A Redefined Variance Inflation Factor: Overcoming the Limitations of the Variance Inflation Factor

Román Salmerón-Gómez, Catalina B. García-García () and José García-Pérez
Additional contact information
Román Salmerón-Gómez: Universidad de Granada
Catalina B. García-García: Universidad de Granada
José García-Pérez: University of Almería

Computational Economics, 2025, vol. 65, issue 1, No 12, 337-363

Abstract: Abstract The variance inflation factor is one the most applied tools for diagnosing the possible existence of multicollinearity in a multiple linear regression model. However, the VIF can detect only the relationships between independent variables without considering the intercept and is not appropriate to use with binary variables. In addition, the orthogonal model from which is calculated is also controversial. All these limitations are not usually considered when the VIF is calculated which may lead to misleading conclusions. This paper parts from an alternative orthogonal model to present a redefined variance inflation factor (RVIF) which overcomes the above limitations. This method was implemented in the rvif R package (Salmerón and García in rvif: collinearity detection using redefined variance inflation factor and graphical methods [Computer software manual]. https://cran.r-project.org/package=rvif , 2022). A Monte Carlo simulation is performed to provide threshold for this new measure. The contribution of this paper is illustrated with different examples. It is also compared with the vif command from the car R package to calculate the VIF, concluding that it could be recommendable to warn non-statisticians of its controversial use.

Keywords: Linear regression; Binary variables; Multicollinearity; Variance inflation factor (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10575-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:65:y:2025:i:1:d:10.1007_s10614-024-10575-8

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-024-10575-8

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().