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Comparing the Mixed Logit Estimates and True Parameters under Informative and Uninformative Heterogeneity: A Simulated Discrete Choice Experiment

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  • Maksat Jumamyradov

  • Benjamin M. Craig
  • William H. Greene
  • Murat Munkin

Abstract

In discrete choice experiments (DCEs), differences between respondents’ preferences may be associated with observable or unobservable factors. Unobservable heterogeneity, related to latent factors associated with the choices of individuals, may be modelled using correlated (i.e. informative heterogeneity) or uncorrelated (i.e. uninformative heterogeneity) individual-specific parameters of a logit model. In this study, we simulated unobservable heterogeneity among DCE respondents and compared the results of the maximum simulated likelihood (MSL) estimation of the mixed logit model when correctly specified and mis-specified. These results show that the MSL estimates are biased and can differ greatly from the true parameters, even when correctly specified. Before estimating a mixed logit model, we highly recommend that choice modellers conduct simulation analyses to assess the potential extent of biases before relying on the MSL estimates, particularly their variances and correlations, and then ultimately determine which model specification produces the least bias.

Suggested Citation

  • Maksat Jumamyradov & Benjamin M. Craig & William H. Greene & Murat Munkin, 2025. "Comparing the Mixed Logit Estimates and True Parameters under Informative and Uninformative Heterogeneity: A Simulated Discrete Choice Experiment," Computational Economics, Springer;Society for Computational Economics, vol. 65(6), pages 3295-3324, June.
    • Handle: RePEc:kap:compec:v:65:y:2025:i:6:d:10.1007_s10614-024-10637-x
    DOI: 10.1007/s10614-024-10637-x
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    References listed on IDEAS

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    1. Hossain, Ishrat & Saqib, Najam U. & Haq, Munshi Masudul, 2018. "Scale heterogeneity in discrete choice experiment: An application of generalized mixed logit model in air travel choice," Economics Letters, Elsevier, vol. 172(C), pages 85-88.
    2. Jumamyradov Maksat & Munkin Murat K., 2022. "Biases in Maximum Simulated Likelihood Estimation of Bivariate Models," Journal of Econometric Methods, De Gruyter, vol. 11(1), pages 55-70, January.
    3. Michael Clark & Domino Determann & Stavros Petrou & Domenico Moro & Esther Bekker-Grob, 2014. "Discrete Choice Experiments in Health Economics: A Review of the Literature," PharmacoEconomics, Springer, vol. 32(9), pages 883-902, September.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, Enero-Abr.
    5. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
    6. Juan Marcos Gonzalez, 2019. "A Guide to Measuring and Interpreting Attribute Importance," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 12(3), pages 287-295, June.
    7. Andrew Daly & Stephane Hess & Kenneth Train, 2012. "Assuring finite moments for willingness to pay in random coefficient models," Transportation, Springer, vol. 39(1), pages 19-31, January.
    8. Maksat Jumamyradov & Benjamin M. Craig & Murat Munkin & William Greene, 2023. "Comparing the Conditional Logit Estimates and True Parameters under Preference Heterogeneity: A Simulated Discrete Choice Experiment," Econometrics, MDPI, vol. 11(1), pages 1-13, January.
    9. Vikas Soekhai & Esther W. Bekker-Grob & Alan R. Ellis & Caroline M. Vass, 2019. "Discrete Choice Experiments in Health Economics: Past, Present and Future," PharmacoEconomics, Springer, vol. 37(2), pages 201-226, February.
    10. Lee, Lung-Fei, 1995. "Asymptotic Bias in Simulated Maximum Likelihood Estimation of Discrete Choice Models," Econometric Theory, Cambridge University Press, vol. 11(3), pages 437-483, June.
    11. Benjamin Matthew Craig & Esther W. Bekker-Grob & Juan Marcos González Sepúlveda & William H. Greene, 2022. "A Guide to Observable Differences in Stated Preference Evidence," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 15(3), pages 329-339, May.
    12. Marco A. Palma & Dmitry V. Vedenov & David Bessler, 2020. "The order of variables, simulation noise, and accuracy of mixed logit estimates," Empirical Economics, Springer, vol. 58(5), pages 2049-2083, May.
    13. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    14. 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.
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