IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v65y2025i6d10.1007_s10614-024-10637-x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10637-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-024-10637-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, June.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Swait, Joffre, 2023. "Distribution-free estimation of individual parameter logit (IPL) models using combined evolutionary and optimization algorithms," Journal of choice modelling, Elsevier, vol. 47(C).
    2. Genie, Mesfin G. & Ryan, Mandy & Krucien, Nicolas, 2021. "To pay or not to pay? Cost information processing in the valuation of publicly funded healthcare," Social Science & Medicine, Elsevier, vol. 276(C).
    3. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    4. Kettlewell, Nathan & Walker, Matthew J. & Yoo, Hong Il, 2024. "Alternative Models of Preference Heterogeneity for Elicited Choice Probabilities," IZA Discussion Papers 16821, Institute of Labor Economics (IZA).
    5. Pestana, Joana & Frutuoso, João & Costa, Eduardo & Fonseca, Filipa, 2024. "Heterogeneity in physician's job preferences in a dual practice context – Evidence from a DCE," Social Science & Medicine, Elsevier, vol. 343(C).
    6. Haghani, Milad & Bliemer, Michiel C.J. & Hensher, David A., 2021. "The landscape of econometric discrete choice modelling research," Journal of choice modelling, Elsevier, vol. 40(C).
    7. de Bekker-Grob, E.W. & Donkers, B. & Bliemer, M.C.J. & Veldwijk, J. & Swait, J.D., 2020. "Can healthcare choice be predicted using stated preference data?," Social Science & Medicine, Elsevier, vol. 246(C).
    8. Robert J. Johnston & Tom Ndebele & David A. Newburn, 2023. "Modeling transaction costs in household adoption of landscape conservation practices," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(1), pages 341-367, January.
    9. Kenneth A. Small & Clifford Winston & Jia Yan, 2005. "Differentiated Road Pricing, Express Lanes and Carpools: Exploiting Heterogeneous Preferences in Policy Design," Working Papers 050616, University of California-Irvine, Department of Economics, revised Mar 2006.
    10. 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.
    11. Scaccia, Luisa & Marcucci, Edoardo & Gatta, Valerio, 2023. "Prediction and confidence intervals of willingness-to-pay for mixed logit models," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 54-78.
    12. Don Fullerton & Li Gan & Miwa Hattori, 2015. "A model to evaluate vehicle emission incentive policies in Japan," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 17(1), pages 79-108, January.
    13. Liu, Ruifeng & ,, 2021. "What We Can Learn from the Interactions of Food Traceable Attributes? a Case Study of Fuji Apple in China," 2021 Conference, August 17-31, 2021, Virtual 315916, International Association of Agricultural Economists.
    14. Frick, Bernd & Barros, Carlos Pestana & Prinz, Joachim, 2010. "Analysing head coach dismissals in the German "Bundesliga" with a mixed logit approach," European Journal of Operational Research, Elsevier, vol. 200(1), pages 151-159, January.
    15. Meredith Fowlie, 2010. "Emissions Trading, Electricity Restructuring, and Investment in Pollution Abatement," American Economic Review, American Economic Association, vol. 100(3), pages 837-869, June.
    16. de Ayala, Amaia & Hoyos, David & Mariel, Petr, 2015. "Suitability of discrete choice experiments for landscape management under the European Landscape Convention," Journal of Forest Economics, Elsevier, vol. 21(2), pages 79-96.
    17. Bernard Fortin & Nicolas Jacquemet & Bruce Shearer, 2008. "Policy Analysis in Health-Services Market: Accounting for Quality and Quantity," Annals of Economics and Statistics, GENES, issue 91-92, pages 293-319.
    18. Lan Anh Nguyen & Manh-Hung Nguyen & Viet-Ngu Hoang & Arnaud Reynaud & Michel Simioni & Clevo Wilson, 2024. "Tourists’ preferences and willingness to pay for protecting a World Heritage site from coastal erosion in Vietnam," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(11), pages 27607-27628, November.
    19. Zemo, Kahsay Haile & Termansen, Mette, 2018. "Farmers’ willingness to participate in collective biogas investment: A discrete choice experiment study," Resource and Energy Economics, Elsevier, vol. 52(C), pages 87-101.
    20. Deka, Devajyoti & Carnegie, Jon, 2021. "Predicting transit mode choice of New Jersey workers commuting to New York City from a stated preference survey," Journal of Transport Geography, Elsevier, vol. 91(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:65:y:2025:i:6:d:10.1007_s10614-024-10637-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.