Skip to main content
Article
Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture data
Applied Economics Letters (2010)
  • Seong-Hoon Cho, The University of Tennessee, Knoxville
  • Dayton M Lambert, The University of Tennessee, Knoxville
  • Zhuo Chen, Centers for Disease Control and Prevention
Abstract
This research note examined the performance of Geographically Weighted Regression (GWR) using two calibration methods. The first method, Cross Validation (CV), has been commonly used in the applied literature using GWR. A second criterion selected an optimal bandwidth that corresponded with the smallest spatial error Lagrange Multiplier (LM) test statistic. We find that there is a tradeoff between addressing spatial autocorrelation and reducing degree of extreme coefficients in GWR. Although spatial autocorrelation can be controlled for by using the LM criterion, a substantial degree of extreme coefficients may remain. However, while the CV approach appears to be less prone to producing extreme coefficients, it may not always attend to the problems that arise in the presence of spatial error autocorrelation
Keywords
  • Geographically weighted regression,
  • China,
  • Spatial correlation
Publication Date
June, 2010
Citation Information
Seong-Hoon Cho, Dayton M Lambert and Zhuo Chen. "Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture data" Applied Economics Letters Vol. 17 Iss. 8 (2010)
Available at: http://works.bepress.com/zhuo_chen/31/