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Predicting the distribution of households and employment: a seemingly unrelated regression model with two spatial processes

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  • Zhou, Bin (Brenda)
  • Kockelman, Kara M.

Abstract

Household and employment counts (by type) are key inputs to models of travel demand and air quality. For a variety of reasons, spatial dependence is very likely present in and across these counts. In order to identify the nature of these unobserved relationships, this study provides the first application of a feasible generalized spatial 3SLS estimation procedure for a seemingly unrelated regression (SUR) model with two spatial processes. Statistical tests reveal that this more generalized model is superior to its constrained versions (e.g., SUR models without spatial components or with just a spatial lag or spatial error process).

Suggested Citation

  • Zhou, Bin (Brenda) & Kockelman, Kara M., 2009. "Predicting the distribution of households and employment: a seemingly unrelated regression model with two spatial processes," Journal of Transport Geography, Elsevier, vol. 17(5), pages 369-376.
    • Handle: RePEc:eee:jotrge:v:17:y:2009:i:5:p:369-376
    DOI: 10.1016/j.jtrangeo.2008.09.003
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    Cited by:

    1. Mkondiwa, Maxwell Gibson, 2015. "Whither Broad or Spatially Specific Fertilizer Recommendations?," Master's Theses and Plan B Papers 237344, University of Minnesota, Department of Applied Economics.
    2. Mounisai Siddartha Middela & Gitakrishnan Ramadurai, 2021. "Incorporating spatial interactions in zero-inflated negative binomial models for freight trip generation," Transportation, Springer, vol. 48(5), pages 2335-2356, October.
    3. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    4. Germán M. Izón & Michael S. Hand & Daniel W. Mccollum & Jennifer A. Thacher & Robert P. Berrens, 2016. "Proximity to Natural Amenities: A Seemingly Unrelated Hedonic Regression Model with Spatial Durbin and Spatial Error Processes," Growth and Change, Wiley Blackwell, vol. 47(4), pages 461-480, December.
    5. Hailong Qian & Heather L. Bednarek, 2015. "Partial efficient estimation of SUR models," Economics Bulletin, AccessEcon, vol. 35(1), pages 338-348.
    6. 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.
    7. Wenbo Zhang & Tho V. Le & Satish V. Ukkusuri & Ruimin Li, 2020. "Influencing factors and heterogeneity in ridership of traditional and app-based taxi systems," Transportation, Springer, vol. 47(2), pages 971-996, April.
    8. Laurent Van Malderen & Bart Jourquin & Isabelle Thomas, 2012. "Employers Transport Plans: Do They Change The Commuting Behaviour Of Workers?," ERSA conference papers ersa12p1048, European Regional Science Association.
    9. Chen, Mu-Chen & Wei, Yu, 2011. "Exploring time variants for short-term passenger flow," Journal of Transport Geography, Elsevier, vol. 19(4), pages 488-498.
    10. Magnus Söderberg, 2010. "Informal Benchmarks as a Source of Regulatory Threat in Unregulated Utility Sectors," CESifo Working Paper Series 2973, CESifo.

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