IDEAS home Printed from https://ideas.repec.org/a/eee/eejocm/v23y2017icp1-8.html
   My bibliography  Save this article

Correlation and scale in mixed logit models

Author

Listed:
  • Hess, Stephane
  • Train, Kenneth

Abstract

This paper examines sources of correlation among utility coefficients in models allowing for random heterogeneity, including correlation that is induced by random scale heterogeneity. We distinguish the capabilities and limitations of various models, including mixed logit, generalized multinomial logit (G-MNL), latent class, and scale-adjusted latent class. We demonstrate that (i) mixed logit allows for all forms of correlation, including scale heterogeneity, (ii) G-MNL is a restricted form of mixed logit that, with an appropriate implementation, can allow for scale heterogeneity but (in its typical form) not other sources of correlation, (iii) none of the models disentangles scale heterogeneity from other sources of correlation, and (iv) models that assume that the only source of correlation is scale heterogeneity necessarily capture, in the estimated scale parameter, whatever other sources of correlation exist.

Suggested Citation

  • Hess, Stephane & Train, Kenneth, 2017. "Correlation and scale in mixed logit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 1-8.
    • Handle: RePEc:eee:eejocm:v:23:y:2017:i:c:p:1-8
    DOI: 10.1016/j.jocm.2017.03.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1755534517300374
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jocm.2017.03.001?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. Joel Huber and Kenneth Train., 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Economics Working Papers E00-289, University of California at Berkeley.
    2. 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.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    4. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    5. Kenneth E. Train, 1998. "Recreation Demand Models with Taste Differences over People," Land Economics, University of Wisconsin Press, vol. 74(2), pages 230-239.
    6. Swait, Joffre & Bernardino, Adriana, 2000. "Distinguishing taste variation from error structure in discrete choice data," Transportation Research Part B: Methodological, Elsevier, vol. 34(1), pages 1-15, January.
    7. Riccardo Scarpa & Mara Thiene & Kenneth Train, 2008. "Utility in Willingness to Pay Space: A Tool to Address Confounding Random Scale Effects in Destination Choice to the Alps," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 994-1010.
    8. Scarpa, R. & Thiene, M. & Train, K., 2008. "Appendix to Utility in WTP space: a tool to address confounding random scale effects in destination choice to the Alps," American Journal of Agricultural Economics APPENDICES, Agricultural and Applied Economics Association, vol. 90(4), pages 1-9, January.
    9. Czajkowski, Mikołaj & Bartczak, Anna & Giergiczny, Marek & Navrud, Stale & Żylicz, Tomasz, 2014. "Providing preference-based support for forest ecosystem service management," Forest Policy and Economics, Elsevier, vol. 39(C), pages 1-12.
    10. Stephane Hess & John Rose, 2012. "Can scale and coefficient heterogeneity be separated in random coefficients models?," Transportation, Springer, vol. 39(6), pages 1225-1239, November.
    11. 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.
    12. William Greene & David Hensher, 2010. "Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models," Transportation, Springer, vol. 37(3), pages 413-428, May.
    13. Michael Keane & Nada Wasi, 2013. "Comparing Alternative Models Of Heterogeneity In Consumer Choice Behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 1018-1045, September.
    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. Mariel, Petr & Meyerhoff, Jürgen, 2018. "A More Flexible Model or Simply More Effort? On the Use of Correlated Random Parameters in Applied Choice Studies," Ecological Economics, Elsevier, vol. 154(C), pages 419-429.
    2. Ajayi, V. & Reiner, D., 2020. "Consumer Willingness to Pay for Reducing the Environmental Footprint of Green Plastics," Cambridge Working Papers in Economics 20110, Faculty of Economics, University of Cambridge.
