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Performance estimation when the distribution of inefficiency is unknown

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Performance estimation when the distribution of inefficiency is unknown. / Tsionas, Mike G.
In: European Journal of Operational Research, Vol. 304, No. 3, 01.02.2023, p. 1212-1222.

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

Harvard

Tsionas, MG 2023, 'Performance estimation when the distribution of inefficiency is unknown', European Journal of Operational Research, vol. 304, no. 3, pp. 1212-1222. https://doi.org/10.1016/j.ejor.2022.05.004

APA

Tsionas, M. G. (2023). Performance estimation when the distribution of inefficiency is unknown. European Journal of Operational Research, 304(3), 1212-1222. https://doi.org/10.1016/j.ejor.2022.05.004

Vancouver

Tsionas MG. Performance estimation when the distribution of inefficiency is unknown. European Journal of Operational Research. 2023 Feb 1;304(3):1212-1222. Epub 2022 May 10. doi: 10.1016/j.ejor.2022.05.004

Author

Tsionas, Mike G. / Performance estimation when the distribution of inefficiency is unknown. In: European Journal of Operational Research. 2023 ; Vol. 304, No. 3. pp. 1212-1222.

Bibtex

@article{da04a9b910914d9ca89bfaf1600caa03,
title = "Performance estimation when the distribution of inefficiency is unknown",
abstract = "We show how to compute inefficiency or performance scores when the distribution of the one-sided error component in Stochastic Frontier Models (SFMs) is unknown; and we do the same with Data Envelopment Analysis (DEA). Our procedure, which is based on the Fast Fourier Transform (FFT), utilizes the empirical characteristic function of the residuals in SFMs or efficiency scores in DEA. The new techniques perform well in Monte Carlo experiments and deliver reasonable results in an empirical application to large U.S. banks. In both cases, deconvolution of DEA scores with the FFT brings the results much closer to the inefficiency estimates from SFM.",
keywords = "Productivity and Competitiveness, Stochastic Frontier Models, Data Envelopment Analysis (DEA), Fast Fourier Transform, Empirical Characteristic Function",
author = "Tsionas, {Mike G.}",
year = "2023",
month = feb,
day = "1",
doi = "10.1016/j.ejor.2022.05.004",
language = "English",
volume = "304",
pages = "1212--1222",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "3",

}

RIS

TY - JOUR

T1 - Performance estimation when the distribution of inefficiency is unknown

AU - Tsionas, Mike G.

PY - 2023/2/1

Y1 - 2023/2/1

N2 - We show how to compute inefficiency or performance scores when the distribution of the one-sided error component in Stochastic Frontier Models (SFMs) is unknown; and we do the same with Data Envelopment Analysis (DEA). Our procedure, which is based on the Fast Fourier Transform (FFT), utilizes the empirical characteristic function of the residuals in SFMs or efficiency scores in DEA. The new techniques perform well in Monte Carlo experiments and deliver reasonable results in an empirical application to large U.S. banks. In both cases, deconvolution of DEA scores with the FFT brings the results much closer to the inefficiency estimates from SFM.

AB - We show how to compute inefficiency or performance scores when the distribution of the one-sided error component in Stochastic Frontier Models (SFMs) is unknown; and we do the same with Data Envelopment Analysis (DEA). Our procedure, which is based on the Fast Fourier Transform (FFT), utilizes the empirical characteristic function of the residuals in SFMs or efficiency scores in DEA. The new techniques perform well in Monte Carlo experiments and deliver reasonable results in an empirical application to large U.S. banks. In both cases, deconvolution of DEA scores with the FFT brings the results much closer to the inefficiency estimates from SFM.

KW - Productivity and Competitiveness

KW - Stochastic Frontier Models

KW - Data Envelopment Analysis (DEA)

KW - Fast Fourier Transform

KW - Empirical Characteristic Function

U2 - 10.1016/j.ejor.2022.05.004

DO - 10.1016/j.ejor.2022.05.004

M3 - Journal article

VL - 304

SP - 1212

EP - 1222

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 3

ER -