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    Using bootstrap methods to investigate coefficient non-stationarity in regression models: an empirical case study

    Harris, P. and Brunsdon, C. and Gollini, Isabella and Nakaya, T. and Charlton, M. (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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    Abstract

    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 non-stationary 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.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): GWR, Georgia Data, Hypothesis Testing, Spatial Regression, Spatial Nonstationary
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Isabella Gollini
    Date Deposited: 04 Oct 2016 14:14
    Last Modified: 04 Oct 2016 14:14
    URI: http://eprints.bbk.ac.uk/id/eprint/15996

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