4.7 Article

Estimating production functions through additive models based on regression splines

期刊

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 312, 期 2, 页码 684-699

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ELSEVIER
DOI: 10.1016/j.ejor.2023.06.035

关键词

Data envelopment analysis; Additive models; Machine learning; Overfitting

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This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
This paper introduces a new methodology for the estimation of production functions satisfying some classical production theory axioms, such as monotonicity and concavity, which is based upon the adapta-tion of an additive version of the machine learning technique known as Multivariate Adaptive Regression Splines (MARS). The new approach shares the piece-wise linear shape of the estimator associated with Data Envelopment Analysis (DEA). However, the new technique is able to surmount the overfitting prob-lems associated with DEA by resorting to generalized cross-validation. In this paper, a computational experience was employed to measure how well the new approach performs, showing that it can reduce the mean squared error and bias of the estimator of the true production function in comparison with DEA and the more recent Corrected Concave Non-Parametric Least Squares (C2NLS) methodology. We also show that the success of the new approach depends on whether or not interactions among variables prevail and the degree of non-additivity of the true production function to be estimated.& COPY; 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

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