    3. Kassie, Girma T. & Zeleke, Fresenbet & Birhanu, Mulugeta Y. & Scarpa, Riccardo, 2020. "Reminder Nudge, Attribute Nonattendance, and Willingness to Pay in a Discrete Choice Experiment," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304208, Agricultural and Applied Economics Association.
    4. Akshay Vij & Rico Krueger, 2018. "Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions," Papers 1802.02299, arXiv.org.
    5. Vij, Akshay & Krueger, Rico, 2017. "Random taste heterogeneity in discrete choice models: Flexible nonparametric finite mixture distributions," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 76-101.
    6. Youssef M Aboutaleb & Mazen Danaf & Yifei Xie & Moshe Ben-Akiva, 2020. "Sparse Covariance Estimation in Logit Mixture Models," Papers 2001.05034, arXiv.org.
    7. Robert J. Johnston & Kevin J. Boyle & Wiktor (Vic) Adamowicz & Jeff Bennett & Roy Brouwer & Trudy Ann Cameron & W. Michael Hanemann & Nick Hanley & Mandy Ryan & Riccardo Scarpa & Roger Tourangeau & Ch, 2017. "Contemporary Guidance for Stated Preference Studies," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(2), pages 319-405.
    8. Mariel, Petr & Artabe, Alaitz, 2020. "Interpreting correlated random parameters in choice experiments," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    9. 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.
    10. Arne Risa Hole & Hong Il Yoo, 2017. "The use of heuristic optimization algorithms to facilitate maximum simulated likelihood estimation of random parameter logit models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(5), pages 997-1013, November.
    11. Stephane Hess & John Rose, 2012. "Can scale and coefficient heterogeneity be separated in random coefficients models?," Transportation, Springer, vol. 39(6), pages 1225-1239, November.
    12. Schmid, Basil & Jokubauskaite, Simona & Aschauer, Florian & Peer, Stefanie & Hössinger, Reinhard & Gerike, Regine & Jara-Diaz, Sergio R. & Axhausen, Kay W., 2019. "A pooled RP/SP mode, route and destination choice model to investigate mode and user-type effects in the value of travel time savings," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 262-294.
    13. Kassie, Girma T. & Abdulai, Awudu & Greene, William H. & Shiferaw, Bekele & Abate, Tsedeke & Tarekegne, Amsal & Sutcliffe, Chloe, 2017. "Modeling Preference and Willingness to Pay for Drought Tolerance (DT) in Maize in Rural Zimbabwe," World Development, Elsevier, vol. 94(C), pages 465-477.
    14. Taro Ohdoko & Satoru Komatsu, 2023. "Integrating a Pareto-Distributed Scale into the Mixed Logit Model: A Mathematical Concept," Mathematics, MDPI, vol. 11(23), pages 1-22, November.
    15. Regier, Dean A. & Ryan, Mandy & Phimister, Euan & Marra, Carlo A., 2009. "Bayesian and classical estimation of mixed logit: An application to genetic testing," Journal of Health Economics, Elsevier, vol. 28(3), pages 598-610, May.
    16. Arne Hole & Julie Kolstad, 2012. "Mixed logit estimation of willingness to pay distributions: a comparison of models in preference and WTP space using data from a health-related choice experiment," Empirical Economics, Springer, vol. 42(2), pages 445-469, April.
    17. David Boto‐García & Antonio Alvarez & José Baños, 2021. "Modelling heterogeneous preferences for nature‐based tourism trips," Papers in Regional Science, Wiley Blackwell, vol. 100(6), pages 1625-1653, December.
    18. Carson, Richard T. & Czajkowski, Mikołaj, 2019. "A new baseline model for estimating willingness to pay from discrete choice models," Journal of Environmental Economics and Management, Elsevier, vol. 95(C), pages 57-61.
    19. Ortega, David L. & Waldman, Kurt B. & Richardson, Robert B. & Clay, Daniel C. & Snapp, Sieglinde, 2016. "Sustainable Intensification and Farmer Preferences for Crop System Attributes: Evidence from Malawi’s Central and Southern Regions," World Development, Elsevier, vol. 87(C), pages 139-151.
    20. Gillespie Rob & Kragt Marit E., 2012. "Accounting for Nonmarket Impacts in a Benefit-Cost Analysis of Underground Coal Mining in New South Wales, Australia," Journal of Benefit-Cost Analysis, De Gruyter, vol. 3(2), pages 1-29, May.

    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:eee:eejocm:v:23:y:2017:i:c:p:1-8. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/journal-of-choice-modelling .

    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.