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Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries

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

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Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries. / Tsionas, Mike.
In: International Journal of Production Economics, Vol. 249, 108492, 31.07.2022.

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

Harvard

Tsionas, M 2022, 'Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries', International Journal of Production Economics, vol. 249, 108492. https://doi.org/10.1016/j.ijpe.2022.108492

APA

Tsionas, M. (2022). Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries. International Journal of Production Economics, 249, Article 108492. https://doi.org/10.1016/j.ijpe.2022.108492

Vancouver

Tsionas M. Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries. International Journal of Production Economics. 2022 Jul 31;249:108492. Epub 2022 May 6. doi: 10.1016/j.ijpe.2022.108492

Author

Tsionas, Mike. / Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries. In: International Journal of Production Economics. 2022 ; Vol. 249.

Bibtex

@article{1b77a26293354e06ad74a8de24fd4ff4,
title = "Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries",
abstract = "We propose smooth monotone concave probabilistic regression trees for the estimation of efficiency and productivity. In particular we modify these techniques to allow for the use of panel data which are often encountered in practice. Probabilistic regression trees provide smooth approximations and at the same time they exploit the versatility of standard regression trees in generating efficiently partitions of the space of the regressors to approximate the unknown frontier. We showcase the new techniques in a large sample of Chilean manufacturing firms.",
keywords = "Industrial and Manufacturing Engineering, Management Science and Operations Research, Economics and Econometrics, General Business, Management and Accounting",
author = "Mike Tsionas",
year = "2022",
month = jul,
day = "31",
doi = "10.1016/j.ijpe.2022.108492",
language = "English",
volume = "249",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries

AU - Tsionas, Mike

PY - 2022/7/31

Y1 - 2022/7/31

N2 - We propose smooth monotone concave probabilistic regression trees for the estimation of efficiency and productivity. In particular we modify these techniques to allow for the use of panel data which are often encountered in practice. Probabilistic regression trees provide smooth approximations and at the same time they exploit the versatility of standard regression trees in generating efficiently partitions of the space of the regressors to approximate the unknown frontier. We showcase the new techniques in a large sample of Chilean manufacturing firms.

AB - We propose smooth monotone concave probabilistic regression trees for the estimation of efficiency and productivity. In particular we modify these techniques to allow for the use of panel data which are often encountered in practice. Probabilistic regression trees provide smooth approximations and at the same time they exploit the versatility of standard regression trees in generating efficiently partitions of the space of the regressors to approximate the unknown frontier. We showcase the new techniques in a large sample of Chilean manufacturing firms.

KW - Industrial and Manufacturing Engineering

KW - Management Science and Operations Research

KW - Economics and Econometrics

KW - General Business, Management and Accounting

U2 - 10.1016/j.ijpe.2022.108492

DO - 10.1016/j.ijpe.2022.108492

M3 - Journal article

VL - 249

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

M1 - 108492

ER -