Multi-criteria optimization in regression

Tsionas, Mike G. (2021) Multi-criteria optimization in regression. Annals of Operations Research, 306 (1-2): 1. pp. 7-25. ISSN 0254-5330

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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.

Item Type:
Journal Article
Journal or Publication Title:
Annals of Operations Research
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800
Subjects:
?? regressioninstrumental variablesautocorrelationheteroskedasticityspecification errormulti-criteria optimizationdecision sciences(all)management science and operations research ??
ID Code:
160654
Deposited By:
Deposited On:
07 Oct 2021 14:00
Refereed?:
Yes
Published?:
Published
Last Modified:
12 Feb 2024 00:42