Geographically weighted regression bandwidth selection and spatial autocorrelation: An empirical example using Chinese agriculture data

Seong Hoon Cho, Dayton M. Lambert, Zhuo Chen

Research output: Journal PublicationArticlepeer-review

38 Citations (Scopus)

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 thatthere isa tradeoffbetween 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.

Original languageEnglish
Pages (from-to)767-772
Number of pages6
JournalApplied Economics Letters
Volume17
Issue number8
DOIs
Publication statusPublished - Jun 2010
Externally publishedYes

ASJC Scopus subject areas

  • Economics and Econometrics

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