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

Applied Economics Letters

Volumn 17, Issue 8, 2010, Pages 767-772

  Cho, Seong Hoon ^a   Lambert, Dayton M ^a   Chen, Zhuo ^b  

^a UNIVERSITY OF TENNESSEE   (United States)

^b UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AGRICULTURE; AUTOCORRELATION; LAGRANGIAN ANALYSIS; REGRESSION ANALYSIS; SPATIAL ANALYSIS;

CHINA;

EID: 77953355421     PISSN: 13504851     EISSN: 14664291     Source Type: Journal    
DOI: 10.1080/13504850802314452     Document Type: Article

References (27)

1. Anselin, L. (1988) Spatial Econometrics: Methods and Models, Kluwer Academic, Dordrecht.
2. Brunsdon, C., Fotheringham, A. and Charlton, M. (1996) Geographically weighted regression: a method for explor- ing spatial nonstationarity, Geographical Analysis, 28, 281-98.
3. Brunsdon, C., Fotheringham, A. and Charlton, M. (1999) Some notes on parametric significance tests for geogra-phically weighted regression, Journal of Regional Science, 39, 497-524.
4. Cameron, A. C. and Trivedi, P. K. (2005) Microeconometrics: Methods and Applications, Cambridge University Press, New York.
5. Cho, S., Chen, Z. and Poudyal, N. C. (2008) Spatial structure of agricultural production in China, Applied Economics (DOI:10.1080/00036840701765320).
6. Cho, S., Lambert, D. M., Roberts, R. K. and Kim, S. (2009) Demand for open space and urban sprawl: the case of Knoxville County, Tennessee, in Progress in Spatial Analysis: Theory and Computation, and Thematic Applications (Eds) A. Paez, J. Le Gallo, R. Buliung, S. Dall'Erba, Springer, Berlin, forthcoming.
7. Cleveland, W. S. (1979) Robust locally weighted regression and smoothing scatterplots, Journal of the American Statistical Association, 74, 829-36.
8. Cleveland, W. S. and Devlin, S. J. (1988) Locally weighted regression: an approach to regression analysis by local fitting, Journal of the American Statistical Association, 83,596-610.
9. Deller, S. C. and Lledo, V. (2007) Amenities and rural Appalachian growth, Agricultural and Resource Economics Review, 36, 107-32.
10. Farber, S. and Paez, A. (2007) A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations, Journal of Geographical Systems, 9, 371-96.
11. Fotheringham, A., Brunsdon, C. and Charlton, M. (1998) Geographically weighted regression: a natural evolution of the expansion method for spa- tial data analysis, Environment and Planning A, 30, 1905-27.
12. Fotheringham, A., Brunsdon, C. and Charlton, M. (2002) Geographically Weighted Regression: The Analysis of Spatially Varying Relationship, John Wiley & Sons Ltd, West Sussex.
13. Huang, Y. and Leung, Y. (2002) Analyzing regional industrialization in Jiangsu province using geographically weighted regression, Journal of Geographical Systems, 4,233-49.
14. Lambert, D. M., McNamara, T. K. and Garrett, M. I. (2006) An application of spatial poisson models to manufacturing investment location analysis, Journal of Agricultural and Applied Economics, 38, 105-21.
15. Leung, Y., Mei, C. and Zhang, W. (2000a) Statistical tests for spatial nonstationary based on the geographically weighted regression model, Environment and Planning A, 32,9-32.
16. Leung, Y., Mei, C. and Zhang, W. (2000b) Testing for spatial autocorrelation among the residuals of the geographically weighted regression, Environment and Planning A, 32, 871-90.
17. McMillen, D. P. (1996) One hundred fifty years of land values in Chicago: a nonparametric approach, Journal of Urban Economics, 40, 100-24.
18. McMillen, D. P. (2004) Employment densities and subcenters in large metropolitan areas, Journal of Regional Science, 44, 225-43.
19. Paez, A., Long, F. and Farber, S. (2008) Moving window approaches for hedonic price estimation: an empirical comparison of modeling techniques, Urban Studies, 45, 1565-81.
20. Paez, A., Uchida, T. and Miyamoto, K. (2002a) A general framework for estimation and inference of geographically weighted regression models: 1. location- specific kernel bandwidths and a test for local hetero- geneity, Environment and Planning A, 34, 733-54.
21. Paez, A., Uchida, T. and Miyamoto, K. (2002b) A general framework for estimation and inference of geographically weighted regression models: 2. Spatial association and model specification tests, Environment and Planning A, 34, 883-904.
22. Waller L.,Zhu L.,Gotway C.,Gorman D.andGruenewald P. (2007) Quantifying geographic variations in associations between alcohol distribution and violence: a comparison of geographically weighted regression and spatially varying coefficient models, Stochastic Environmental Research and Risk Assessment, 21, 573-88.
23. Wan, G. H. and Cheng, E. (2001) Effects of land fragmentation and returns to scale in the Chinese farming sector, Applied Economics, 33, 183-94.
24. Wheeler, D. (2007) Diagnostic tools and a remedial method for collinearity in geographically weighted regression, Environment and Planning A, 39, 2464-81.
25. Wheeler, D. and Calder, C. (2007) An assessment of coefficient accuracy in linear regression models with spatially varying coefficients, Journal of Geographical Systems, 9,145-66.
26. Wheeler, D. and Tiefelsdorf, M. (2005) Multicollinearity and correlation among local regression coefficients in geographically weighted regression, Journal of Geographical Systems, 7, 161-87.
27. Yu, D. and Wu, C. (2004) Understanding population segregation from Landsat ETM+ imagery: a geographically weighted regression approach, GIScience and Remote Sensing, 41, 145-64.