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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," 2022 Annual Meeting, July 31-August 2, Anaheim, California 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

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    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. 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.
    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. Marianne Baxter, 2017. "Robust Determinants of Bilateral Trade," 2017 Meeting Papers 591, Society for Economic Dynamics.
    5. Jonathan Hersh & Matthew Harding, 2018. "Big Data in economics," IZA World of Labor, Institute of Labor Economics (IZA), pages 451-451, September.
    6. 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.
    7. 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.
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