A simple way to deal with multicollinearity

Gikuang Jeff Chen

Journal of Applied Statistics, 2012, vol. 39, issue 9, 1893-1909

Abstract: Despite the long and frustrating history of struggling with the wrong signs or other types of implausible estimates under multicollinearity, it turns out that the problem can be solved in a surprisingly easy way. This paper presents a simple approach that ensures both statistically sound and theoretically consistent estimates under multicollinearity. The approach is simple in the sense that it requires nothing but basic statistical methods plus a piece of a priori knowledge. In addition, the approach is robust even to the extreme case when the a priori knowledge is wrong. A simulation test shows astonishingly superior performance of the method in repeated samples comparing to the OLS, the Ridge Regression and the Dropping-Variable approach.

Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2012.690857 (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:japsta:v:39:y:2012:i:9:p:1893-1909

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2012.690857

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().