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A new generalized heterogeneous data model (GHDM) to jointly model mixed types of dependent variables

Listed author(s):
  • Bhat, Chandra R.
Registered author(s):

    This paper formulates a generalized heterogeneous data model (GHDM) that jointly handles mixed types of dependent variables—including multiple nominal outcomes, multiple ordinal variables, and multiple count variables, as well as multiple continuous variables—by representing the covariance relationships among them through a reduced number of latent factors. Sufficiency conditions for identification of the GHDM parameters are presented. The maximum approximate composite marginal likelihood (MACML) method is proposed to estimate this jointly mixed model system. This estimation method provides computational time advantages since the dimensionality of integration in the likelihood function is independent of the number of latent factors. The study undertakes a simulation experiment within the virtual context of integrating residential location choice and travel behavior to evaluate the ability of the MACML approach to recover parameters. The simulation results show that the MACML approach effectively recovers underlying parameters, and also that ignoring the multi-dimensional nature of the relationship among mixed types of dependent variables can lead not only to inconsistent parameter estimation, but also have important implications for policy analysis.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0191261515001198
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    Article provided by Elsevier in its journal Transportation Research Part B: Methodological.

    Volume (Year): 79 (2015)
    Issue (Month): C ()
    Pages: 50-77

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    Handle: RePEc:eee:transb:v:79:y:2015:i:c:p:50-77
    DOI: 10.1016/j.trb.2015.05.017
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    9. Bhat, Chandra R. & Astroza, Sebastian & Sidharthan, Raghuprasad & Alam, Mohammad Jobair Bin & Khushefati, Waleed H., 2014. "A joint count-continuous model of travel behavior with selection based on a multinomial probit residential density choice model," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 31-51.
    10. Rashidi, Taha Hossein & Auld, Joshua & Mohammadian, Abolfazl (Kouros), 2012. "A behavioral housing search model: Two-stage hazard-based and multinomial logit approach to choice-set formation and location selection," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(7), pages 1097-1107.
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    14. 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.
    15. Cao, Xinyu & Mokhtarian, Patricia & Handy, Susan, 2008. "Examining The Impacts of Residential Self-Selection on Travel Behavior: Methodologies and Empirical Findings," Institute of Transportation Studies, Working Paper Series qt08x1k476, Institute of Transportation Studies, UC Davis.
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    18. Ipek Sener & Naveen Eluru & Chandra Bhat, 2009. "An analysis of bicycle route choice preferences in Texas, US," Transportation, Springer, vol. 36(5), pages 511-539, September.
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    21. Bhat, Chandra R. & Dubey, Subodh K., 2014. "A new estimation approach to integrate latent psychological constructs in choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 68-85.
    22. Clark, William A. V. & Huang, Youqin & Withers, Suzanne, 2003. "Does commuting distance matter?: Commuting tolerance and residential change," Regional Science and Urban Economics, Elsevier, vol. 33(2), pages 199-221, March.
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    24. Brownstone, David & Golob, Thomas F., 2009. "The impact of residential density on vehicle usage and energy consumption," Journal of Urban Economics, Elsevier, vol. 65(1), pages 91-98, January.
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