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    Introducing bootstrap methods to investigate coefficient non-stationarity in spatial regression models


    Harris, Paul and Brunsdon, Chris and Lu, Binbin and Nakaya, Tomoki and Charlton, Martin (2017) Introducing bootstrap methods to investigate coefficient non-stationarity in spatial regression models. Spatial Statistics, 21 (1). pp. 241-261. ISSN 2211-6753

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    Abstract

    In this simulation study, parametric bootstrap methods are introduced to test for spatial non-stationarity in the coefficients of regression models. Such a test can be rather simply conducted by comparing a model such as geographically weighted regression(GWR) as an alternative to a standard linear regression, the null hypothesis. In this study however, 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 non-stationary relationships. The new test is objectively assessed via a simulation experiment that generates data and coefficients with known multivariate spatial properties, all within the spatial setting of the oft-studied Georgia educational attainment data set. By applying the bootstrap test and associated contextual diagnostics to pre-specified, area-based, geographical processes, our studyprovides a valuable steer to choosing a suitable regression model for a given spatial process.

    Item Type: Article
    Keywords: Geographically weighted regression; Spatial regression; Hypothesis testing; Collinearity; GWmodel
    Academic Unit: Faculty of Social Sciences > Geography
    Item ID: 10977
    Identification Number: https://doi.org/10.1016/j.spasta.2017.07.006
    Depositing User: Martin Charlton
    Date Deposited: 07 Aug 2019 14:51
    Journal or Publication Title: Spatial Statistics
    Publisher: Elsevier
    Refereed: Yes
    URI:

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