IDEAS home Printed from https://ideas.repec.org/p/gbl/wpaper/2020-12.html
   My bibliography  Save this paper

Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness

Author

Listed:
  • Gusarov, N.
  • Talebijmalabad, A.
  • Joly, I.

Abstract

This work is a cross-disciplinary study of econometrics and machine learning (ML) models applied to consumer choice preference modelling. To bridge the interdisciplinary gap, a simulation and theorytesting framework is proposed. It incorporates all essential steps from hypothetical setting generation to the comparison of various performance metrics. The flexibility of the framework in theory-testing and models comparison over economics and statistical indicators is illustrated based on the work of Michaud, Llerena and Joly (2012). Two datasets are generated using the predefined utility functions simulating the presence of homogeneous and heterogeneous individual preferences for alternatives’ attributes. Then, three models issued from econometrics and ML disciplines are estimated and compared. The study demonstrates the proposed methodological approach’s efficiency, successfully capturing the differences between the models issued from different fields given the homogeneous or heterogeneous consumer preferences.

Suggested Citation

  • Gusarov, N. & Talebijmalabad, A. & Joly, I., 2020. "Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness," Working Papers 2020-12, Grenoble Applied Economics Laboratory (GAEL).
    • Handle: RePEc:gbl:wpaper:2020-12
    as

    Download full text from publisher

    File URL: https://gael.univ-grenoble-alpes.fr/sites/gael/files/doc-recherche/WP/A2020/gael2020-12.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Celine Michaud & Daniel Llerena & Iragael Joly, 2013. "Willingness to pay for environmental attributes of non-food agricultural products: a real choice experiment," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 40(2), pages 313-329, March.
    2. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D. With contributions by-Name:Adamowicz,Wiktor, 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304.
    3. McFadden, Daniel, 1974. "The measurement of urban travel demand," Journal of Public Economics, Elsevier, vol. 3(4), pages 303-328, November.
    4. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    5. Scholz, Michael & Dorner, Verena & Franz, Markus & Hinz, Oliver, 2015. "Measuring Consumers' Willingness-to-Pay with Utility-based Recommendation Systems Decision Support Systems," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77134, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    6. Hess, Stephane & Rose, John M. & Hensher, David A., 2008. "Asymmetric preference formation in willingness to pay estimates in discrete choice models," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(5), pages 847-863, September.
    7. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    8. Danaf, Mazen & Atasoy, Bilge & Ben-Akiva, Moshe, 2020. "Logit mixture with inter and intra-consumer heterogeneity and flexible mixing distributions," Journal of choice modelling, Elsevier, vol. 35(C).
    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. Nikita Gusarov & Amirreza Talebijamalabad & Iragaël Joly, 2020. "Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness," Working Papers hal-03019739, HAL.
    2. Celine Michaud & Daniel Llerena & Iragael Joly, 2013. "Willingness to pay for environmental attributes of non-food agricultural products: a real choice experiment," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 40(2), pages 313-329, March.
    3. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    4. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    5. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    6. Lidia Ceriani & Sergio Olivieri & Marco Ranzani, 2023. "Housing, imputed rent, and household welfare," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 21(1), pages 131-168, March.
    7. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    8. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    9. Jens Ludwig & Sendhil Mullainathan, 2021. "Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System," Journal of Economic Perspectives, American Economic Association, vol. 35(4), pages 71-96, Fall.
    10. Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
    11. Asrat, Sinafikeh & Yesuf, Mahmud & Carlsson, Fredrik & Wale, Edilegnaw, 2009. "Farmers’ Preferences for Crop Variety Traits: Lessons for On-Farm Conservation and Technology Adoption," RFF Working Paper Series dp-09-15-efd, Resources for the Future.
    12. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    13. He, Xue-Zhong & Lin, Shen, 2022. "Reinforcement Learning Equilibrium in Limit Order Markets," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    14. Román, Concepción & Espino, Raquel & Martín, Juan Carlos, 2007. "Competition of high-speed train with air transport: The case of Madrid–Barcelona," Journal of Air Transport Management, Elsevier, vol. 13(5), pages 277-284.
    15. Giovanni Di Franco & Michele Santurro, 2021. "Machine learning, artificial neural networks and social research," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(3), pages 1007-1025, June.
    16. Galdo, Virgilio & Li, Yue & Rama, Martin, 2021. "Identifying urban areas by combining human judgment and machine learning: An application to India," Journal of Urban Economics, Elsevier, vol. 125(C).
    17. Woo, JongRoul & Moon, Sungho & Choi, Hyunhong, 2022. "Economic value and acceptability of advanced solar power systems for multi-unit residential buildings: The case of South Korea," Applied Energy, Elsevier, vol. 324(C).
    18. Concepción Román & Raquel Espino & Juan Martín & Ofelia Betancor & Gustavo Nombela, 2008. "Analyzing Mobility in Peripheral Regions of the European Union: The Case of Canarias-Madeira-Azores," Networks and Spatial Economics, Springer, vol. 8(2), pages 141-160, September.
    19. Arthur Blouin & Julian Dyer, 2021. "How Cultures Converge: An Empirical Investigation of Trade and Linguistic Exchange," Working Papers tecipa-691, University of Toronto, Department of Economics.
    20. Mona Aghdaee & Bonny Parkinson & Kompal Sinha & Yuanyuan Gu & Rajan Sharma & Emma Olin & Henry Cutler, 2022. "An examination of machine learning to map non‐preference based patient reported outcome measures to health state utility values," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1525-1557, August.

    More about this item

    Keywords

    DISCRETE CHOICE MODELS; NEURAL NETWORK; PERFORMANCE COMPARISON; HETEREGENEOUS PREFERENCES;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:gbl:wpaper:2020-12. 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: Adrien Hervouet (email available below). General contact details of provider: https://edirc.repec.org/data/inragfr.html .

    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.