Analysis of the condition number in the raise regression

A. F. Roldán López de Hierro, C. García and R. Salmerón

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 24, 6195-6210

Abstract: The raise regression was presented as an alternative methodology to estimate models with collinearity, it is to say, when columns of matrix X are nearly linearly dependent. This method consists in transforming matrix X by matrix X that is obtained after raising one of the explanatory variable to move it geometrically away from the span of the remaining variables. In this article, we analyze the behavior of the condition number in the raise regression showing that the effectiveness of the raise method to solve the problem of multicollinearity is limited to the potential reduction of the condition number that presents an asymptote corresponding to the condition number associated to the auxiliary regression of the raised variable. All the contributions are illustrated with an empirical application.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1740737 (text/html)
Access to full text is restricted to subscribers.

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:taf:lstaxx:v:50:y:2021:i:24:p:6195-6210

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2020.1740737

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().