Geographically weighted regression and multicollinearity: dispelling the myth

A. Stewart Fotheringham and Taylor M. Oshan ()
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A. Stewart Fotheringham: Arizona State University
Taylor M. Oshan: Arizona State University

Journal of Geographical Systems, 2016, vol. 18, issue 4, No 1, 303-329

Abstract: Abstract Geographically weighted regression (GWR) extends the familiar regression framework by estimating a set of parameters for any number of locations within a study area, rather than producing a single parameter estimate for each relationship specified in the model. Recent literature has suggested that GWR is highly susceptible to the effects of multicollinearity between explanatory variables and has proposed a series of local measures of multicollinearity as an indicator of potential problems. In this paper, we employ a controlled simulation to demonstrate that GWR is in fact very robust to the effects of multicollinearity. Consequently, the contention that GWR is highly susceptible to multicollinearity issues needs rethinking.

Keywords: Geographically weighted regression; GWR; Collinearity; Regression diagnostics (search for similar items in EconPapers)
JEL-codes: C18 C52 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (30)

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DOI: 10.1007/s10109-016-0239-5

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