing field of research. Recently, a bridge has been built between these two literatures; machine learning and production theory, through a new technique proposed in Esteve et al. (Exp Syst Appl 162:113783, 2020), called Efficiency Analysis Trees (EAT). The algorithm corresponding to EAT builds upon the Classification and Regression Trees (CART) technique by Breiman et al. (Classification and regression trees. Taylor & Francis, 1984) for estimating upper enveloping surfaces of data clouds and satisfying monotonicity. In this study, we revise the fundamentals of this new methodology and extend it to the context of measuring productive efficiency under convexification, using the directional distance function. Additionally, a dedicated EATpy package in Python is provided for executing the EAT algorithm, which could be useful for analyzing both small and big data sets in practice. Finally, the methodology is applied to two different-sized empirical datasets.
|Title of host publication||International Series in Operations Research and Management Science|
|Editors||Joe Zhu, Vincent Charles|
|Number of pages||42|
|Publication status||Published - 2021|
Bibliographical noteFunding Information:
Acknowledgments The authors thank the financial support from the Spanish Ministry of Science and Innovation and the State Research Agency under grant PID2019-105952GB-I00/ AEI / 10.13039/501100011033. Additionally, J.L. Zofío thanks the financial support from the Spanish Ministry of Science and Innovation and the State Research Agency under grant EIN2020-112260/AEI/10.13039/501100011033. This work was also supported by the Spanish Ministry of Science, Innovation and Universities under Grant FPU17/05365. Authors thank Dr. Lidia Ortiz for assisting in the calculation of the Li tests.
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.