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Estimating Consumer Segments and Choices from Limited Information: The Application of Machine Learning Methods

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  • Qin, Fei
  • Wu, Steven Y.

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  • Qin, Fei & Wu, Steven Y., 2022. "Estimating Consumer Segments and Choices from Limited Information: The Application of Machine Learning Methods," 2024 Annual Meeting, July 28-30, New Orleans, LA 322473, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea22:322473
    DOI: 10.22004/ag.econ.322473
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    References listed on IDEAS

    as
    1. Jayson L. Lusk, 2017. "Consumer Research with Big Data: Applications from the Food Demand Survey (FooDS)," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(2), pages 303-320.
    2. repec:cdl:agrebk:qt6gz1t778 is not listed on IDEAS
    3. Timothy F. Bresnahan & Scott Stern & Manuel Trajtenberg, 1995. "Market Segmentation and the Sources of Rents from Innovation: Personal Computers in the Late 1980s," Working Papers 95001, Stanford University, Department of Economics.
    4. Sofia Berto Villas-Boas, 2007. "Vertical Relationships between Manufacturers and Retailers: Inference with Limited Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(2), pages 625-652.
    5. Marianne Baxter, 2017. "Robust Determinants of Bilateral Trade," 2017 Meeting Papers 591, Society for Economic Dynamics.
    6. Harding, Matthew & Lovenheim, Michael, 2017. "The effect of prices on nutrition: Comparing the impact of product- and nutrient-specific taxes," Journal of Health Economics, Elsevier, vol. 53(C), pages 53-71.
    7. Jonathan Hersh & Matthew Harding, 2018. "Big Data in economics," IZA World of Labor, Institute of Labor Economics (IZA), pages 451-451, September.
    8. William R. Dillon & Sunil Gupta, 1996. "A Segment-Level Model of Category Volume and Brand Choice," Marketing Science, INFORMS, vol. 15(1), pages 38-59.
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