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Efficient estimation of heteroscedastic mixed geographically weighted regression models

Author

Listed:
  • Chang-Lin Mei

    (Xi’an Polytechnic University)

  • Feng Chen

    (Xi’an Jiaotong University)

  • Wen-Tao Wang

    (Xi’an Jiaotong University)

  • Peng-Cheng Yang

    (Xi’an Jiaotong University)

  • Si-Lian Shen

    (Henan University of Science and Technology)

Abstract

Mixed geographically weighted regression (MGWR) models are a useful tool to model a regression relationship where the impact of some explanatory variables on the response variable is global and that of the others is spatially varying. The existing estimation methods for MGWR models assume that the model errors are homoscedastic. However, heteroscedasticity is very common in geo-referenced data and ignoring heteroscedasticity may cause efficiency loss on the coefficient estimates. In this paper, we propose a re-weighting estimation method for heteroscedastic MGWR models, in which the variance function of the model errors is estimated by the kernel method with an adaptive bandwidth and the coefficients are re-estimated based on the weighted observations. The simulation study shows that the proposed method can substantially improve the estimation efficiency especially for the constant coefficients. A real-world example based on the Dublin voter turnout data is given to demonstrate the application of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:anresc:v:66:y:2021:i:1:d:10.1007_s00168-020-01016-z
    DOI: 10.1007/s00168-020-01016-z
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    References listed on IDEAS

    as
    1. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
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    3. Gollini, Isabella & Lu, Binbin & Charlton, Martin & Brunsdon, Christopher & Harris, Paul, 2015. "GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i17).
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    5. 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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    8. 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.
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    More about this item

    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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