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Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture data

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  • Seong-Hoon Cho
  • Dayton Lambert
  • Zhuo Chen

Abstract

This research note examined the performance of Geographically Weighted Regression (GWR) using two calibration methods. The first method, Cross Validation (CV), has been commonly used in the applied literature using GWR. A second criterion selected an optimal bandwidth that corresponded with the smallest spatial error Lagrange Multiplier (LM) test statistic. We find that there is a tradeoff between addressing spatial autocorrelation and reducing degree of extreme coefficients in GWR. Although spatial autocorrelation can be controlled for by using the LM criterion, a substantial degree of extreme coefficients may remain. However, while the CV approach appears to be less prone to producing extreme coefficients, it may not always attend to the problems that arise in the presence of spatial error autocorrelation.

Suggested Citation

  • Seong-Hoon Cho & Dayton Lambert & Zhuo Chen, 2010. "Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture data," Applied Economics Letters, Taylor & Francis Journals, vol. 17(8), pages 767-772.
    • Handle: RePEc:taf:apeclt:v:17:y:2010:i:8:p:767-772
    DOI: 10.1080/13504850802314452
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    Citations

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    Cited by:

    1. Zhang, Pengyan & Yang, Dan & Qin, Mingzhou & Jing, Wenlong, 2020. "Spatial heterogeneity analysis and driving forces exploring of built-up land development intensity in Chinese prefecture-level cities and implications for future Urban Land intensive use," Land Use Policy, Elsevier, vol. 99(C).
    2. Pede, Valerien O. & Florax, Raymond J.G.M. & Holt, Matthew T., 2009. "A Spatial Econometric Star Model With An Application To U.S. County Economic Growth, 1969–2003," Working papers 48117, Purdue University, Department of Agricultural Economics.
    3. D.P. von Fintel, 2018. "Long-Run Spatial Inequality in South Africa: Early Settlement Patterns and Separate Development," Studies in Economics and Econometrics, Taylor & Francis Journals, vol. 42(2), pages 81-102, August.
    4. Gianluca Boo & Stefan Leyk & Christopher Brunsdon & Ramona Graf & Andreas Pospischil & Sara Irina Fabrikant, 2018. "The importance of regional models in assessing canine cancer incidences in Switzerland," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-16, April.
    5. Mouhcine Guettabi & Abdul Munasib, 2014. "“Space Obesity”: The Effect of Remoteness on County Obesity," Growth and Change, Wiley Blackwell, vol. 45(4), pages 518-548, December.
    6. Yi Pan & Qiqi Yuan & Jinsong Ma & Lachun Wang, 2022. "Improved Daily Spatial Precipitation Estimation by Merging Multi-Source Precipitation Data Based on the Geographically Weighted Regression Method: A Case Study of Taihu Lake Basin, China," IJERPH, MDPI, vol. 19(21), pages 1-18, October.
    7. Mouhcine Guettabi & Abdul Munasib, 2014. "Urban Sprawl, Obesogenic Environment, And Child Weight," Journal of Regional Science, Wiley Blackwell, vol. 54(3), pages 378-401, June.
    8. Xu, Wan & Lambert, Dayton M., 2011. "Business Establishment Growth in the Appalachian Region, 2000-2007: An Application of Smooth Transition Spatial Process Models," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(3), pages 1-16, August.
    9. Guang Chen & Yue Deng & Apurbo Sarkar & Zhengbing Wang, 2022. "An Integrated Assessment of Different Types of Environment-Friendly Technological Progress and Their Spatial Spillover Effects in the Chinese Agriculture Sector," Agriculture, MDPI, vol. 12(7), pages 1-24, July.
    10. Bo Pieter Johannes Andree & Francisco Blasques & Eric Koomen, 2017. "Smooth Transition Spatial Autoregressive Models," Tinbergen Institute Discussion Papers 17-050/III, Tinbergen Institute.

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