IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v32y2023i1d10.1007_s10260-022-00642-5.html
   My bibliography  Save this article

Heterogeneity in general multinomial choice models

Author

Listed:
  • Ingrid Mauerer

    (University of Málaga)

  • Gerhard Tutz

    (LMU Munich)

Abstract

Different voters behave differently at the polls, different students make different university choices, or different countries choose different health care systems. Many research questions important to social scientists concern choice behavior, which involves dealing with nominal dependent variables. Drawing on the principle of maximum random utility, we propose applying a flexible and general heterogeneous multinomial logit model to study differences in choice behavior. The model systematically accounts for heterogeneity that classical models do not capture, indicates the strength of heterogeneity, and permits examining which explanatory variables cause heterogeneity. As the proposed approach allows incorporating theoretical expectations about heterogeneity into the analysis of nominal dependent variables, it can be applied to a wide range of research problems. Our empirical example uses individual-level survey data to demonstrate the benefits of the model in studying heterogeneity in electoral decisions.

Suggested Citation

  • Ingrid Mauerer & Gerhard Tutz, 2023. "Heterogeneity in general multinomial choice models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 129-148, March.
    • Handle: RePEc:spr:stmapp:v:32:y:2023:i:1:d:10.1007_s10260-022-00642-5
    DOI: 10.1007/s10260-022-00642-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-022-00642-5
    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/s10260-022-00642-5?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. Macdonald, Stuart Elaine & Rabinowitz, George & Listhaug, Ola, 1995. "Political Sophistication and Models of Issue Voting," British Journal of Political Science, Cambridge University Press, vol. 25(4), pages 453-483, October.
    2. Greene, William H. & Hensher, David A. & Rose, John, 2006. "Accounting for heterogeneity in the variance of unobserved effects in mixed logit models," Transportation Research Part B: Methodological, Elsevier, vol. 40(1), pages 75-92, January.
    3. Mauerer, Ingrid & Pößnecker, Wolfgang & Thurner, Paul W. & Tutz, Gerhard, 2015. "Modeling electoral choices in multiparty systems with high-dimensional data: A regularized selection of parameters using the lasso approach," Journal of choice modelling, Elsevier, vol. 16(C), pages 23-42.
    4. 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.
    5. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    7. Alvarez, R. Michael & Nagler, Jonathan, 2004. "Party System Compactness: Measurement and Consequences," Political Analysis, Cambridge University Press, vol. 12(1), pages 46-62, January.
    8. DeShazo, J. R. & Fermo, German, 2002. "Designing Choice Sets for Stated Preference Methods: The Effects of Complexity on Choice Consistency," Journal of Environmental Economics and Management, Elsevier, vol. 44(1), pages 123-143, July.
    9. Byeong U. Park & Enno Mammen & Young K. Lee & Eun Ryung Lee, 2015. "Varying Coefficient Regression Models: A Review and New Developments," International Statistical Review, International Statistical Institute, vol. 83(1), pages 36-64, April.
    10. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    11. Hensher, David & Louviere, Jordan & Swait, Joffre, 1998. "Combining sources of preference data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 197-221, November.
    12. Davis, Otto A. & Hinich, Melvin J. & Ordeshook, Peter C., 1970. "An Expository Development of a Mathematical Model of the Electoral Process," American Political Science Review, Cambridge University Press, vol. 64(2), pages 426-448, June.
    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. 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).
    2. Campbell, Danny & Hutchinson, W. George & Scarpa, Riccardo, 2006. "Lexicographic Preferences in Discrete Choice Experiments: Consequences on Individual-Specific Willingness to Pay Estimates," Sustainability Indicators and Environmental Valuation Working Papers 12224, Fondazione Eni Enrico Mattei (FEEM).
    3. repec:sss:wpaper:201404 is not listed on IDEAS
    4. Stephane Hess, 2014. "Latent class structures: taste heterogeneity and beyond," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 14, pages 311-330, Edward Elgar Publishing.
    5. Mikołaj Czajkowski & Nick Hanley & Jacob LaRiviere, 2016. "Controlling for the Effects of Information in a Public Goods Discrete Choice Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 63(3), pages 523-544, March.
    6. 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).
    7. 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.
    8. 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).
    9. David A. Hensher, 2006. "How do respondents process stated choice experiments? Attribute consideration under varying information load," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 861-878, September.
    10. Hoyos, David, 2010. "The state of the art of environmental valuation with discrete choice experiments," Ecological Economics, Elsevier, vol. 69(8), pages 1595-1603, June.
    11. De Ayala Bilbao, Amaya & Hoyos Ramos, David & Mariel Chladkova, Petr, 2012. "Landscape valuation through discrete choice experiments: Current practice and future research reflections," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).
    12. Alaitz Artabe & Javier Gardeazabal, 2017. "Degree choice evidence from stated preferences," Empirical Economics, Springer, vol. 52(4), pages 1205-1234, June.
    13. Martin, Inès & Vranken, Liesbet & Ugás, Roberto, 2021. "Farmers’ Preferences to Cultivate Threatened Crop Varieties: Evidence from Peru," 2021 Conference, August 17-31, 2021, Virtual 315216, International Association of Agricultural Economists.
    14. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    15. 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.
    16. 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.
    17. Campbell, Danny, 2007. "Combining mixed logit models and random effects models to identify the determinants of willingness to pay for rural landscape improvements," 81st Annual Conference, April 2-4, 2007, Reading University, UK 7975, Agricultural Economics Society.
    18. Mikołaj Czajkowski & Anna Bartczak & Marek Giergiczny & Stale Navrud & Tomasz Żylicz, 2013. "Providing Preference-Based Support for Forest Ecosystem Service Management in Poland," Working Papers 2013-05, Faculty of Economic Sciences, University of Warsaw.
    19. 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.
    20. 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.
    21. Rid, Wolfgang & Haider, Wolfgang & Ryffel, Andrea & Beardmore, Ben, 2018. "Visualisations in Choice Experiments: Comparing 3D Film-sequences and Still-images to Analyse Housing Development Alternatives," Ecological Economics, Elsevier, vol. 146(C), pages 203-217.

    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:spr:stmapp:v:32:y:2023:i:1:d:10.1007_s10260-022-00642-5. 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.