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Combining Data Envelopment Analysis and Stochastic Frontiers via a LASSO prior

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Combining Data Envelopment Analysis and Stochastic Frontiers via a LASSO prior. / Tsionas, Mike G.
In: European Journal of Operational Research, Vol. 304, No. 3, 3, 01.02.2023, p. 1158-1166.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tsionas, MG 2023, 'Combining Data Envelopment Analysis and Stochastic Frontiers via a LASSO prior', European Journal of Operational Research, vol. 304, no. 3, 3, pp. 1158-1166. https://doi.org/10.1016/j.ejor.2022.04.029

APA

Tsionas, M. G. (2023). Combining Data Envelopment Analysis and Stochastic Frontiers via a LASSO prior. European Journal of Operational Research, 304(3), 1158-1166. Article 3. https://doi.org/10.1016/j.ejor.2022.04.029

Vancouver

Tsionas MG. Combining Data Envelopment Analysis and Stochastic Frontiers via a LASSO prior. European Journal of Operational Research. 2023 Feb 1;304(3):1158-1166. 3. Epub 2022 Apr 27. doi: 10.1016/j.ejor.2022.04.029

Author

Tsionas, Mike G. / Combining Data Envelopment Analysis and Stochastic Frontiers via a LASSO prior. In: European Journal of Operational Research. 2023 ; Vol. 304, No. 3. pp. 1158-1166.

Bibtex

@article{cf64428a8dfd4e948d69ce95dcde9d35,
title = "Combining Data Envelopment Analysis and Stochastic Frontiers via a LASSO prior",
abstract = "Technical inefficiencies in stochastic frontier models can be thought of as non-negative parameters. Since, however, their number along with other parameters exceeds the sample size, an adaptive LASSO estimator is a reasonable way to overcome the problem, especially in view of the oracle properties of the estimator under broad conditions. It is shown that the adaptive LASSO estimator can be thought of as the posterior mean of a usual stochastic frontier model with a special prior that benchmarks inefficiencies on known quantities. We take these quantities from DEA scores to obtain technical inefficiencies having oracle properties. The LASSO parameters can be estimated routinely in the Bayesian context without the need for cross-validation. In an application to a data set of large U.S. banks we find that adaptive LASSO outperforms significantly traditional stochastic frontier models.",
keywords = "Information Systems and Management, Management Science and Operations Research, Modeling and Simulation, General Computer Science, Industrial and Manufacturing Engineering",
author = "Tsionas, {Mike G.}",
year = "2023",
month = feb,
day = "1",
doi = "10.1016/j.ejor.2022.04.029",
language = "English",
volume = "304",
pages = "1158--1166",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "3",

}

RIS

TY - JOUR

T1 - Combining Data Envelopment Analysis and Stochastic Frontiers via a LASSO prior

AU - Tsionas, Mike G.

PY - 2023/2/1

Y1 - 2023/2/1

N2 - Technical inefficiencies in stochastic frontier models can be thought of as non-negative parameters. Since, however, their number along with other parameters exceeds the sample size, an adaptive LASSO estimator is a reasonable way to overcome the problem, especially in view of the oracle properties of the estimator under broad conditions. It is shown that the adaptive LASSO estimator can be thought of as the posterior mean of a usual stochastic frontier model with a special prior that benchmarks inefficiencies on known quantities. We take these quantities from DEA scores to obtain technical inefficiencies having oracle properties. The LASSO parameters can be estimated routinely in the Bayesian context without the need for cross-validation. In an application to a data set of large U.S. banks we find that adaptive LASSO outperforms significantly traditional stochastic frontier models.

AB - Technical inefficiencies in stochastic frontier models can be thought of as non-negative parameters. Since, however, their number along with other parameters exceeds the sample size, an adaptive LASSO estimator is a reasonable way to overcome the problem, especially in view of the oracle properties of the estimator under broad conditions. It is shown that the adaptive LASSO estimator can be thought of as the posterior mean of a usual stochastic frontier model with a special prior that benchmarks inefficiencies on known quantities. We take these quantities from DEA scores to obtain technical inefficiencies having oracle properties. The LASSO parameters can be estimated routinely in the Bayesian context without the need for cross-validation. In an application to a data set of large U.S. banks we find that adaptive LASSO outperforms significantly traditional stochastic frontier models.

KW - Information Systems and Management

KW - Management Science and Operations Research

KW - Modeling and Simulation

KW - General Computer Science

KW - Industrial and Manufacturing Engineering

U2 - 10.1016/j.ejor.2022.04.029

DO - 10.1016/j.ejor.2022.04.029

M3 - Journal article

VL - 304

SP - 1158

EP - 1166

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 3

M1 - 3

ER -