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A new estimation approach to integrate latent psychological constructs in choice modeling

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  • Bhat, Chandra R.
  • Dubey, Subodh K.

Abstract

In the current paper, we propose a new multinomial probit-based model formulation for integrated choice and latent variable (ICLV) models, which, as we show in the paper, has several important advantages relative to the traditional logit kernel-based ICLV formulation. Combining this MNP-based ICLV model formulation with Bhat’s maximum approximate composite marginal likelihood (MACML) inference approach resolves the specification and estimation challenges that are typically encountered with the traditional ICLV formulation estimated using simulation approaches. Our proposed approach can provide very substantial computational time advantages, because the dimensionality of integration in the log-likelihood function is independent of the number of latent variables. Further, our proposed approach easily accommodates ordinal indicators for the latent variables, as well as combinations of ordinal and continuous response indicators. The approach can be extended in a relatively straightforward fashion to also include nominal indicator variables. A simulation exercise in the virtual context of travel mode choice shows that the MACML inference approach is very effective at recovering parameters. The time for convergence is of the order of 30–80min for sample sizes ranging from 500 observations to 2000 observations, in contrast to much longer times for convergence experienced in typical ICLV model estimations.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:transb:v:67:y:2014:i:c:p:68-85
    DOI: 10.1016/j.trb.2014.04.011
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    References listed on IDEAS

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    1. Vredin Johansson, Maria & Heldt, Tobias & Johansson, Per, 2006. "The effects of attitudes and personality traits on mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(6), pages 507-525, July.
    2. Bhat, Chandra R., 1998. "Accommodating variations in responsiveness to level-of-service measures in travel mode choice modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(7), pages 495-507, September.
    3. Bhat, Chandra R. & Sidharthan, Raghuprasad, 2011. "A simulation evaluation of the maximum approximate composite marginal likelihood (MACML) estimator for mixed multinomial probit models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 940-953, August.
    4. Munkin, Murat K. & Trivedi, Pravin K., 2008. "Bayesian analysis of the ordered probit model with endogenous selection," Journal of Econometrics, Elsevier, vol. 143(2), pages 334-348, April.
    5. Bhat, Chandra R., 2001. "Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 677-693, August.
    6. 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.
    7. Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
    8. Andrew Daly & Stephane Hess & Bhanu Patruni & Dimitris Potoglou & Charlene Rohr, 2012. "Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour," Transportation, Springer, vol. 39(2), pages 267-297, March.
    9. D. R. Cox, 2004. "A note on pseudolikelihood constructed from marginal densities," Biometrika, Biometrika Trust, vol. 91(3), pages 729-737, September.
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    Citations

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    Cited by:

    1. Vij, Akshay & Walker, Joan L., 2016. "How, when and why integrated choice and latent variable models are latently useful," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 192-217.
    2. Kamargianni, Maria & Dubey, Subodh & Polydoropoulou, Amalia & Bhat, Chandra, 2015. "Investigating the subjective and objective factors influencing teenagers’ school travel mode choice – An integrated choice and latent variable model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 473-488.
    3. repec:eee:eejocm:v:26:y:2018:i:c:p:80-100 is not listed on IDEAS
    4. Bouscasse, H., 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers 2018-07, Grenoble Applied Economics Laboratory (GAEL).
    5. repec:eee:transb:v:108:y:2018:i:c:p:84-105 is not listed on IDEAS
    6. Bhat, Chandra R., 2015. "A new generalized heterogeneous data model (GHDM) to jointly model mixed types of dependent variables," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 50-77.
    7. Bhat, Chandra R. & Astroza, Sebastian & Bhat, Aarti C. & Nagel, Kai, 2016. "Incorporating a multiple discrete-continuous outcome in the generalized heterogeneous data model: Application to residential self-selection effects analysis in an activity time-use behavior model," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 52-76.
    8. Xuemei Fu & Zhicai Juan, 2017. "Estimation of multinomial probit-kernel integrated choice and latent variable model: comparison on one sequential and two simultaneous approaches," Transportation, Springer, vol. 44(1), pages 91-116, January.
    9. repec:eee:transa:v:114:y:2018:i:pb:p:272-287 is not listed on IDEAS
    10. repec:eee:eejocm:v:29:y:2018:i:c:p:152-168 is not listed on IDEAS
    11. Bhat, Chandra R., 2015. "A comprehensive dwelling unit choice model accommodating psychological constructs within a search strategy for consideration set formation," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 161-188.
    12. repec:eee:eejocm:v:26:y:2018:i:c:p:64-79 is not listed on IDEAS
    13. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    14. Hélène Bouscasse, 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers hal-01795630, HAL.
    15. Bhat, Chandra R. & Dubey, Subodh K. & Nagel, Kai, 2015. "Introducing non-normality of latent psychological constructs in choice modeling with an application to bicyclist route choice," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 341-363.
    16. repec:eee:transa:v:116:y:2018:i:c:p:547-567 is not listed on IDEAS

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