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On the estimation and testing of mixed geographically weighted regression models

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  • Wei, Chuan-Hua
  • Qi, Fei

Abstract

Mixed geographically weighted regression (MGWR) model is a useful technique to explore spatial non-stationarity by allowing that some coefficients of the explanatory variables are constant and others are spatially varying, but its estimation and inference have not been systematically studied. This paper is concerned with estimation and testing of the model when there are certain linear constraints on the elements of constant coefficients. We propose a constrained two-step technique for estimating the constant coefficients and spatial varying coefficients, and develop a test procedure for the validity of the linear constraints. Finally, some simulations are conducted to examine the performance of our proposed procedure and the results are satisfactory.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecmode:v:29:y:2012:i:6:p:2615-2620
    DOI: 10.1016/j.econmod.2012.08.015
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    References listed on IDEAS

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    1. Daniel P. McMillen & Christian L. Redfearn, 2010. "Estimation And Hypothesis Testing For Nonparametric Hedonic House Price Functions," Journal of Regional Science, Wiley Blackwell, vol. 50(3), pages 712-733, August.
    2. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Testing for Spatial Autocorrelation among the Residuals of the Geographically Weighted Regression," Environment and Planning A, , vol. 32(5), pages 871-890, May.
    3. Antonio Páez & Takashi Uchida & Kazuaki Miyamoto, 2002. "A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 2. Spatial Association and Model Specification Tests," Environment and Planning A, , vol. 34(5), pages 883-904, May.
    4. A S Fotheringham & M E Charlton & C Brunsdon, 1998. "Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis," Environment and Planning A, , vol. 30(11), pages 1905-1927, November.
    5. Stuart A. Foster & Wilpen L. Gorr, 1986. "An Adaptive Filter for Estimating Spatially-Varying Parameters: Application to Modeling Police Hours Spent in Response to Calls for Service," Management Science, INFORMS, vol. 32(7), pages 878-889, July.
    6. Antonio Páez & Takashi Uchida & Kazuaki Miyamoto, 2002. "A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity," Environment and Planning A, , vol. 34(4), pages 733-754, April.
    7. Chang-Lin Mei & Ning Wang & Wen-Xiu Zhang, 2006. "Testing the Importance of the Explanatory Variables in a Mixed Geographically Weighted Regression Model," Environment and Planning A, , vol. 38(3), pages 587-598, March.
    8. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model," Environment and Planning A, , vol. 32(1), pages 9-32, January.
    9. 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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    Cited by:

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    2. Yaxiong Ma & Sucharita Gopal, 2018. "Geographically Weighted Regression Models in Estimating Median Home Prices in Towns of Massachusetts Based on an Urban Sustainability Framework," Sustainability, MDPI, vol. 10(4), pages 1-27, March.
    3. 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.
    4. 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.
    5. Xijian Hu & Yaori Lu & Huiguo Zhang & Haijun Jiang & Qingdong Shi, 2021. "Selection of the Bandwidth Matrix in Spatial Varying Coefficient Models to Detect Anisotropic Regression Relationships," Mathematics, MDPI, vol. 9(18), pages 1-14, September.

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