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Scale-adaptive estimation of mixed geographically weighted regression models

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  • Chen, Feng
  • Mei, Chang-Lin

Abstract

Mixed geographically weighted regression (GWR) models, a combination of linear and spatially varying coefficient models, have found their applications in a variety of disciplines including economic modelling for geo-referenced data analysis. Generally, different explanatory variables may operate at different spatial scales, leading to different levels of spatial heterogeneity of the varying coefficients. To deal with such a multiscale problem, we propose a scale-adaptive method to calibrate mixed GWR models, in which a different bandwidth is separately assumed for each spatially varying coefficient and is selected based on the backfitting procedure. Extensive simulations with different spatial layouts and a real-world example based on the Dublin voter turnout data demonstrate that the scale-adaptive method can not only significantly improve the estimation accuracy of the spatially varying coefficients, but also provide valuable information on the scale at which each explanatory variable operates.

Suggested Citation

  • Chen, Feng & Mei, Chang-Lin, 2021. "Scale-adaptive estimation of mixed geographically weighted regression models," Economic Modelling, Elsevier, vol. 94(C), pages 737-747.
    • Handle: RePEc:eee:ecmode:v:94:y:2021:i:c:p:737-747
    DOI: 10.1016/j.econmod.2020.02.015
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    1. Holly, Sean & Pesaran, M. Hashem & Yamagata, Takashi, 2010. "A spatio-temporal model of house prices in the USA," Journal of Econometrics, Elsevier, vol. 158(1), pages 160-173, September.
    2. Seong-Hoon Cho & Dayton M. Lambert & Roland K. Roberts & Seung Gyu Kim, 2010. "Moderating urban sprawl: is there a balance between shared open space and housing parcel size?," Journal of Economic Geography, Oxford University Press, vol. 10(5), pages 763-783, September.
    3. Gilley, Otis W. & Pace, R. Kelley, 1996. "On the Harrison and Rubinfeld Data," Journal of Environmental Economics and Management, Elsevier, vol. 31(3), pages 403-405, November.
    4. Oikarinen, Elias & Bourassa, Steven C. & Hoesli, Martin & Engblom, Janne, 2018. "U.S. metropolitan house price dynamics," Journal of Urban Economics, Elsevier, vol. 105(C), pages 54-69.
    5. Nadir Öcal & Jülide Yildirim, 2010. "Regional effects of terrorism on economic growth in Turkey: A geographically weighted regression approach," Journal of Peace Research, Peace Research Institute Oslo, vol. 47(4), pages 477-489, July.
    6. Salvati, Luca & Ciommi, Maria Teresa & Serra, Pere & Chelli, Francesco M., 2019. "Exploring the spatial structure of housing prices under economic expansion and stagnation: The role of socio-demographic factors in metropolitan Rome, Italy," Land Use Policy, Elsevier, vol. 81(C), pages 143-152.
    7. Megan Heckert & Jeremy Mennis, 2012. "The Economic Impact of Greening Urban Vacant Land: A Spatial Difference-In-Differences Analysis," Environment and Planning A, , vol. 44(12), pages 3010-3027, December.
    8. Kai Cao & Mi Diao & Bo Wu, 2019. "A Big Data–Based Geographically Weighted Regression Model for Public Housing Prices: A Case Study in Singapore," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 109(1), pages 173-186, January.
    9. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    10. Daisuke Murakami & Binbin Lu & Paul Harris & Chris Brunsdon & Martin Charlton & Tomoki Nakaya & Daniel A. Griffith, 2019. "The Importance of Scale in Spatially Varying Coefficient Modeling," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 109(1), pages 50-70, January.
    11. Chang‐Lin Mei & Shu‐Yuan He & Kai‐Tai Fang, 2004. "A Note on the Mixed Geographically Weighted Regression Model," Journal of Regional Science, Wiley Blackwell, vol. 44(1), pages 143-157, February.
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    More about this item

    Keywords

    Geographically weighted regression; Backfitting; Spatial scale; Bandwidth;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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