Configuration of sample points for the reduction of multicollinearity in regression models with distance variables

Yukio Sadahiro () and Yan Wang
Additional contact information
Yukio Sadahiro: The University of Tokyo
Yan Wang: The University of Tokyo

The Annals of Regional Science, 2018, vol. 61, issue 2, No 4, 295-317

Abstract: Abstract Regression models often suffer from multicollinearity that greatly reduces the reliability of estimated coefficients and hinders an appropriate understanding of the role of independent variables. It occurs in regional science especially when independent variables include the distances from urban facilities. This paper proposes a new method for deriving the configuration of sample points that reduces multicollinearity in regression models with distance variables. Multicollinearity is evaluated by the maximum absolute correlation coefficient between distance variables. A spatial optimization technique is utilized to calculate the optimal configuration of sample points. The method permits us not only to locate sample points appropriately but also to evaluate the location of facilities from which the distance is measured in terms of the correlation between distance variables in a systematic way. Numerical experiments and empirical applications are performed to test the validity of the method. The results support the technical soundness of the proposed method and provided some useful implications for the design of sample location.

JEL-codes: C10 C61 R29 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00168-018-0868-3 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:spr:anresc:v:61:y:2018:i:2:d:10.1007_s00168-018-0868-3

Ordering information: This journal article can be ordered from
http://link.springer.com/journal/168

DOI: 10.1007/s00168-018-0868-3

Access Statistics for this article

The Annals of Regional Science is currently edited by Martin Andersson, E. Kim and Janet E. Kohlhase

More articles in The Annals of Regional Science from Springer, Western Regional Science Association Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().