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


  • Xiaokun Wang


  • Kara Kockelman



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

    1. Noland, Robert B., 2001. "Relationships between highway capacity and induced vehicle travel," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(1), pages 47-72, January.
    2. Peter Egger & Michael Pfaffermayr, 2004. "Distance, trade and FDI: a Hausman-Taylor SUR approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(2), pages 227-246.
    3. Magnus, Jan R., 1982. "Multivariate error components analysis of linear and nonlinear regression models by maximum likelihood," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 239-285, August.
    4. Noland, Robert B. & Karlaftis, Matthew G., 2005. "Sensitivity of crash models to alternative specifications," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(5), pages 439-458, September.
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    Cited by:

    1. 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.
    2. Wang, Sicong & Wang, Shifeng, 2016. "Integrating spatial and biomass planning for the United States," Energy, Elsevier, vol. 114(C), pages 113-120.
    3. 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.
    4. 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.
    5. Baltagi, Badi H., 2013. "Panel Data Forecasting," Handbook of Economic Forecasting, Elsevier.
    6. 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.


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