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Geographically weighted regression and multicollinearity: dispelling the myth

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  • A. Stewart Fotheringham

    (Arizona State University)

  • Taylor M. Oshan

    (Arizona State University)

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.

Suggested Citation

  • A. Stewart Fotheringham & Taylor M. Oshan, 2016. "Geographically weighted regression and multicollinearity: dispelling the myth," Journal of Geographical Systems, Springer, vol. 18(4), pages 303-329, October.
    • Handle: RePEc:kap:jgeosy:v:18:y:2016:i:4:d:10.1007_s10109-016-0239-5
    DOI: 10.1007/s10109-016-0239-5
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    References listed on IDEAS

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    More about this item

    Keywords

    Geographically weighted regression; GWR; Collinearity; Regression diagnostics;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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