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Mixed logit with a flexible mixing distribution

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  • Train, Kenneth

Abstract

This paper presents a flexible procedure for representing the distribution of random parameters in mixed logit models. A logit formula is specified for the mixing distribution, in addition to its use for the choice probabilities. The properties of logit assure positivity and provide the normalizing constant for the mixing distribution. Any mixing distribution can be approximated to any degree of accuracy by this specification. The researcher defines variables to describe the shape of the mixing distribution, using flexible forms such as polynomials, splines, and step functions. The gradient of the log-likelihood is easy to calculate, which facilitates estimation. The procedure is illustrated with data on consumers' choice among video streaming services.

Suggested Citation

  • Train, Kenneth, 2016. "Mixed logit with a flexible mixing distribution," Journal of choice modelling, Elsevier, vol. 19(C), pages 40-53.
  • Handle: RePEc:eee:eejocm:v:19:y:2016:i:c:p:40-53
    DOI: 10.1016/j.jocm.2016.07.004
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    References listed on IDEAS

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    3. Heiss, Florian & Hetzenecker, Stephan & Osterhaus, Maximilian, 2019. "Nonparametric estimation of the random coefficients model: An elastic net approach," DICE Discussion Papers 326, University of Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
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    7. Riccardo Scarpa & Cristiano Franceschinis & Mara Thiene, 2017. "A Monte Carlo Evaluation of the Logit-Mixed Logit under Asymmetry and Multimodality," Working Papers in Economics 17/23, University of Waikato.
    8. Rico Krueger & Taha H. Rashidi & Akshay Vij, 2019. "Semi-Parametric Hierarchical Bayes Estimates of New Yorkers' Willingness to Pay for Features of Shared Automated Vehicle Services," Papers 1907.09639, arXiv.org.
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    11. Akshay Vij & Rico Krueger, 2018. "Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions," Papers 1802.02299, arXiv.org.
    12. Czajkowski, Mikołaj & Budziński, Wiktor, 2019. "Simulation error in maximum likelihood estimation of discrete choice models," Journal of choice modelling, Elsevier, vol. 31(C), pages 73-85.
    13. repec:eee:eejocm:v:27:y:2018:i:c:p:97-113 is not listed on IDEAS
    14. Rico Krueger & Akshay Vij & Taha H. Rashidi, 2018. "A Dirichlet Process Mixture Model of Discrete Choice," Papers 1801.06296, arXiv.org.
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    18. Caputo, Vincenzina & Scarpa, Riccardo & Nayga, Rodolfo M. & Ortega, David L., 2018. "Are preferences for food quality attributes really normally distributed? An analysis using flexible mixing distributions," Journal of choice modelling, Elsevier, vol. 28(C), pages 10-27.
    19. repec:eee:eejocm:v:30:y:2019:i:c:p:50-60 is not listed on IDEAS
    20. Heiss, Florian & Hetzenecker, Stephan & Osterhaus, Maximilian, 2019. "Nonparametric estimation of the random coefficients model: An elastic net approach," Ruhr Economic Papers 824, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

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    Keywords

    Mixed logit; Mixing distribution; Nonparametric;

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