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Random covariance heterogeneity in discrete choice models

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  • Stephane Hess

    ()

  • Denis Bolduc

    ()

  • John Polak

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Abstract

The area of discrete choice modelling has developed rapidly in recent years. In particular, continuing refinements of the Generalised Extreme Value (GEV) model family have permitted the representation of increasingly complex patterns of substitution and parallel advances in estimation capability have led to the increased use of model forms requiring simulation in estimation and application. One model form especially, namely the Mixed Multinomial Logit (MMNL) model, is being used ever more widely. Aside from allowing for random variations in tastes across decision-makers in a Random Coefficients Logit (RCL) framework, this model additionally allows for the representation of inter-alternative correlation as well as heteroscedasticity in an Error Components Logit (ECL) framework, enabling the model to approximate any Random Utility model arbitrarily closely. While the various developments discussed above have led to gradual gains in modelling flexibility, little effort has gone into the development of model forms allowing for a representation of heterogeneity across respondents in the correlation structure in place between alternatives. Such correlation heterogeneity is however possibly a crucial factor in the variation of choice-making behaviour across decision-makers, given the potential presence of individual-specific terms in the unobserved part of utility of multiple alternatives. To the authors' knowledge, there has so far only been one application of a model allowing for such heterogeneity, by Bhat (1997). In this Covariance NL model, the logsum parameters themselves are a function of socio-demographic attributes of the decision-makers, such that the correlation heterogeneity is explained with the help of these attributes. While the results by Bhat show the presence of statistically significant levels of covariance heterogeneity, the improvements in terms of model performance are almost negligible. While it is possible to interpret this as a lack of covariance h
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Stephane Hess & Denis Bolduc & John Polak, 2010. "Random covariance heterogeneity in discrete choice models," Transportation, Springer, vol. 37(3), pages 391-411, May.
  • Handle: RePEc:kap:transp:v:37:y:2010:i:3:p:391-411
    DOI: 10.1007/s11116-009-9255-3
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    References listed on IDEAS

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    Cited by:

    1. Morten Mørkbak & Jonas Nordström, 2009. "The Impact of Information on Consumer Preferences for Different Animal Food Production Methods," Journal of Consumer Policy, Springer, vol. 32(4), pages 313-331, December.
    2. Peter Davis & Pasquale Schiraldi, 2014. "The flexible coefficient multinomial logit (FC-MNL) model of demand for differentiated products," RAND Journal of Economics, RAND Corporation, vol. 45(1), pages 32-63, March.
    3. Eric Gautier & Yuichi Kitamura, 2013. "Nonparametric Estimation in Random Coefficients Binary Choice Models," Econometrica, Econometric Society, vol. 81(2), pages 581-607, March.
    4. Marcucci, Edoardo & Gatta, Valerio, 2012. "Dissecting preference heterogeneity in consumer stated choices," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 331-339.
    5. Habib, Khandker M. Nurul & Sasic, Ana, 2014. "A GEV model with scale heterogeneity for investigating the role of mobility tool ownership in peak period non-work travel mode choices," Journal of choice modelling, Elsevier, vol. 10(C), pages 46-59.
    6. Matzkin, Rosa L., 2012. "Identification in nonparametric limited dependent variable models with simultaneity and unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 166(1), pages 106-115.

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