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A new spatial (social) interaction discrete choice model accommodating for unobserved effects due to endogenous network formation

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  • Chandra Bhat

Abstract

This paper formulates a model that extends the traditional panel discrete choice model to include social/spatial dependencies in the form of dyadic interactions between each pair of decision-makers. In addition, the formulation accommodates spatial correlation effects as well as allows a global spatial structure to be placed on the individual-specific unobserved response sensitivity to exogenous variables. We interpret these latter two effects, sometimes referred to as spatial drift effects, as originating from endogenous group formation. To our knowledge, we are the first to suggest this endogenous group formation interpretation for spatial drift effects in the social/spatial interactions literature. The formulation is motivated in a travel mode choice context, but is applicable in a wide variety of other empirical contexts. Bhat’s (Transp Res B 45(7):923–939, 2011) maximum approximate composite marginal likelihood (MACML) procedure is used for model estimation. A simulation exercise indicates that the MACML approach recovers the model parameters very well, even in the presence of high spatial dependence and endogenous group formation tendency. In addition, the simulation results demonstrate that ignoring spatial dependence and endogenous group formation when both are actually present will lead to bias in parameter estimation. Copyright Springer Science+Business Media New York 2015

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  • Chandra Bhat, 2015. "A new spatial (social) interaction discrete choice model accommodating for unobserved effects due to endogenous network formation," Transportation, Springer, vol. 42(5), pages 879-914, September.
  • Handle: RePEc:kap:transp:v:42:y:2015:i:5:p:879-914
    DOI: 10.1007/s11116-015-9651-9
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    Cited by:

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    2. Patil, Priyadarshan N. & Dubey, Subodh K. & Pinjari, Abdul R. & Cherchi, Elisabetta & Daziano, Ricardo & Bhat, Chandra R., 2017. "Simulation evaluation of emerging estimation techniques for multinomial probit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 9-20.
    3. Mondal, Aupal & Bhat, Chandra R., 2022. "A spatial rank-ordered probit model with an application to travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 374-393.
    4. Vinayak, Pragun & Dias, Felipe F. & Astroza, Sebastian & Bhat, Chandra R. & Pendyala, Ram M. & Garikapati, Venu M., 2018. "Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels," Transport Policy, Elsevier, vol. 72(C), pages 129-137.
    5. Zhang, Guijie & Wei, Fangfang & Jia, Ning & Ma, Shoufeng & Wu, Yi, 2019. "Information adoption in commuters’ route choice in the context of social interactions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 300-316.
    6. Bhat, Chandra R. & Pinjari, Abdul R. & Dubey, Subodh K. & Hamdi, Amin S., 2016. "On accommodating spatial interactions in a Generalized Heterogeneous Data Model (GHDM) of mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 240-263.
    7. Fu, Xiaowen & Jin, Huan & Liu, Shaoxuan & Oum, Tae H. & Yan, Jia, 2019. "Exploring network effects of point-to-point networks: An investigation of the spatial patterns of Southwest Airlines’ network," Transport Policy, Elsevier, vol. 76(C), pages 36-45.
    8. Bhat, Chandra R., 2018. "New matrix-based methods for the analytic evaluation of the multivariate cumulative normal distribution function," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 238-256.
    9. Frank Goetzke & Regine Gerike & Antonio Páez & Elenna Dugundji, 2015. "Social interactions in transportation: analyzing groups and spatial networks," Transportation, Springer, vol. 42(5), pages 723-731, September.

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