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A mixed spatially correlated logit model: formulation and application to residential choice modeling

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  • Bhat, Chandra R.
  • Guo, Jessica
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    Abstract

    In recent years, there have been important developments in the simulation analysis of the mixed multinomial logit model as well as in the formulation of increasingly flexible closed-form models belonging to the generalized extreme value class. In this paper, we bring these developments together to propose a mixed spatially correlated logit (MSCL) model for location-related choices. The MSCL model represents a powerful approach to capture both random taste variations as well as spatial correlation in location choice analysis. The MSCL model is applied to an analysis of residential location choice using data drawn from the 1996 Dallas-Fort Worth household survey. The empirical results underscore the need to capture unobserved taste variations and spatial correlation, both for improved data fit and the realistic assessment of the effect of sociodemographic, transportation system, and land-use changes on residential location choice.

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    Bibliographic Info

    Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

    Volume (Year): 38 (2004)
    Issue (Month): 2 (February)
    Pages: 147-168

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    Handle: RePEc:eee:transb:v:38:y:2004:i:2:p:147-168

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    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. Koppelman, Frank S. & Wen, Chieh-Hua, 2000. "The paired combinatorial logit model: properties, estimation and application," Transportation Research Part B: Methodological, Elsevier, vol. 34(2), pages 75-89, February.
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    7. Swait, Joffre, 2001. "Choice set generation within the generalized extreme value family of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 643-666, August.
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    Citations

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    Cited by:
    1. Bhat, Chandra R., 2011. "The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 923-939, August.
    2. Smirnov, Oleg A., 2010. "Modeling spatial discrete choice," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 292-298, September.
    3. Sigal Kaplan & Shlomo Bekhor & Yoram Shiftan, 2011. "Development and estimation of a semi-compensatory residential choice model based on explicit choice protocols," The Annals of Regional Science, Springer, vol. 47(1), pages 51-80, August.
    4. Bekhor, Shlomo & Prashker, Joseph N., 2008. "GEV-based destination choice models that account for unobserved similarities among alternatives," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 243-262, March.
    5. Meyerhoff, Jürgen, 2013. "Do turbines in the vicinity of respondents' residences influence choices among programmes for future wind power generation?," Journal of choice modelling, Elsevier, vol. 7(C), pages 58-71.
    6. Sigal Kaplan & Yoram Shiftan & Shlomo Bekhor, 2011. "A Semi-Compensatory Residential Choice Model With Flexible Error Structure," ERSA conference papers ersa10p65, European Regional Science Association.
    7. Vega, Amaya & Reynolds-Feighan, Aisling, 2009. "A methodological framework for the study of residential location and travel-to-work mode choice under central and suburban employment destination patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 401-419, May.
    8. Cao Nguyen & Kazushi Sano & Tu Tran & Tan Doan, 2013. "Firm relocation patterns incorporating spatial interactions," The Annals of Regional Science, Springer, vol. 50(3), pages 685-703, June.
    9. Stephane Hess & Denis Bolduc & John Polak, 2005. "Random Covariance Heterogeneity in Discrete Choice Models," ERSA conference papers ersa05p375, European Regional Science Association.
    10. Páez, Antonio & López, Fernando A. & Ruiz, Manuel & Morency, Catherine, 2013. "Development of an indicator to assess the spatial fit of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 217-233.
    11. Joan L. Walker & Moshe Ben-Akiva & Denis Bolduc, 2007. "Identification of parameters in normal error component logit-mixture (NECLM) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1095-1125.
    12. Kaplan, Sigal & Shiftan, Yoram & Bekhor, Shlomo, 2012. "Development and estimation of a semi-compensatory model with a flexible error structure," Transportation Research Part B: Methodological, Elsevier, vol. 46(2), pages 291-304.
    13. Dalal, Pamela & Chen, Yali & Ravulaparthy, Srinath & Goulias, Konstadinos G., 2011. "Dynamic Opportunity-Based Multipurpose Accessibility Indicators in California," University of California Transportation Center, Working Papers qt2920x3kw, University of California Transportation Center.
    14. Dalal, Pamela & Chen, Yali & Ravulaparthy, Srinath & Goulias, Konstadinos G., 2012. "Dynamic Opportunity-Based Multipurpose Accessibility Indicators in California," University of California Transportation Center, Working Papers qt474714fg, University of California Transportation Center.
    15. Ibeas, Ángel & Cordera, Ruben & dell’Olio, Luigi & Coppola, Pierluigi, 2013. "Modelling the spatial interactions between workplace and residential location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 110-122.
    16. Ignacio A. Inoa & Nathalie Picard & André de Palma, 2013. "Commuting Time and Accessibility in a Joint Residential Location, Workplace, and Job Type Choice Model," THEMA Working Papers 2013-02, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    17. Smirnov, Oleg A. & Egan, Kevin J., 2012. "Spatial random utility model with an application to recreation demand," Economic Modelling, Elsevier, vol. 29(1), pages 72-78.
    18. Bhat, Chandra R. & Guo, Jessica Y., 2007. "A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels," Transportation Research Part B: Methodological, Elsevier, vol. 41(5), pages 506-526, June.
    19. Yao, Jia & Chen, Anthony & Ryu, Seungkyu & Shi, Feng, 2014. "A general unconstrained optimization formulation for the combined distribution and assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 137-160.
    20. Ulimwengu, John M. & Guo, Xiaoqi, 2004. "Modeling Spatial Accessibility Within Discrete Choice Framework," 2004 Annual meeting, August 1-4, Denver, CO 20170, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    21. Joseph DeSalvo & Sisinnio Concas, 2013. "The Effect of Density and Trip-Chaining on the Interaction between Urban Form and Transit Demand," Working Papers 0413, University of South Florida, Department of Economics.
    22. de Grange, Louis & González, Felipe & Muñoz, Juan Carlos & Troncoso, Rodrigo, 2013. "Aggregate estimation of the price elasticity of demand for public transport in integrated fare systems: The case of Transantiago," Transport Policy, Elsevier, vol. 29(C), pages 178-185.
    23. Chandra Bhat & Ipek Sener, 2009. "A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units," Journal of Geographical Systems, Springer, vol. 11(3), pages 243-272, September.

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