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Asymmetric triangular mixing densities for mixed logit models

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  • Dekker, Thijs

Abstract

A novel method is proposed to estimate random parameter logit models using the asymmetric triangular distribution to describe unobserved preference heterogeneity in the population of interest. The asymmetric triangular mixing density has the potential to overcome behavioural limitations associated with the most frequently applied mixing densities like the normal and log-normal distribution. With only three parameters it remains parsimonious whilst its bounded support can easily be brought in line with behavioural intuitions. The triangular mixing density is not associated with an incredibly large upper (or lower) bound and it can accommodate varying degrees of skewness in unobserved preference heterogeneity. The proposed estimation procedure is based on the principle of mixture densities and circumvents additional simulation chatter arising when applying the inverse cumulative density function method to generate draws from the mixing density.

Suggested Citation

  • Dekker, Thijs, 2016. "Asymmetric triangular mixing densities for mixed logit models," Journal of choice modelling, Elsevier, vol. 21(C), pages 48-55.
    • Handle: RePEc:eee:eejocm:v:21:y:2016:i:c:p:48-55
    DOI: 10.1016/j.jocm.2016.09.006
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    References listed on IDEAS

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    1. Fosgerau, Mogens & Hess, Stephane, 2009. "A comparison of methods for representing random taste heterogeneity in discrete choice models," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 42, pages 1-25.
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    Cited by:

    1. Mouter, Niek & van Cranenburgh, Sander & van Wee, Bert, 2017. "Do individuals have different preferences as consumer and citizen? The trade-off between travel time and safety," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 333-349.
    2. Paz, Alexander & Arteaga, Cristian & Cobos, Carlos, 2019. "Specification of mixed logit models assisted by an optimization framework," Journal of choice modelling, Elsevier, vol. 30(C), pages 50-60.
    3. von Haefen, Roger H. & Domanski, Adam, 2018. "Estimation and welfare analysis from mixed logit models with large choice sets," Journal of Environmental Economics and Management, Elsevier, vol. 90(C), pages 101-118.
    4. Tinessa, Fiore & Marzano, Vittorio & Papola, Andrea, 2020. "Mixing distributions of tastes with a Combination of Nested Logit (CoNL) kernel: Formulation and performance analysis," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 1-23.

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