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Geographically weight seemingly unrelated regression (GWSUR): a method for exploring spatio-temporal heterogeneity

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  • Chuanhua Wei
  • Chao Liu
  • Fengyun Gui

Abstract

Geographically weight seemingly unrelated regression is a useful technique to explore the temporal and spatial heterogeneity simultaneously in space-time data analysis. In this article, a local linear-based estimating approach is developed to estimate the unknown coefficient functions. Some simulations are conducted to examine the performance of our proposed method and the results are satisfactory. Finally, a real data example is considered.

Suggested Citation

  • Chuanhua Wei & Chao Liu & Fengyun Gui, 2017. "Geographically weight seemingly unrelated regression (GWSUR): a method for exploring spatio-temporal heterogeneity," Applied Economics, Taylor & Francis Journals, vol. 49(42), pages 4189-4195, September.
  • Handle: RePEc:taf:applec:v:49:y:2017:i:42:p:4189-4195
    DOI: 10.1080/00036846.2017.1279266
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    References listed on IDEAS

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    1. Julie Le Gallo & Sandy Dall’erba, 2006. "Evaluating the Temporal and Spatial Heterogeneity of the European Convergence Process, 1980–1999," Journal of Regional Science, Wiley Blackwell, vol. 46(2), pages 269-288, May.
    2. Badi Baltagi & Alain Pirotte, 2011. "Seemingly unrelated regressions with spatial error components," Empirical Economics, Springer, vol. 40(1), pages 5-49, February.
    3. Ning Wang & Chang-Lin Mei & Xiao-Dong Yan, 2008. "Local linear estimation of spatially varying coefficient models: an improvement on the geographically weighted regression technique," Environment and Planning A, Pion Ltd, London, vol. 40(4), pages 986-1005, April.
    4. Xiaokun Wang & Kara Kockelman, 2007. "Specification and estimation of a spatially and temporally autocorrelated seemingly unrelated regression model: application to crash rates in China," Transportation, Springer, vol. 34(3), pages 281-300, May.
    5. Sergio Rey & Brett Montouri, 1999. "US Regional Income Convergence: A Spatial Econometric Perspective," Regional Studies, Taylor & Francis Journals, vol. 33(2), pages 143-156.
    6. Jesús Mur & Fernando López & Marcos Herrera, 2010. "Testing for Spatial Effects in Seemingly Unrelated Regressions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(4), pages 399-440.
    7. Baltagi, Badi H. & Bresson, Georges, 2011. "Maximum likelihood estimation and Lagrange multiplier tests for panel seemingly unrelated regressions with spatial lag and spatial errors: An application to hedonic housing prices in Paris," Journal of Urban Economics, Elsevier, vol. 69(1), pages 24-42, January.
    8. Bernard Fingleton, 2007. "A multi-equation spatial econometric model, with application to EU manufacturing productivity growth," Journal of Geographical Systems, Springer, vol. 9(2), pages 119-144, June.
    9. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    10. Ning Wang & Chang-Lin Mei & Xiao-Dong Yan, 2008. "Local Linear Estimation of Spatially Varying Coefficient Models: An Improvement on the Geographically Weighted Regression Technique," Environment and Planning A, , vol. 40(4), pages 986-1005, April.
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