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Specification and estimation of a spatially and temporally autocorrelated seemingly unrelated regression model: application to crash rates in China

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  • Xiaokun Wang
  • Kara Kockelman

Abstract

In transportation studies, variables of interest are often influenced by similar factors and have correlated latent terms (errors). In such cases, a seemingly unrelated regression (SUR) model is normally used. However, most studies ignore the potential temporal and spatial autocorrelations across observations, which may lead to inaccurate conclusions. In contrast, the SUR model proposed in this study also considers these correlations, making the model more behaviorally convincing and applicable to circumstances where a three-dimensional correlation exists, across time, space, and equations. An example of crash rates in Chinese cities is used. The results show that incorporation of spatial and temporal effects significantly improves the model. Moreover, investment in transportation infrastructure is estimated to have statistically significant effects on reducing severe crash rates, but with an elasticity of only −0.078. It is also observed that, while vehicle ownership is associated with higher per capita crash rates, elasticities for severe and non-severe crashes are just 0.13 and 0.18, respectively; much lower than one. The techniques illustrated in this study should contribute to future studies requiring multiple equations in the presence of temporal and spatial effects. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • 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.
    • Handle: RePEc:kap:transp:v:34:y:2007:i:3:p:281-300
    DOI: 10.1007/s11116-007-9117-9
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. Lai, Kee-hung & Wu, Sarah J. & Wong, Christina W.Y., 2013. "Did reverse logistics practices hit the triple bottom line of Chinese manufacturers?," International Journal of Production Economics, Elsevier, vol. 146(1), pages 106-117.
    3. Herrera Gomez Marcos & Fernández Pablo, 2023. "Regresiones SUR Espaciales. Análisis espacio-temporal del empleo sectorial en Argentina," Asociación Argentina de Economía Política: Working Papers 4660, Asociación Argentina de Economía Política.
    4. Eleftheria Kontou & Noreen McDonald, 2021. "Associating ridesourcing with road safety outcomes: Insights from Austin, Texas," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-18, March.
    5. 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.
    6. Fernando López & Jesús Mur & Ana Angulo, 2014. "Spatial model selection strategies in a SUR framework. The case of regional productivity in EU," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 197-220, August.
    7. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 995-1024, Elsevier.
    8. 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.
    9. Nur Oktaviani Widiastuti & Muhammad Zudhy Irawan, 2024. "Ride-Hailing Preferences for First- and Last-Mile Connectivity at Intercity Transit Hubs," Sustainability, MDPI, vol. 16(7), pages 1-20, April.
    10. Mendiola, Lorea & González, Pilar, 2018. "Temporal dynamics in the relationship between land use factors and modal split in commuting: A local case study," Land Use Policy, Elsevier, vol. 77(C), pages 267-278.
    11. Fernando A. López & Román Mínguez & Jesús Mur, 2020. "ML versus IV estimates of spatial SUR models: evidence from the case of Airbnb in Madrid urban area," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 313-347, April.
    12. Wang, Sicong & Wang, Shifeng, 2016. "Integrating spatial and biomass planning for the United States," Energy, Elsevier, vol. 114(C), pages 113-120.
    13. Meysam Effati & Jean-Claude Thill & Shahin Shabani, 2015. "Geospatial and machine learning techniques for wicked social science problems: analysis of crash severity on a regional highway corridor," Journal of Geographical Systems, Springer, vol. 17(2), pages 107-135, April.
    14. Bardaka, Eleni & Delgado, Michael S. & Florax, Raymond J.G.M., 2019. "A spatial multiple treatment/multiple outcome difference-in-differences model with an application to urban rail infrastructure and gentrification," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 325-345.
    15. Sheng, Mingyue & Sharp, Basil, 2019. "Aggregate road passenger travel demand in New Zealand: A seemingly unrelated regression approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 55-68.
    16. Wang, Xiaokun (Cara) & Kockelman, Kara M. & Lemp, Jason D., 2012. "The dynamic spatial multinomial probit model: analysis of land use change using parcel-level data," Journal of Transport Geography, Elsevier, vol. 24(C), pages 77-88.

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