IDEAS home Printed from https://ideas.repec.org/a/bla/jregsc/v44y2004i1p143-157.html
   My bibliography  Save this article

A Note on the Mixed Geographically Weighted Regression Model

Author

Listed:
  • Chang‐Lin Mei
  • Shu‐Yuan He
  • Kai‐Tai Fang

Abstract

. A mixed, geographically weighted regression (GWR) model is useful in the situation where certain explanatory variables influencing the response are global while others are local. Undoubtedly, how to identify these two types of the explanatory variables is essential for building such a model. Nevertheless, It seems that there has not been a formal way to achieve this task. Based on some work on the GWR technique and the distribution theory of quadratic forms in normal variables, a statistical test approach is suggested here to identify a mixed GWR model. Then, this note mainly focuses on simulation studies to examine the performance of the test and to provide some guidelines for performing the test in practice. The simulation studies demonstrate that the test works quite well and provides a feasible way to choose an appropriate mixed GWR model for a given data set.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jregsc:v:44:y:2004:i:1:p:143-157
    DOI: 10.1111/j.1085-9489.2004.00331.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1085-9489.2004.00331.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1085-9489.2004.00331.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chang-Lin Mei & Feng Chen & Wen-Tao Wang & Peng-Cheng Yang & Si-Lian Shen, 2021. "Efficient estimation of heteroscedastic mixed geographically weighted regression models," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(1), pages 185-206, February.
    2. Hans-Friedrich Eckey & Reinhold Kosfeld & Matthias Türck, 2007. "Regional Convergence in Germany: a Geographically Weighted Regression Approach," Spatial Economic Analysis, Taylor & Francis Journals, vol. 2(1), pages 45-64.
    3. Cem Ertur & Julie Le Gallo, 2008. "Regional Growth and Convergence: Heterogenous reaction versus interaction in spatial econometric approaches," Working Papers hal-00463274, HAL.
    4. Danny Wende, 2019. "Spatial risk adjustment between health insurances: using GWR in risk adjustment models to conserve incentives for service optimisation and reduce MAUP," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(7), pages 1079-1091, September.
    5. Wei, Chuan-Hua & Qi, Fei, 2012. "On the estimation and testing of mixed geographically weighted regression models," Economic Modelling, Elsevier, vol. 29(6), pages 2615-2620.
    6. Ingrid Nappi‐Choulet & Tristan‐Pierre Maury, 2011. "A Spatial And Temporal Autoregressive Local Estimation For The Paris Housing Market," Journal of Regional Science, Wiley Blackwell, vol. 51(4), pages 732-750, October.
    7. Andrea Furková, 2021. "Simultaneous consideration of spatial heterogeneity and spatial autocorrelation in European innovation: a spatial econometric approach based on the MGWR-SAR estimation," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 41(2), pages 157-184, October.
    8. Chen, Feng & Mei, Chang-Lin, 2021. "Scale-adaptive estimation of mixed geographically weighted regression models," Economic Modelling, Elsevier, vol. 94(C), pages 737-747.
    9. Mduma, John K. & Wobst, Peter, 2005. "Village Level Labor Market Development in Tanzania: Evidence from Spatial Econometrics," Discussion Papers 276260, University of Bonn, Center for Development Research (ZEF).
    10. Wangchongyu Peng & Weijun Gao & Xin Yuan & Rui Wang & Jinming Jiang, 2019. "Spatiotemporal Differences in Determinants of City Shrinkage Based on Semiparametric Geographically Weighted Regression," Sustainability, MDPI, vol. 11(24), pages 1-17, December.
    11. Geniaux, Ghislain & Martinetti, Davide, 2018. "A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 74-85.
    12. Andrea Furková, 2022. "Implementation of MGWR-SAR models for investigating a local particularity of European regional innovation processes," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(2), pages 733-755, June.
    13. Li Gao & Mingjing Huang & Wuping Zhang & Lei Qiao & Guofang Wang & Xumeng Zhang, 2021. "Comparative Study on Spatial Digital Mapping Methods of Soil Nutrients Based on Different Geospatial Technologies," Sustainability, MDPI, vol. 13(6), pages 1-19, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jregsc:v:44:y:2004:i:1:p:143-157. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0022-4146 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.