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

Configuration of sample points for the reduction of multicollinearity in regression models with... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Annals of Regional Science Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Economics; Regional/Spatial Science; Landscape/Regional and Urban Planning; Microeconomics; Environmental Economics; Geography, general
ISSN
0570-1864
eISSN
1432-0592
D.O.I.
10.1007/s00168-018-0868-3
Publisher site
See Article on Publisher Site

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.

Journal

The Annals of Regional ScienceSpringer Journals

Published: Jun 5, 2018

References

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