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Performances of model selection criteria when variables are ill conditioned
Linnaeus University, School of Business and Economics, Department of Economics and Statistics. (Economics and Statistics)ORCID iD: 0000-0002-3623-5034
Linnaeus University, School of Business and Economics, Department of Economics and Statistics. (Economics and Statistics)
Linnaeus University, School of Business and Economics, Department of Economics and Statistics. Jönköping University. (Economics and Statistics)ORCID iD: 0000-0002-3416-5896
2018 (English)In: Computational Economics, ISSN 0927-7099, E-ISSN 1572-9974, p. 1-22Article in journal (Refereed) Epub ahead of print
Abstract [en]

Model selection criteria are often used to find a "proper" model for the data under investigation when building models in cases in which the dependent or explained variables are assumed to be functions of several independent or explanatory variables. For this purpose, researchers have suggested using a large number of such criteria. These criteria have been shown to act differently, under the same or different conditions, when trying to select the "correct" number of explanatory variables to be included in a given model; this, unfortunately, leads to severe problems and confusion for researchers. In this paper, using Monte Carlo methods, we investigate the properties of four of the most common criteria under a number of realistic situations. These criteria are the adjusted coefficient of determination (R2-adj), Akaike's information criterion (AIC), the Hannan–Quinn information criterion (HQC) and the Bayesian information criterion (BIC). The results from this investigation indicate that the HQC outperforms the BIC, the AIC and the R2-adj under specific circumstances. None of them perform satisfactorily, however, when the degree of multicollinearity is high, the sample sizes are small or when the fit of the model is poor (i.e., there is a low R2) . In the presence of all these factors, the criteria perform very badly and are not very useful. In these cases, the criteria are often not able to select the true model.

Place, publisher, year, edition, pages
Springer, 2018. p. 1-22
Keywords [en]
Information criteria, Model selection, Multicollinearity, Monte Carlo methods
National Category
Social Sciences
Research subject
Statistics/Econometrics
Identifiers
URN: urn:nbn:se:lnu:diva-62075DOI: 10.1007/s10614-017-9682-8OAI: oai:DiVA.org:lnu-62075DiVA, id: diva2:1086736
Available from: 2017-04-03 Created: 2017-04-03 Last updated: 2018-03-02

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Karlsson, Peter S.Behrenz, LarsShukur, Ghazi

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