Tsionas, Mike G. (2021) Multi-criteria optimization in regression. Annals of Operations Research, 306 (1-2): 1. pp. 7-25. ISSN 0254-5330
              
Text (BLINDpaper)
BLINDpaper.pdf - Accepted Version
Available under License Creative Commons Attribution.
Download (601kB)
          BLINDpaper.pdf - Accepted Version
Available under License Creative Commons Attribution.
Download (601kB)
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/1800/1800
        Subjects:
          ?? regressioninstrumental variablesautocorrelationheteroskedasticityspecification errormulti-criteria optimizationgeneral decision sciencesmanagement science and operations researchdecision sciences(all) ??
        Departments:
          
        ID Code:
          160654
        Deposited By:
          
        Deposited On:
          07 Oct 2021 14:00
        Refereed?:
          Yes
        Published?:
          Published
        Last Modified:
          19 Sep 2025 16:47
        
 Altmetric
 Altmetric