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Multi-criteria optimization in regression

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Multi-criteria optimization in regression. / Tsionas, Mike G.
In: Annals of Operations Research, Vol. 306, No. 1-2, 1, 30.11.2021, p. 7-25.

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

Harvard

Tsionas, MG 2021, 'Multi-criteria optimization in regression', Annals of Operations Research, vol. 306, no. 1-2, 1, pp. 7-25. https://doi.org/10.1007/s10479-021-03990-9

APA

Tsionas, M. G. (2021). Multi-criteria optimization in regression. Annals of Operations Research, 306(1-2), 7-25. Article 1. https://doi.org/10.1007/s10479-021-03990-9

Vancouver

Tsionas MG. Multi-criteria optimization in regression. Annals of Operations Research. 2021 Nov 30;306(1-2):7-25. 1. Epub 2021 Mar 19. doi: 10.1007/s10479-021-03990-9

Author

Tsionas, Mike G. / Multi-criteria optimization in regression. In: Annals of Operations Research. 2021 ; Vol. 306, No. 1-2. pp. 7-25.

Bibtex

@article{e13562a390ff418aa242f5da8647719e,
title = "Multi-criteria optimization in regression",
abstract = "In this paper, we consider standard as well as instrumental variables regression. Specification problems related to autocorrelation, heteroskedasticity, neglected non-linearity, unsatisfactory out-of-small performance and endogeneity can be addressed in the context of multi-criteria optimization. The new technique performs well, it minimizes all these problems simultaneously, and eliminates them for the most part. Markov Chain Monte Carlo techniques are used to perform the computations. An empirical application to NASDAQ returns is provided.",
keywords = "Regression, Instrumental variables, Autocorrelation, Heteroskedasticity, Specification error, Multi-criteria optimization",
author = "Tsionas, {Mike G.}",
year = "2021",
month = nov,
day = "30",
doi = "10.1007/s10479-021-03990-9",
language = "English",
volume = "306",
pages = "7--25",
journal = "Annals of Operations Research",
issn = "0254-5330",
publisher = "Springer",
number = "1-2",

}

RIS

TY - JOUR

T1 - Multi-criteria optimization in regression

AU - Tsionas, Mike G.

PY - 2021/11/30

Y1 - 2021/11/30

N2 - In this paper, we consider standard as well as instrumental variables regression. Specification problems related to autocorrelation, heteroskedasticity, neglected non-linearity, unsatisfactory out-of-small performance and endogeneity can be addressed in the context of multi-criteria optimization. The new technique performs well, it minimizes all these problems simultaneously, and eliminates them for the most part. Markov Chain Monte Carlo techniques are used to perform the computations. An empirical application to NASDAQ returns is provided.

AB - In this paper, we consider standard as well as instrumental variables regression. Specification problems related to autocorrelation, heteroskedasticity, neglected non-linearity, unsatisfactory out-of-small performance and endogeneity can be addressed in the context of multi-criteria optimization. The new technique performs well, it minimizes all these problems simultaneously, and eliminates them for the most part. Markov Chain Monte Carlo techniques are used to perform the computations. An empirical application to NASDAQ returns is provided.

KW - Regression

KW - Instrumental variables

KW - Autocorrelation

KW - Heteroskedasticity

KW - Specification error

KW - Multi-criteria optimization

U2 - 10.1007/s10479-021-03990-9

DO - 10.1007/s10479-021-03990-9

M3 - Journal article

VL - 306

SP - 7

EP - 25

JO - Annals of Operations Research

JF - Annals of Operations Research

SN - 0254-5330

IS - 1-2

M1 - 1

ER -