Using machine learning to select variables in data envelopment analysis : Simulations and application using electricity distribution data
(2023) In Energy Economics 120.- Abstract
Agencies that regulate electricity providers often apply nonparametric data envelopment analysis (DEA) to assess the relative efficiency of each firm. The reliability and validity of DEA are contingent upon selecting relevant input variables. In the era of big (wide) data, the assumptions of traditional variable selection techniques are often violated due to challenges related to high-dimensional data and their standard empirical properties. Currently, regulators have access to a large number of potential input variables. Therefore, our aim is to introduce new machine learning methods for regulators of the energy market. We also propose a new two-step analytical approach where, in the first step, the machine learning-based adaptive... (More)
Agencies that regulate electricity providers often apply nonparametric data envelopment analysis (DEA) to assess the relative efficiency of each firm. The reliability and validity of DEA are contingent upon selecting relevant input variables. In the era of big (wide) data, the assumptions of traditional variable selection techniques are often violated due to challenges related to high-dimensional data and their standard empirical properties. Currently, regulators have access to a large number of potential input variables. Therefore, our aim is to introduce new machine learning methods for regulators of the energy market. We also propose a new two-step analytical approach where, in the first step, the machine learning-based adaptive least absolute shrinkage and selection operator (ALASSO) is used to select variables and, in the second step, selected variables are used in a DEA model. In contrast to previous research, we find, by using a more realistic data-generating process common for production functions (i.e., Cobb–Douglas and Translog), that the performance of different machine learning techniques differs substantially in different empirically relevant situations. Simulations also reveal that the ALASSO is superior to other machine learning and regression-based methods when the collinearity is low or moderate. However, in situations of multicollinearity, the LASSO approach exhibits the best performance. We also use real data from the Swedish electricity distribution market to illustrate the empirical relevance of selecting the most appropriate variable selection method.
(Less)
- author
- Duras, Toni ; Javed, Farrukh LU ; Månsson, Kristofer ; Sjölander, Pär and Söderberg, Magnus
- organization
- publishing date
- 2023-04
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Curse of dimensionality, Data envelopment analysis, Machine learning, Regulation, Variable selection
- in
- Energy Economics
- volume
- 120
- article number
- 106621
- publisher
- Elsevier
- external identifiers
-
- scopus:85150299788
- ISSN
- 0140-9883
- DOI
- 10.1016/j.eneco.2023.106621
- language
- English
- LU publication?
- yes
- id
- 8a6c86d1-b54e-4111-871b-073420133db9
- date added to LUP
- 2023-04-24 13:26:55
- date last changed
- 2023-04-24 13:26:55
@article{8a6c86d1-b54e-4111-871b-073420133db9,
abstract = {{<p>Agencies that regulate electricity providers often apply nonparametric data envelopment analysis (DEA) to assess the relative efficiency of each firm. The reliability and validity of DEA are contingent upon selecting relevant input variables. In the era of big (wide) data, the assumptions of traditional variable selection techniques are often violated due to challenges related to high-dimensional data and their standard empirical properties. Currently, regulators have access to a large number of potential input variables. Therefore, our aim is to introduce new machine learning methods for regulators of the energy market. We also propose a new two-step analytical approach where, in the first step, the machine learning-based adaptive least absolute shrinkage and selection operator (ALASSO) is used to select variables and, in the second step, selected variables are used in a DEA model. In contrast to previous research, we find, by using a more realistic data-generating process common for production functions (i.e., Cobb–Douglas and Translog), that the performance of different machine learning techniques differs substantially in different empirically relevant situations. Simulations also reveal that the ALASSO is superior to other machine learning and regression-based methods when the collinearity is low or moderate. However, in situations of multicollinearity, the LASSO approach exhibits the best performance. We also use real data from the Swedish electricity distribution market to illustrate the empirical relevance of selecting the most appropriate variable selection method.</p>}},
author = {{Duras, Toni and Javed, Farrukh and Månsson, Kristofer and Sjölander, Pär and Söderberg, Magnus}},
issn = {{0140-9883}},
keywords = {{Curse of dimensionality; Data envelopment analysis; Machine learning; Regulation; Variable selection}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Energy Economics}},
title = {{Using machine learning to select variables in data envelopment analysis : Simulations and application using electricity distribution data}},
url = {{http://dx.doi.org/10.1016/j.eneco.2023.106621}},
doi = {{10.1016/j.eneco.2023.106621}},
volume = {{120}},
year = {{2023}},
}