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    Using Bootstrap Methods to Investigate Coefficient Non-stationarity in Regression Models: An Empirical Case Study

    Harris, Paul and Brunsdon, Chris and Gollini, Isabella and Nakaya, Tomoki and Charlton, Martin (2015) Using Bootstrap Methods to Investigate Coefficient Non-stationarity in Regression Models: An Empirical Case Study. Procedia Environmental Sciences, 27. pp. 112-115. ISSN 1878-0296

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    In this study, parametric bootstrap methods are used to test for spatial non-stationarity in the coefficients of regression models (i.e. test for relationship non-stationarity). Such a test can be rather simply conducted by comparing a model such as geographically weighted regression (GWR) as an alternative to a standard regression, the null hypothesis. However here, three spatially autocorrelated regressions are also used as null hypotheses: (i) a simultaneous autoregressive error model; (ii) a moving average error model; and (iii) a simultaneous autoregressive lag model. This expansion of null hypotheses, allows an investigation as to whether the spatial variation in the coefficients obtained using GWR could be attributed to some other spatial process, rather than one depicting nonstationary relationships. In this short presentation, the bootstrap approach is applied empirically to an educational attainment data set for Georgia, USA. Results suggest value in the bootstrap approach, providing a more informative test than any related test that is commonly applied.

    Item Type: Article
    Keywords: GWR; Georgia Data; Hypothesis Testing; Spatial Regression; Spatial Nonstationary;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG
    Item ID: 7845
    Identification Number:
    Depositing User: Martin Charlton
    Date Deposited: 01 Feb 2017 15:47
    Journal or Publication Title: Procedia Environmental Sciences
    Publisher: Elsevier
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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