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Original Articles

A study on geographically weighted spatial autoregression models with spatial autoregressive disturbances

, &
Pages 5235-5251 | Received 14 Oct 2017, Accepted 01 May 2019, Published online: 23 May 2019

References

  • Brunsdon, C., A. S. Fotheringham, and M. Charlton. 1996. Geographically weighted regression: A method for exploring spatial non-stationarity. Geographical Analysis 28 (4):281–98. doi: 10.1111/j.1538-4632.1996.tb00936.x.
  • Brunsdon, C., A. S. Fotheringham, and M. Charlton. 1998. Geographically weighted regression: Modelling spatial non-stationarity. Journal of the Royal Statistical Society: Series D (the Statistician) 47 (3):431–43. doi: 10.1111/1467-9884.00145.
  • Brunsdon, C., A. S. Fotheringham, and M. Charlton. 1999. Some notes on parametric significance tests for geographically weighted regression. Journal of Regional Science 39 (3):497–524. doi: 10.1111/0022-4146.00146.
  • Fan, J., and W. Zhang. 1999. Statistical estimation in varying coefficient models. The Annals of Statistics 27 (5):1491–518. doi: 10.1214/aos/1017939139.
  • Fan, J., C. Zhang, and J. Zhang. 2001. Generalized likelihood ratio statistics and Wilks phenomenon. The Annals of Statistics 29 (1):153–93. doi: 10.1214/aos/996986505.
  • Fan, J., and J. Jiang. 2007. Nonparametric inference with generalized likelihood ratio tests. Test 16(3):409–44. doi: 10.1007/s11749-007-0080-8.
  • Farber, S., and A. Páez. 2007. A systematic investigation of cross-validation in GWR model estimation:empirical analysis and Monte Carlo simulations. Journal of Geographical Systems 9 (4):371–96. doi: 10.1007/s10109-007-0051-3.
  • Geniaux, G., and D. Martinetti. 2017. A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models. Regional Science & Urban Economics.
  • Jetz, W., C. Rahbek, and J. W. Lichstein. 2005. Local and global approaches to spatial data analysis in ecology. Global Ecology and Biogeography 14 (1):97–8. doi: 10.1111/geb.2005.14.issue-1.
  • Kelejian, H. H., and I. R. Prucha. 1998. A generalized spatial two stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbances. Journal of Real Estate Finance & Economics 17 (1):99–121.
  • Leung, Y., C. L. Mei, and W. X. Zhang. 2000. Statistical tests for spatial nonstationarity based on the geographically weighted regression model. Environment and Planning A: Economy and Space 32 (1):9–32. doi: 10.1068/a3162.
  • Loader, C. R. 1999. Bandwidth selection: classical or plug-in? The Annals of Statistics 27 (2):415–38. doi: 10.1214/aos/1018031201.
  • Mei, C. L., N. Wang, and W. X. Zhang. 2006. Testing the importance of the explanatory variables in a mixed geographically weighted regression model. Environment and Planning A: Economy and Space 38 (3):587–98. doi: 10.1068/a3768.
  • Mei, C.-L., M. Xu, and N. Wang. 2016. A bootstrap test for constant coefficients in geographically weighted regression models. International Journal of Geographical Information Science 30 (8):1622–43. doi: 10.1080/13658816.2016.1149181.
  • Páez, A., S. Farber, and D. C. Wheeler. 2011. A simulation-based study of geographically weighted regression as a method for investigating spatially varying relationships. Environment and Planning A: Economy and Space 43 (12):2992–3010. doi: 10.1068/a44111.
  • Páez, A., T. Uchida, and K. 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: Economy and Space 34 (4):733–54. doi: 10.1068/a34110.
  • Qiao, N. N. 2013. A Study on Spatial Correlation Test Parameter Estimation in Mixed Geographically Weighted Regression Models. The Journal of Quantitative & Technical Economics 8:93–108.
  • Wheeler, D. C., and C. Calder. 2007. An assessment of coefficient accuracy in linear regression models with spatially varying coefficients. Journal of Geographical Systems 9 (2):145–66. doi: 10.1007/s10109-006-0040-y.
  • Wheeler, D. C., and M. Tiefelsdorf. 2005. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems 7 (2):161–87. doi: 10.1007/s10109-005-0155-6.
  • Wei, C. H., and F. Qi. 2012. On the estimation and testing of mixed geographically weighted regression models. Economic Modelling 29 (6):2615–20. doi: 10.1016/j.econmod.2012.08.015.
  • Yang, W., A. S. Fotheringham, and P. Harris. 2011. Model selection in GWR: the development of a flexible bandwidth GWR. Paper presented at Geocomputation, London, UK January 2011.
  • Yang, W., A. S. Fotheringham, and P. Harris. 2012. An extension of geographically weighted regression with flexible bandwidths. Lancaster, UK: GISRUK.

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